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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Linchangco GV, Hui Q, Wilson P, Ho YL, Cho K, Arumäe K, Wittemans LBL, Nellåker C, Vainik U, Sun YV, Holmes C, Lindgren CM, Nicholson G. Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records. Nat Commun 2024; 15:5801. [PMID: 38987242 DOI: 10.1038/s41467-024-49998-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/25/2024] [Indexed: 07/12/2024] Open
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.
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Affiliation(s)
- Samvida S Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Habib Ganjgahi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Gregorio V Linchangco
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Peter Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kadri Arumäe
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Christoffer Nellåker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Uku Vainik
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, University of McGill, Montreal, Canada
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Chris Holmes
- Department of Statistics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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Lee B, Lee YK, Kim SH, Oh H, Won S, Jang SY, Jeon YJ, Yoo BN, Bak JK. Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study. BMC Med Inform Decis Mak 2024; 24:193. [PMID: 38982481 PMCID: PMC11234607 DOI: 10.1186/s12911-024-02586-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 06/24/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Linkage errors that occur according to linkage levels can adversely affect the accuracy and reliability of analysis results. This study aimed to identify the differences in results according to personally identifiable information linkage level, sample size, and analysis methods through empirical analysis. METHODS The difference between the results of linkage in directly identifiable information (DII) and indirectly identifiable information (III) linkage levels was set as III linkage based on name, date of birth, and sex and DII linkage based on resident registration number. The datasets linked at each level were named as databaseIII (DBIII) and databaseDII (DBDII), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed. RESULTS The linkage rates for DBDII and DBIII were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was "none" to "very little." With respect to cervical cancer that had a relatively small sample size, analysis of DBIII resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DBIII vs. 1.80 [95% CI: 1.18-2.73] in DBDII). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DBIII vs. 1.92 [95% CI: 1.70-2.17] in DBDII for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DBIII vs. 2.05 [95% CI: 1.67-2.52] in DBDII for the regional stage). CONCLUSIONS To prevent distortion of the analyses results in health and medical research, it is important to check that the patient population and sample size by each factor of interest (FOI) are sufficient when different data are linked using DBDII. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.
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Affiliation(s)
- Bora Lee
- Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea
| | - Young-Kyun Lee
- Department of Orthopedic Surgery, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Sung Han Kim
- Department of Urology, Urologic Cancer Center, Research Institute and Hospital of National Cancer Center, Goyang-si, Republic of Korea
| | - HyunJin Oh
- Division of Gastroenterology, Department of Internal Medicine, Center for Cancer Prevention and Detection of National Cancer Center, Goyang-si, Republic of Korea
| | - Sungho Won
- Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea
| | - Suk-Yong Jang
- Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul, Republic of Korea
| | - Ye Jin Jeon
- Department of Public Health, Graduate School, Yonsei University, Seoul, Republic of Korea
| | - Bit-Na Yoo
- National Evidence-based Healthcare Collaborating Agency (NECA), 3-5F 400, Neungdong-ro, Gwangin-gu, Seoul, 04933, Republic of Korea
| | - Jean-Kyung Bak
- National Evidence-based Healthcare Collaborating Agency (NECA), 3-5F 400, Neungdong-ro, Gwangin-gu, Seoul, 04933, Republic of Korea.
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Miller M, Jorm L, Partyka C, Burns B, Habig K, Oh C, Immens S, Ballard N, Gallego B. Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia. Injury 2024; 55:111570. [PMID: 38664086 DOI: 10.1016/j.injury.2024.111570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/11/2024] [Accepted: 04/14/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. METHODS We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). CONCLUSION The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.
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Affiliation(s)
- Matthew Miller
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Anesthesia, St George Hospital, Kogarah, NSW 2217 Australia; Centre for Big Data Research in Health at UNSW Sydney, Kensington, NSW 2052, Australia.
| | - Louisa Jorm
- Foundation Director of the Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia
| | - Chris Partyka
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, Royal North Shore Hospital, St Leonards, NSW 2065, Australia
| | - Brian Burns
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Royal North Shore Hospital, St Leonards, NSW 2065, Australia; Faculty of Medicine & Health, University of Sydney, Camperdown, NSW 2050, Australia
| | - Karel Habig
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia
| | - Carissa Oh
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, St George Hospital, Kogarah, NSW 2217 Australia
| | - Sam Immens
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia
| | - Neil Ballard
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Paediatric Emergency Medicine, Sydney Children's Hospital, Randwick, NSW 2031, Australia; Department of Emergency Medicine, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - Blanca Gallego
- Clinical analytics and machine learning unit, Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia
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Alie MS, Negesse Y, Kindie K, Merawi DS. Machine learning algorithms for predicting COVID-19 mortality in Ethiopia. BMC Public Health 2024; 24:1728. [PMID: 38943093 PMCID: PMC11212371 DOI: 10.1186/s12889-024-19196-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 06/19/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia. METHODS Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC). RESULTS The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features. CONCLUSION Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.
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Affiliation(s)
- Melsew Setegn Alie
- Department Public Health, School of Public Health, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia.
| | - Yilkal Negesse
- Department of Public Health, College of Medicine and Health Science, Debre Markos University, Gojjam, Ethiopia
| | - Kassa Kindie
- Department Nursing, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia
| | - Dereje Senay Merawi
- Department of Information Technology, Faculty of Technology, Debre Tabor University, Gonder, Ethiopia
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Wan J, Zeng Y. Prediction of hepatic metastasis in esophageal cancer based on machine learning. Sci Rep 2024; 14:14507. [PMID: 38914571 PMCID: PMC11196737 DOI: 10.1038/s41598-024-63213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 05/27/2024] [Indexed: 06/26/2024] Open
Abstract
This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.
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Affiliation(s)
- Jun Wan
- Department of Emergency surgery, Yangtze University Jingzhou Hospital, jingzhou, China
| | - Yukai Zeng
- Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, No. 126 Xiantai street, Changchun, Jilin, China.
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Eissa T, Leonardo C, Kepesidis KV, Fleischmann F, Linkohr B, Meyer D, Zoka V, Huber M, Voronina L, Richter L, Peters A, Žigman M. Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening. Cell Rep Med 2024:101625. [PMID: 38944038 DOI: 10.1016/j.xcrm.2024.101625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 04/19/2024] [Accepted: 06/07/2024] [Indexed: 07/01/2024]
Abstract
Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.
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Affiliation(s)
- Tarek Eissa
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany.
| | - Cristina Leonardo
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany
| | - Kosmas V Kepesidis
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary
| | - Frank Fleischmann
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany
| | - Birgit Linkohr
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Daniel Meyer
- Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary
| | - Viola Zoka
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary
| | - Marinus Huber
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany
| | - Liudmila Voronina
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany
| | - Lothar Richter
- School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany; School of Public Health, Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer, Ludwig Maximilian University of Munich (LMU), Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany
| | - Mihaela Žigman
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany.
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Teng X, Han K, Jin W, Ma L, Wei L, Min D, Chen L, Du Y. Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism. EClinicalMedicine 2024; 72:102617. [PMID: 38707910 PMCID: PMC11066529 DOI: 10.1016/j.eclinm.2024.102617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Background Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. Methods The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves. Findings Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. Interpretation This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. Funding This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).
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Affiliation(s)
- Xiaoyan Teng
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Kun Han
- Department of Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Wei Jin
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Liru Ma
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Lirong Wei
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Daliu Min
- Department of Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Libo Chen
- Department of Nuclear Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Yuzhen Du
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
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Saari ME, Giosa JL, Holyoke P, Heckman GA, Hirdes JP. Profiling the medical, functional, cognitive, and psychosocial care needs of adults assessed for home care in Ontario, Canada: The case for long-term 'life care' at home. PLoS One 2024; 19:e0300521. [PMID: 38558082 PMCID: PMC10984553 DOI: 10.1371/journal.pone.0300521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024] Open
Abstract
Calls to leverage routinely collected data to inform health system improvements have been made. Misalignment between home care services and client needs can result in poor client, caregiver, and system outcomes. To inform development of an integrated model of community-based home care, grounded in a holistic definition of health, comprehensive clinical profiles were created using Ontario, Canada home care assessment data. Retrospective, cross-sectional analyses of 2017-2018 Resident Assessment Instrument Home Care (RAI-HC) assessments (n = 162,523) were completed to group home care clients by service needs and generate comprehensive profiles of each group's dominant medical, functional, cognitive, and psychosocial care needs. Six unique groups were identified, with care profiles representing home care clients living with Geriatric Syndromes, Medical Complexity, Cognitive Impairment and Behaviours, Caregiver Distress and Social Frailty. Depending on group membership, between 51% and 81% of clients had identified care needs spanning four or more Positive Health dimensions, demonstrating both the heterogeneity and complexity of clients served by home care. Comprehensive clinical profiles, developed from routinely collected assessment data, support a future-focused, evidence-informed, and community-engaged approach to research and practice in integrated home-based health and social care.
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Affiliation(s)
- Margaret E. Saari
- SE Research Centre, SE Health, Markham, Ontario, Canada
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
| | - Justine L. Giosa
- SE Research Centre, SE Health, Markham, Ontario, Canada
- School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Paul Holyoke
- SE Research Centre, SE Health, Markham, Ontario, Canada
| | - George A. Heckman
- School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - John P. Hirdes
- School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
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9
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Jain S, Krumholz HM. Patient Privacy and Data Provenance in Pulmonary and Critical Care Research Using Big Data. Ann Am Thorac Soc 2024; 21:538-540. [PMID: 38259228 PMCID: PMC10995548 DOI: 10.1513/annalsats.202305-497ip] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/22/2024] [Indexed: 01/24/2024] Open
Affiliation(s)
- Snigdha Jain
- Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut; and
| | - Harlan M. Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
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10
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Zimmermann JA, Storp JJ, Dicke C, Leclaire MD, Eter N. [Frequency and distribution of the active agent of intravitreal injections in German centers 2015-2021-An oregis study]. DIE OPHTHALMOLOGIE 2024; 121:196-206. [PMID: 38315190 DOI: 10.1007/s00347-024-01986-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/09/2023] [Accepted: 01/04/2024] [Indexed: 02/07/2024]
Abstract
INTRODUCTION Digitalization in medicine, especially the electronic documentation of patient data, is revolutionizing healthcare systems worldwide. The evaluation of real-world data collected under everyday conditions presents opportunities but also challenges. Electronic medical registries provide a means to compile extensive patient data for scientific inquiries. Oregis is the first nationwide digital registry for health services research established by the German Ophthalmological Society (DOG). Intravitreal operative medicinal injections (IVOM) are among the most frequently performed procedures in ophthalmology. Data on injection numbers and injection frequencies with anti-vascular endothelial growth factor (VEGF) are already available from other countries, whereas data at a national level are not yet available in Germany due to the lack of a nationwide register. It is known that the treatment success of anti-VEGF IVOMs depends largely on the adherence to treatment and thus on the number of injections. There are also differences in cost. In the context of this study, real-world data on the frequency and distribution of intravitreal injections in German centers from 2015 to 2021 were compiled for the first time since the introduction of oregis. The aim of this study is to collect data on the use of anti-VEGF IVOMs in Germany from oregis for the first time and to show the development of injection numbers and anti-VEGF drugs used. At the same time, the possibilities of data retrieval from oregis are demonstrated using a concrete example from daily ophthalmological practice. MATERIAL AND METHODS An automated query of records was performed for all patients who received IVOM at oregis-affiliated healthcare facilities between 2015 and 2021. The number of treated patients and the use of anti-VEGF medications, including aflibercept, bevacizumab, brolucizumab, and ranibizumab, were determined. The data were collected in a pseudonymized and anonymized manner. RESULTS At the time of data collection, 9 German ophthalmological healthcare facilities were affiliated with oregis. In total, 309,152 patients were registered during the observation period, with 8474 receiving IVOMs. Over the observation period, the number of participating centers, patients, and intravitreal injections increased. The proportional share of anti-VEGF agents among the total number of injections varied during the observation period. DISCUSSION Real-world data captured in oregis offer significant potential for enhancing healthcare provision. Oregis enables the depiction of ophthalmological care conditions in Germany and contributes to research and quality assurance. The ability to query the presented data exemplifies the multitude of inquiries through which oregis can contribute to the representation of ophthalmological care in Germany.
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Affiliation(s)
| | - Jens Julian Storp
- Klinik für Augenheilkunde, Universitätsklinikum Münster, Domagkstr. 15, 48149, Münster, Deutschland
| | - Christopher Dicke
- oregis, Projektmanagement, Deutsche Ophthalmologische Gesellschaft, München, Deutschland
| | - Martin Dominik Leclaire
- Klinik für Augenheilkunde, Universitätsklinikum Münster, Domagkstr. 15, 48149, Münster, Deutschland
| | - Nicole Eter
- Klinik für Augenheilkunde, Universitätsklinikum Münster, Domagkstr. 15, 48149, Münster, Deutschland
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11
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Kleinstreuer N, Hartung T. Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine). Arch Toxicol 2024; 98:735-754. [PMID: 38244040 PMCID: PMC10861653 DOI: 10.1007/s00204-023-03666-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024]
Abstract
The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.
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Affiliation(s)
| | - Thomas Hartung
- Bloomberg School of Public Health, Doerenkamp-Zbinden Chair for Evidence-Based Toxicology, Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, MD, USA.
- CAAT-Europe, University of Konstanz, Constance, Germany.
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12
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Lo Barco T, Garcelon N, Neuraz A, Nabbout R. Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome. Epilepsia 2024; 65:350-361. [PMID: 38065926 DOI: 10.1111/epi.17855] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 12/31/2023]
Abstract
OBJECTIVE The increasing implementation of electronic health records allows the use of advanced text-mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years. METHODS We used a document-based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2 years and a follow-up after the age of 3 years ("DS cohort," 56 individuals). We selected two control populations, a "general control cohort" (275 individuals) and a "neurological control cohort" (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome-wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance. RESULTS A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2 years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in-hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non-seizure-related concepts emerged after the age of 2-3 years, referring to the nonseizure comorbidities classically associated with DS. SIGNIFICANCE The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of "longitudinal phenotyping" could be applied to other rare and very rare conditions with poor natural history description.
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Affiliation(s)
- Tommaso Lo Barco
- Department of Pediatric Neurology, Necker-Enfants Malades Hospital, Assistance Publique-Hôpitaux de Paris, Reference Center for Rare Epilepsies, Member of European Reference Network EpiCARE, Université Paris Cité, Paris, France
| | - Nicolas Garcelon
- Data Science Platform, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1163, Imagine Institute, Université Paris Cité, Paris, France
| | - Antoine Neuraz
- Data Science Platform, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1163, Imagine Institute, Université Paris Cité, Paris, France
| | - Rima Nabbout
- Department of Pediatric Neurology, Necker-Enfants Malades Hospital, Assistance Publique-Hôpitaux de Paris, Reference Center for Rare Epilepsies, Member of European Reference Network EpiCARE, Université Paris Cité, Paris, France
- Translational Research for Neurological Disorders, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1163, Imagine Institute, Université Paris Cité, Paris, France
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13
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Luu VP, Fiorini M, Combes S, Quemeneur E, Bonneville M, Bousquet PJ. Challenges of artificial intelligence in precision oncology: public-private partnerships including national health agencies as an asset to make it happen. Ann Oncol 2024; 35:154-158. [PMID: 37769849 DOI: 10.1016/j.annonc.2023.09.3106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/13/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023] Open
Affiliation(s)
- V P Luu
- Epidemiology and innovation Unit, Artificial Intelligence and Cancers Association, Paris, France.
| | - M Fiorini
- Artificial Intelligence and Cancers Association, Paris, France
| | | | - E Quemeneur
- France Biotech, Paris, France; Transgene S.A., Illkirch-Graffenstaden, France
| | - M Bonneville
- Alliance pour la Recherche et l'Innovation des Industries de Santé, Paris, France; Institut Mérieux, Lyon, France
| | - P J Bousquet
- Health Survey, Data-Science, Assessment Division, Institut National du Cancer, Boulogne Billancourt, France; Aix Marseille University, INSERM, IRD, Economics and Social Sciences Applied to Health & Analysis of Medical Information (SESSTIM), Marseille, France
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14
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Levine Z, Kalka I, Kolobkov D, Rossman H, Godneva A, Shilo S, Keshet A, Weissglas-Volkov D, Shor T, Diament A, Talmor-Barkan Y, Aviv Y, Sharon T, Weinberger A, Segal E. Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project. MED 2024; 5:90-101.e4. [PMID: 38157848 DOI: 10.1016/j.medj.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/29/2023] [Accepted: 12/03/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes. METHODS We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging. FINDINGS In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations. CONCLUSIONS The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes. FUNDING E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.
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Affiliation(s)
- Zachary Levine
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Iris Kalka
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Dmitry Kolobkov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Hagai Rossman
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel
| | - Anastasia Godneva
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Smadar Shilo
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ayya Keshet
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel
| | - Daphna Weissglas-Volkov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel
| | - Tal Shor
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel
| | - Alon Diament
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel
| | - Yeela Talmor-Barkan
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel; Department of Cardiology, Rabin Medical Center, Petah-Tikva 49100, Israel
| | - Yaron Aviv
- Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel; Department of Cardiology, Rabin Medical Center, Petah-Tikva 49100, Israel
| | - Tom Sharon
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.
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15
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Pezoulas VC, Kalatzis F, Exarchos TP, Goules A, Tzioufas AG, Fotiadis DI. FHBF: Federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets. PATTERNS (NEW YORK, N.Y.) 2024; 5:100893. [PMID: 38264722 PMCID: PMC10801222 DOI: 10.1016/j.patter.2023.100893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 01/25/2024]
Abstract
Although several studies have deployed gradient boosting trees (GBT) as a robust classifier for federated learning tasks (federated GBT [FGBT]), even with dropout rates (federated gradient boosting trees with dropout rate [FDART]), none of them have investigated the overfitting effects of FGBT across heterogeneous and highly imbalanced datasets within federated environments nor the effect of dropouts in the loss function. In this work, we present the federated hybrid boosted forests (FHBF) algorithm, which incorporates a hybrid weight update approach to overcome ill-posed problems that arise from overfitting effects during the training across highly imbalanced datasets in the cloud. Eight case studies were conducted to stress the performance of FHBF against existing algorithms toward the development of robust AI models for lymphoma development across 18 European federated databases. Our results highlight the robustness of FHBF, yielding an average loss of 0.527 compared with FGBT (0.611) and FDART (0.584) with increased classification performance (0.938 sensitivity, 0.732 specificity).
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Affiliation(s)
- Vasileios C. Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
| | - Fanis Kalatzis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
| | - Themis P. Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Andreas Goules
- Department of Pathophysiology, Faculty of Medicine, National and Kapodistrian University of Athens (NKUA), 15772 Athens, Greece
| | - Athanasios G. Tzioufas
- Department of Pathophysiology, Faculty of Medicine, National and Kapodistrian University of Athens (NKUA), 15772 Athens, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, FORTH, 45110 Ioannina, Greece
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16
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Cohen NM, Lifshitz A, Jaschek R, Rinott E, Balicer R, Shlush LI, Barbash GI, Tanay A. Longitudinal machine learning uncouples healthy aging factors from chronic disease risks. NATURE AGING 2024; 4:129-144. [PMID: 38062254 DOI: 10.1038/s43587-023-00536-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/02/2023] [Indexed: 01/21/2024]
Abstract
To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.
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Affiliation(s)
- Netta Mendelson Cohen
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Aviezer Lifshitz
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Rami Jaschek
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ehud Rinott
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ran Balicer
- Clalit Research Institute, Ramat Gan, Israel
| | - Liran I Shlush
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Gabriel I Barbash
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
| | - Amos Tanay
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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17
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Demuth S, Müller J, Quenardelle V, Lauer V, Gheoca R, Trzeciak M, Pierre-Paul I, De Sèze J, Gourraud PA, Wolff V. Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment. J Neurol 2023; 270:6113-6123. [PMID: 37668701 DOI: 10.1007/s00415-023-11979-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Acute ischemic stroke (AIS) is an immediate emergency whose management is becoming more and more personalized while facing a limited number of neurologists with high expertise. Clinical decision support systems (CDSS) are digital tools leveraging information and artificial intelligence technologies. Here, we present the Strokecopilot project, a CDSS for the management of the acute phase of AIS. It has been designed to support the evidence-based medicine reasoning of neurologists regarding the indications of intravenous thrombolysis (IVT) and endovascular treatments (ET). METHODS Reference populations were manually extracted from the field's main guidelines and randomized clinical trials (RCT). Their characteristics were harmonized in a computerized reference database. We developed a web application whose algorithm identifies the reference populations matching the patient's characteristics. It returns the latter's outcomes in a graphical user interface (GUI), whose design has been driven by real-world practices. RESULTS Strokecopilot has been released at www.digitalneurology.net . The reference database includes 25 reference populations from 2 guidelines and 15 RCTs. After a request, the reference populations matching the patient characteristics are displayed with a summary and a meta-analysis of their results. The status regarding IVT and ET indications are presented as "in guidelines", "in literature", or "outside literature references". The GUI is updated to provide several levels of explanation. Strokecopilot may be updated as the literature evolves by loading a new version of the reference populations' database. CONCLUSION Strokecopilot is a literature-based CDSS, developed to support neurologists in the management of the acute phase of AIS.
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Affiliation(s)
- Stanislas Demuth
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France.
- INSERM U1119 Myelin Biopathology, Neuroprotection, and Therapeutic Strategies, Strasbourg, France.
- INSERM U1064 Center for Research in Transplantation and Translational Immunology, Nantes University, Nantes, France.
| | - Joris Müller
- Public Health Service, University Hospital of Strasbourg, Strasbourg, France
| | | | - Valérie Lauer
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Roxana Gheoca
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Malwina Trzeciak
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
| | | | - Jérôme De Sèze
- INSERM U1119 Myelin Biopathology, Neuroprotection, and Therapeutic Strategies, Strasbourg, France
- Department of Neurology, University Hospital of Strasbourg, Strasbourg, France
- Center of Clinical Investigations, University Hospital of Strasbourg, Strasbourg, France
| | - Pierre-Antoine Gourraud
- INSERM U1064 Center for Research in Transplantation and Translational Immunology, Nantes University, Nantes, France
- Data Clinic, Nantes University Hospital, Nantes, France
| | - Valérie Wolff
- Stroke Unit, University Hospital of Strasbourg, Strasbourg, France
- «Mitochondrie, Stress Oxydant et Protection Musculaire», UR3072, University of Strasbourg, Strasbourg, France
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18
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Bibi I, Schaffert D, Blauth M, Lull C, von Ahnen JA, Gross G, Weigandt WA, Knitza J, Kuhn S, Benecke J, Leipe J, Schmieder A, Olsavszky V. Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study. J Med Internet Res 2023; 25:e50886. [PMID: 38015608 PMCID: PMC10716771 DOI: 10.2196/50886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. OBJECTIVE We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. METHODS After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. RESULTS Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. CONCLUSIONS This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.
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Affiliation(s)
- Igor Bibi
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Daniel Schaffert
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Mara Blauth
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Christian Lull
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Jan Alwin von Ahnen
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Georg Gross
- Department of Medicine V, Division of Rheumatology, University Medical Centre and Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Wanja Alexander Weigandt
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Johannes Knitza
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Sebastian Kuhn
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Johannes Benecke
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Jan Leipe
- Department of Medicine V, Division of Rheumatology, University Medical Centre and Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Astrid Schmieder
- Department of Dermatology, Venereology, and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Victor Olsavszky
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
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Skrehot HC, Bhatnagar A, Weng CY. Epidemiology of Retinopathy of Prematurity in the US From 2003 to 2019-Reply. JAMA Ophthalmol 2023; 141:1090. [PMID: 37856101 DOI: 10.1001/jamaophthalmol.2023.4616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Affiliation(s)
- Henry C Skrehot
- School of Medicine, Baylor College of Medicine, Houston, Texas
| | | | - Christina Y Weng
- Department of Ophthalmology, Cullen Eye Institute, Baylor College of Medicine, Houston, Texas
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20
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Malorgio A, Henckert D, Schweiger G, Braun J, Zacharowski K, Raimann FJ, Piekarski F, Meybohm P, Hottenrott S, Froehlich C, Spahn DR, Noethiger CB, Tscholl DW, Roche TR. Using Visual Patient to Show Vital Sign Predictions, a Computer-Based Mixed Quantitative and Qualitative Simulation Study. Diagnostics (Basel) 2023; 13:3281. [PMID: 37892102 PMCID: PMC10606017 DOI: 10.3390/diagnostics13203281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Machine learning can analyze vast amounts of data and make predictions for events in the future. Our group created machine learning models for vital sign predictions. To transport the information of these predictions without numbers and numerical values and make them easily usable for human caregivers, we aimed to integrate them into the Philips Visual-Patient-avatar, an avatar-based visualization of patient monitoring. METHODS We conducted a computer-based simulation study with 70 participants in 3 European university hospitals. We validated the vital sign prediction visualizations by testing their identification by anesthesiologists and intensivists. Each prediction visualization consisted of a condition (e.g., low blood pressure) and an urgency (a visual indication of the timespan in which the condition is expected to occur). To obtain qualitative user feedback, we also conducted standardized interviews and derived statements that participants later rated in an online survey. RESULTS The mixed logistic regression model showed 77.9% (95% CI 73.2-82.0%) correct identification of prediction visualizations (i.e., condition and urgency both correctly identified) and 93.8% (95% CI 93.7-93.8%) for conditions only (i.e., without considering urgencies). A total of 49 out of 70 participants completed the online survey. The online survey participants agreed that the prediction visualizations were fun to use (32/49, 65.3%), and that they could imagine working with them in the future (30/49, 61.2%). They also agreed that identifying the urgencies was difficult (32/49, 65.3%). CONCLUSIONS This study found that care providers correctly identified >90% of the conditions (i.e., without considering urgencies). The accuracy of identification decreased when considering urgencies in addition to conditions. Therefore, in future development of the technology, we will focus on either only displaying conditions (without urgencies) or improving the visualizations of urgency to enhance usability for human users.
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Affiliation(s)
- Amos Malorgio
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (A.M.); (D.H.); (G.S.); (D.R.S.); (C.B.N.); (D.W.T.)
| | - David Henckert
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (A.M.); (D.H.); (G.S.); (D.R.S.); (C.B.N.); (D.W.T.)
| | - Giovanna Schweiger
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (A.M.); (D.H.); (G.S.); (D.R.S.); (C.B.N.); (D.W.T.)
| | - Julia Braun
- Departments of Epidemiology and Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, 8001 Zurich, Switzerland;
| | - Kai Zacharowski
- Department of Anesthesiology, Intensive Care Medicine, and Pain Therapy, University Hospital Frankfurt, Goethe University Frankfurt, 60323 Frankfurt, Germany; (K.Z.); (F.J.R.); (F.P.)
| | - Florian J. Raimann
- Department of Anesthesiology, Intensive Care Medicine, and Pain Therapy, University Hospital Frankfurt, Goethe University Frankfurt, 60323 Frankfurt, Germany; (K.Z.); (F.J.R.); (F.P.)
| | - Florian Piekarski
- Department of Anesthesiology, Intensive Care Medicine, and Pain Therapy, University Hospital Frankfurt, Goethe University Frankfurt, 60323 Frankfurt, Germany; (K.Z.); (F.J.R.); (F.P.)
| | - Patrick Meybohm
- Department of Anesthesiology, Intensive Care, Emergency, and Pain Medicine, University Hospital Wuerzburg, 97070 Wuerzburg, Germany; (P.M.); (S.H.); (C.F.)
| | - Sebastian Hottenrott
- Department of Anesthesiology, Intensive Care, Emergency, and Pain Medicine, University Hospital Wuerzburg, 97070 Wuerzburg, Germany; (P.M.); (S.H.); (C.F.)
| | - Corinna Froehlich
- Department of Anesthesiology, Intensive Care, Emergency, and Pain Medicine, University Hospital Wuerzburg, 97070 Wuerzburg, Germany; (P.M.); (S.H.); (C.F.)
| | - Donat R. Spahn
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (A.M.); (D.H.); (G.S.); (D.R.S.); (C.B.N.); (D.W.T.)
| | - Christoph B. Noethiger
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (A.M.); (D.H.); (G.S.); (D.R.S.); (C.B.N.); (D.W.T.)
| | - David W. Tscholl
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (A.M.); (D.H.); (G.S.); (D.R.S.); (C.B.N.); (D.W.T.)
| | - Tadzio R. Roche
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (A.M.); (D.H.); (G.S.); (D.R.S.); (C.B.N.); (D.W.T.)
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21
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Li M, Zhang J. A focus on harnessing big data and artificial intelligence: revolutionizing drug discovery from traditional Chinese medicine sources. Chem Sci 2023; 14:10628-10630. [PMID: 37829008 PMCID: PMC10566454 DOI: 10.1039/d3sc90185h] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023] Open
Abstract
The advent of big data-driven artificial intelligence (AI) modeling has profoundly impacted the realm of drug discovery. Chen et al. (Q. Lv et al., Chem. Sci., 2023, https://doi.org/10.1039/D3SC02139D) have paved a way for modern drug discovery from traditional Chinese medicine (TCM) sources through their efforts over the past decade. They achieved this by creating TCMBank, the most extensive systematic central resource for TCM, which integrates standardized TCM-related big data and streamlines the AI-based drug discovery process.
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Affiliation(s)
- Mingyu Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jian Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine Shanghai 200025 China
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22
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Jacobsson M, Seoane F, Abtahi F. The role of compression in large scale data transfer and storage of typical biomedical signals at hospitals. Health Informatics J 2023; 29:14604582231213846. [PMID: 38063181 DOI: 10.1177/14604582231213846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In modern hospitals, monitoring patients' vital signs and other biomedical signals is standard practice. With the advent of data-driven healthcare, Internet of medical things, wearable technologies, and machine learning, we expect this to accelerate and to be used in new and promising ways, including early warning systems and precision diagnostics. Hence, we see an ever-increasing need for retrieving, storing, and managing the large amount of biomedical signal data generated. The popularity of standards, such as HL7 FHIR for interoperability and data transfer, have also resulted in their use as a data storage model, which is inefficient. This article raises concern about the inefficiency of using FHIR for storage of biomedical signals and instead highlights the possibility of a sustainable storage based on data compression. Most reported efforts have focused on ECG signals; however, many other typical biomedical signals are understudied. In this article, we are considering arterial blood pressure, photoplethysmography, and respiration. We focus on simple lossless compression with low implementation complexity, low compression delay, and good compression ratios suitable for wide adoption. Our results show that it is easy to obtain a compression ratio of 2.7:1 for arterial blood pressure, 2.9:1 for photoplethysmography, and 4.1:1 for respiration.
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Affiliation(s)
- Martin Jacobsson
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden
| | - Fernando Seoane
- Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital Huddinge, Sweden; Department of Textile Technology, University of Borås, Sweden; Department of Medical Technology - Management and Development, Karolinska University Hospital, Sweden
| | - Farhad Abtahi
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden; Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Sweden; Department of Clinical Physiology, Karolinska University Hospital Huddinge, Sweden
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23
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Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2023:10.1038/s41587-023-01934-1. [PMID: 37679543 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
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Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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Ruiz-Fresneda MA, Gijón A, Morales-Álvarez P. Bibliometric analysis of the global scientific production on machine learning applied to different cancer types. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:96125-96137. [PMID: 37566331 PMCID: PMC10482761 DOI: 10.1007/s11356-023-28576-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/29/2023] [Indexed: 08/12/2023]
Abstract
Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.
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Affiliation(s)
| | - Alfonso Gijón
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
| | - Pablo Morales-Álvarez
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
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25
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Vega EA, Mellado S, Panettieri E, Newhook T, Vauthey JN. ASO Author Reflections: Harnessing the Power of Data- The Impact of Benchmarking in Healthcare. Ann Surg Oncol 2023; 30:4912-4913. [PMID: 37202572 DOI: 10.1245/s10434-023-13647-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/03/2023] [Indexed: 05/20/2023]
Affiliation(s)
- Eduardo A Vega
- Department of Surgery, Saint Elizabeth's Medical Center, Boston University Medical School, Boston, MA, USA.
| | - Sebastian Mellado
- Department of Surgery, Saint Elizabeth's Medical Center, Boston University Medical School, Boston, MA, USA
| | - Elena Panettieri
- Department of Surgery, Saint Elizabeth's Medical Center, Boston University Medical School, Boston, MA, USA
| | - Timothy Newhook
- Department of Surgery, Saint Elizabeth's Medical Center, Boston University Medical School, Boston, MA, USA
| | - Jean Nicolas Vauthey
- Department of Surgery, Saint Elizabeth's Medical Center, Boston University Medical School, Boston, MA, USA
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26
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Cutshaw G, Uthaman S, Hassan N, Kothadiya S, Wen X, Bardhan R. The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine. Chem Rev 2023; 123:8297-8346. [PMID: 37318957 PMCID: PMC10626597 DOI: 10.1021/acs.chemrev.2c00897] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.
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Affiliation(s)
- Gabriel Cutshaw
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Saji Uthaman
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Nora Hassan
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Siddhant Kothadiya
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Xiaona Wen
- Biologics Analytical Research and Development, Merck & Co., Inc., Rahway, NJ, 07065, USA
| | - Rizia Bardhan
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
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27
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Błajda J, Kucab A, Miazga A, Masłowski M, Kopańska M, Nowak A, Barnaś E. Google Trends Analysis Reflecting Internet Users' Interest in Selected Terms of Sexual and Reproductive Health in Ukraine. Healthcare (Basel) 2023; 11:healthcare11111541. [PMID: 37297681 DOI: 10.3390/healthcare11111541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 06/12/2023] Open
Abstract
INTRODUCTION The war in Ukraine has had adverse impacts on all areas of life, including health-related issues. Limited access to medical care increases the need to look for alternative sources of medical information. AIM To analyze trends of Internet users' interest in sexual and reproductive health in Ukraine based on Google Trends. MATERIALS AND METHODS The retrospective study was based on the analysis of terms related to sexual and reproductive health searched by Ukrainian Internet users. The tool used was Google Trends. The period from 1 January 2021 to 1 January 2023 was analyzed. The time variability in search growths and peaks was assessed from the perspective of two time intervals (before the war and during the war) using the chi-square test. RESULTS Significant changes have been demonstrated in the interests of Internet users from Ukraine regarding selected issues of sexual and reproductive health during the ongoing war. Compared to the pre-war period, a marked increase was observed in active searches for terms such as "condoms" (p = 0.0081), "rape" (p = 0.0008), "syphilis" (p = 0.0136), "ovulation" (p = 0.0002) and "pregnancy test" (p = 0.0008). CONCLUSIONS The conducted analysis clearly indicates an increased need for information regarding sexual and reproductive health among Ukrainian citizens during the ongoing armed conflict. The analysis of trends among Internet users interests can be a valuable source of knowledge for decision makers, including human rights organizations, regarding the scope and coordination of activities aimed at protecting the sexual and reproductive health of the inhabitants of Ukraine.
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Affiliation(s)
- Joanna Błajda
- Institute of Health Sciences, Medical College, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Anna Kucab
- Institute of Health Sciences, Medical College, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Aldona Miazga
- Institute of Health Sciences, Medical College, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Maciej Masłowski
- Institute of Health Sciences, Medical College, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Marta Kopańska
- Institute of Medical Sciences, Medical College, University of Rzeszow, 35-959 Rzeszow, Poland
| | - Anna Nowak
- Center for Foreign Language Studies, University of Rzeszow, Aleja Rejtana 16c, 35-959 Rzeszow, Poland
| | - Edyta Barnaś
- Institute of Health Sciences, Medical College, University of Rzeszow, 35-959 Rzeszow, Poland
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Goetschius LG, Henderson M, Han F, Mahmoudi D, Perman C, Haft H, Stockwell I. Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events. Soc Sci Med 2023; 326:115943. [PMID: 37156187 DOI: 10.1016/j.socscimed.2023.115943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 04/03/2023] [Accepted: 04/30/2023] [Indexed: 05/10/2023]
Abstract
Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries' ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.
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Affiliation(s)
- Leigh G Goetschius
- The Hilltop Institute at the University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA.
| | - Morgan Henderson
- The Hilltop Institute at the University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA; Department of Economics, College of Arts, Humanities, and Social Sciences, UMBC, Baltimore, MD, 21250, USA
| | - Fei Han
- The Hilltop Institute at the University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA; Department of Computer Science and Electrical Engineering, College of Engineering and Information Technology, UMBC, Baltimore, MD, 21250, USA
| | - Dillon Mahmoudi
- Department of Geography and Environmental Systems, College of Arts, Humanities, and Social Sciences, UMBC, Baltimore, MD, USA
| | - Chad Perman
- Program Management Office for the Maryland Primary Care Program, Maryland Department of Health, Baltimore, MD, USA
| | - Howard Haft
- Program Management Office for the Maryland Primary Care Program, Maryland Department of Health, Baltimore, MD, USA
| | - Ian Stockwell
- Department of Information Systems, College of Engineering and Information Technology, UMBC, Baltimore, MD, 21250, USA; Erickson School of Aging Studies, UMBC, Baltimore, MD, 21228, USA
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29
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Sato J, Mitsutake N, Yamada H, Kitsuregawa M, Goda K. Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database. Heliyon 2023; 9:e16209. [PMID: 37234615 PMCID: PMC10205637 DOI: 10.1016/j.heliyon.2023.e16209] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Objective Japan's national-level healthcare insurance claims database (NDB) is a collective database that contains the entire information on healthcare services being provided to all citizens. However, existing anonymized identifiers (ID1 and ID2) have a poor capability of tracing patients' claims in the database, hindering longitudinal analyses. This study presents a virtual patient identifier (vPID), which we have developed on top of these existing identifiers, to improve the patient traceability. Methods vPID is a new composite identifier that intensively consolidates ID1 and ID2 co-occurring in an identical claim to allow to collect claims of each patient even though its ID1 or ID2 may change due to life events or clerical errors. We conducted a verification test with prefecture-level datasets of healthcare insurance claims and enrollee history records, which allowed us to compare vPID with the ground truth, in terms of an identifiability score (indicating a capability of distinguishing a patient's claims from another patient's claims) and a traceability score (indicating a capability of collecting claims of an identical patient). Results The verification test has clarified that vPID offers significantly higher traceability scores (0.994, Mie; 0.997, Gifu) than ID1 (0.863, Mie; 0.884, Gifu) and ID2 (0.602, Mie; 0.839, Gifu), and comparable (0.996, Mie) and lower (0.979, Gifu) identifiability scores. Discussion vPID is seemingly useful for a wide spectrum of analytic studies unless they focus on sensitive cases to the design limitation of vPID, such as patients experiencing marriage and job change, simultaneously, and same-sex twin children. Conclusion vPID successfully improves patient traceability, providing an opportunity for longitudinal analyses that used to be practically impossible for NDB. Further exploration is also necessary, in particular, for mitigating identification errors.
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Affiliation(s)
- Jumpei Sato
- Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan
| | | | - Hiroyuki Yamada
- Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan
| | - Masaru Kitsuregawa
- Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan
| | - Kazuo Goda
- Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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31
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Tay Wee Teck J, Oteo A, Baldacchino A. Rapid opioid overdose response system technologies. Curr Opin Psychiatry 2023:00001504-990000000-00063. [PMID: 37185583 DOI: 10.1097/yco.0000000000000870] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
PURPOSE OF REVIEW Opioid overdose events are a time sensitive medical emergency, which is often reversible with naloxone administration if detected in time. Many countries are facing rising opioid overdose deaths and have been implementing rapid opioid overdose response Systems (ROORS). We describe how technology is increasingly being used in ROORS design, implementation and delivery. RECENT FINDINGS Technology can contribute in significant ways to ROORS design, implementation, and delivery. Artificial intelligence-based modelling and simulations alongside wastewater-based epidemiology can be used to inform policy decisions around naloxone access laws and effective naloxone distribution strategies. Data linkage and machine learning projects can support service delivery organizations to mobilize and distribute community resources in support of ROORS. Digital phenotyping is an advancement in data linkage and machine learning projects, potentially leading to precision overdose responses. At the coalface, opioid overdose detection devices through fixed location or wearable sensors, improved connectivity, smartphone applications and drone-based emergency naloxone delivery all have a role in improving outcomes from opioid overdose. Data driven technologies also have an important role in empowering community responses to opioid overdose. SUMMARY This review highlights the importance of technology applied to every aspect of ROORS. Key areas of development include the need to protect marginalized groups from algorithmic bias, a better understanding of individual overdose trajectories and new reversal agents and improved drug delivery methods.
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Affiliation(s)
- Joseph Tay Wee Teck
- DigitAS Project, Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews
- Forward Leeds and Humankind Charity, Durham, UK
| | - Alberto Oteo
- DigitAS Project, Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews
| | - Alexander Baldacchino
- DigitAS Project, Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews
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32
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Oliveira M, Seringa J, Pinto FJ, Henriques R, Magalhães T. Machine learning prediction of mortality in Acute Myocardial Infarction. BMC Med Inform Decis Mak 2023; 23:70. [PMID: 37072766 PMCID: PMC10111317 DOI: 10.1186/s12911-023-02168-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. METHODS Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. RESULTS Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. CONCLUSIONS Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.
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Affiliation(s)
- Mariana Oliveira
- NOVA National School of Public Health, Universidade NOVA Lisboa, Lisbon, Portugal
| | - Joana Seringa
- NOVA National School of Public Health, Universidade NOVA Lisboa, Lisbon, Portugal
| | - Fausto José Pinto
- Serviço de Cardiologia, Centro Hospitalar Universitário de Lisboa Norte (CHULN), CAML, CCUL, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Roberto Henriques
- NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal.
| | - Teresa Magalhães
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, Nova University of Lisbon, Lisbon, Portugal
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Ivanoska-Dacikj A, Oguz-Gouillart Y, Hossain G, Kaplan M, Sivri Ç, Ros-Lis JV, Mikucioniene D, Munir MU, Kizildag N, Unal S, Safarik I, Akgül E, Yıldırım N, Bedeloğlu AÇ, Ünsal ÖF, Herwig G, Rossi RM, Wick P, Clement P, Sarac AS. Advanced and Smart Textiles during and after the COVID-19 Pandemic: Issues, Challenges, and Innovations. Healthcare (Basel) 2023; 11:healthcare11081115. [PMID: 37107948 PMCID: PMC10137734 DOI: 10.3390/healthcare11081115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/28/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023] Open
Abstract
The COVID-19 pandemic has hugely affected the textile and apparel industry. Besides the negative impact due to supply chain disruptions, drop in demand, liquidity problems, and overstocking, this pandemic was found to be a window of opportunity since it accelerated the ongoing digitalization trends and the use of functional materials in the textile industry. This review paper covers the development of smart and advanced textiles that emerged as a response to the outbreak of SARS-CoV-2. We extensively cover the advancements in developing smart textiles that enable monitoring and sensing through electrospun nanofibers and nanogenerators. Additionally, we focus on improving medical textiles mainly through enhanced antiviral capabilities, which play a crucial role in pandemic prevention, protection, and control. We summarize the challenges that arise from personal protective equipment (PPE) disposal and finally give an overview of new smart textile-based products that emerged in the markets related to the control and spread reduction of SARS-CoV-2.
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Affiliation(s)
- Aleksandra Ivanoska-Dacikj
- Research Centre for Environment and Materials, Macedonian Academy of Sciences and Arts, Krste Misirkov 2, 1000 Skopje, North Macedonia
| | - Yesim Oguz-Gouillart
- Department of Building and Urban Environment, Innovative Textile Material, JUNIA, 59000 Lille, France
| | - Gaffar Hossain
- V-Trion GmbH Textile Research, Millennium Park 15, 6890 Lustenau, Austria
| | - Müslüm Kaplan
- Department of Textile Engineering, Faculty of Engineering, Architecture and Design, Bartin University, Bartin 74110, Turkey
| | - Çağlar Sivri
- Management Engineering Department, Faculty of Engineering and Natural Sciences, Bahcesehir University, İstanbul 34349, Turkey
| | - José Vicente Ros-Lis
- Centro de Reconocimiento Molecular y Desarrollo Tecnologico (IDM), Unidad Mixta Universitat Politecnica de Valencia, Universitat de Valencia, Departamento de Química Inorgánica, Universitat de València, Doctor Moliner 56, 46100 Valencia, Spain
| | - Daiva Mikucioniene
- Faculty of Mechanical Engineering and Design, Kaunas University of Technology, Studentu Str. 56, 50404 Kaunas, Lithuania
| | - Muhammad Usman Munir
- Faculty of Mechanical Engineering and Design, Kaunas University of Technology, Studentu Str. 56, 50404 Kaunas, Lithuania
| | - Nuray Kizildag
- Institute of Nanotechnology, Gebze Technical University, Gebze, Kocaeli 41400, Turkey
- Integrated Manufacturing Technologies Research and Application Center, Sabanci University, Pendik, Istanbul 34906, Turkey
| | - Serkan Unal
- Integrated Manufacturing Technologies Research and Application Center, Sabanci University, Pendik, Istanbul 34906, Turkey
- Faculty of Engineering and Natural Sciences, Material Science and Nanoengineering, Sabanci University, Tuzla, Istanbul 34956, Turkey
| | - Ivo Safarik
- Department of Nanobiotechnology, Biology Centre, ISBB, CAS, Na Sadkach 7, 370 05 Ceske Budejovice, Czech Republic
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic
| | - Esra Akgül
- Department of Industrial Design Engineering, Faculty of Engineering, Erciyes University, Kayseri 38039, Turkey
| | - Nida Yıldırım
- Trabzon Vocational School, Karadeniz Technical University, Trabzon 61080, Turkey
| | - Ayşe Çelik Bedeloğlu
- Department of Polymer Materials Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Bursa 16310, Turkey
| | - Ömer Faruk Ünsal
- Department of Polymer Materials Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Bursa 16310, Turkey
| | - Gordon Herwig
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Biomimetic Membranes and Textiles, 9014 St. Gallen, Switzerland
| | - René M Rossi
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Biomimetic Membranes and Textiles, 9014 St. Gallen, Switzerland
| | - Peter Wick
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Particle-Biology Interactions, 9014 St. Gallen, Switzerland
| | - Pietro Clement
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Particle-Biology Interactions, 9014 St. Gallen, Switzerland
| | - A Sezai Sarac
- Department of Chemistry, Polymer Science and Technology, Faculty of Sciences and Letters, Istanbul Technical University, Istanbul 34469, Turkey
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Shawwa L. The Use of Telemedicine in Medical Education and Patient Care. Cureus 2023; 15:e37766. [PMID: 37213963 PMCID: PMC10198592 DOI: 10.7759/cureus.37766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2023] [Indexed: 05/23/2023] Open
Abstract
The COVID-19 pandemic has accelerated and expanded the adoption of telemedicine globally. This allowed telemedicine to engage medical students in patient care and ensured continuity of care for vulnerable patients. In this review, the history of telemedicine and some of its applications in medical education were reviewed. Furthermore, we also shed light on how to incorporate telemedicine into several curricula and the strategies used to include it. The article also explored how to evaluate telemedicine and the major facilitators and barriers any medical and educational institution must address when using telemedicine. At the end of the review, we explored the future promises telemedicine has for medical education.
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Affiliation(s)
- Lana Shawwa
- Medical Education, King Abdul Aziz University, Jeddah, SAU
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35
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Soh ZD, Cheng CY. Application of big data in ophthalmology. Taiwan J Ophthalmol 2023; 13:123-132. [PMID: 37484625 PMCID: PMC10361443 DOI: 10.4103/tjo.tjo-d-23-00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/02/2023] [Indexed: 07/25/2023] Open
Abstract
The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.
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Affiliation(s)
- Zhi Da Soh
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
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36
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Liu Y, Luo Y, Naidech AM. Big Data in Stroke: How to Use Big Data to Make the Next Management Decision. Neurotherapeutics 2023; 20:744-757. [PMID: 36899137 PMCID: PMC10275829 DOI: 10.1007/s13311-023-01358-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/17/2023] [Indexed: 03/12/2023] Open
Abstract
The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improve patient outcomes after stroke in selected patients; however, significant gaps remain in our ability to select patients, predict complications, and understand outcomes. Big data and the computational methods needed to analyze it can address these gaps. For example, automated analysis of neuroimaging to estimate the volume of brain tissue that is ischemic and salvageable can help triage patients for acute interventions. Data-intensive computational techniques can perform complex risk calculations that are too cumbersome to be completed by humans, resulting in more accurate and timely prediction of which patients require increased vigilance for adverse events such as treatment complications. To handle the accumulation of complex medical data, a variety of advanced computational techniques referred to as machine learning and artificial intelligence now routinely complement traditional statistical inference. In this narrative review, we explore data-intensive techniques in stroke research, how it has informed the management of stroke patients, and how current work could shape clinical practice in the future.
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Affiliation(s)
- Yuzhe Liu
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Yuan Luo
- Section of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Andrew M Naidech
- Section of Neurocritical Care, Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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Yang J, Nittala MR, Velazquez AE, Buddala V, Vijayakumar S. An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series. Cureus 2023; 15:e37889. [PMID: 37113463 PMCID: PMC10129036 DOI: 10.7759/cureus.37889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 04/29/2023] Open
Abstract
Advances in science and technology in the past century and a half have helped improve disease management, prevention, and early diagnosis and better health maintenance. These have led to a longer life expectancy in most developed and middle-income countries. However, resource- and infrastructure-scarce countries and populations have not enjoyed these benefits. Furthermore, in every society, including in developed nations, the lag time from new advances, either in the laboratory or from clinical trials, to using those findings in day-to-day medical practice often takes many years and sometimes close to or longer than a decade. A similar trend is seen in the application of "precision medicine" (PM) in terms of improving population health (PH). One of the reasons for such lack of application of precision medicine in population health is the misunderstanding of equating precision medicine with genomic medicine (GM) as if they are the same. Precision medicine needs to be recognized as encompassing genomic medicine in addition to other new developments such as big data analytics, electronic health records (EHR), telemedicine, and information communication technology. By leveraging these new developments together and applying well-tested epidemiological concepts, it can be posited that population/public health can be improved. In this paper, we take cancer as an example of the benefits of recognizing the potential of precision medicine in applying it to population/public health. Breast cancer and cervical cancer are taken as examples to demonstrate these hypotheses. There exists significant evidence already to show the importance of recognizing "precision population medicine" (PPM) in improving cancer outcomes not only in individual patients but also for its applications in early detection and cancer screening (especially in high-risk populations) and achieving those goals in a more cost-efficient manner that can reach resource- and infrastructure-scarce societies and populations. This is the first report of a series that will focus on individual cancer sites in the future.
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Affiliation(s)
- Johnny Yang
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Mary R Nittala
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | | | - Vedanth Buddala
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
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Grote T, Berens P. Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice. THE JOURNAL OF MEDICINE AND PHILOSOPHY 2023; 48:84-97. [PMID: 36630292 DOI: 10.1093/jmp/jhac034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level need to be overcome. In this paper, we discuss different sources of uncertainty arising for clinicians trying to evaluate the trustworthiness of algorithmic evidence when making diagnostic judgments. Thereby, we examine many of the limitations of current machine learning algorithms (with deep learning in particular) and highlight their relevance for medical diagnostics. Among the problems we inspect are the theoretical foundations of deep learning (which are not yet adequately understood), the opacity of algorithmic decisions, and the vulnerabilities of machine learning models, as well as concerns regarding the quality of medical data used to train the models. Building on this, we discuss different desiderata for an uncertainty amelioration strategy that ensures that the integration of machine learning into clinical settings proves to be medically beneficial in a meaningful way.
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Mijanur Rahman M, Khatun F. Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications during the Pandemic. ARTIF INTELL 2023. [DOI: 10.5772/intechopen.109450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
5G is being implemented in the Internet of things (IoT) era. This book chapter focuses on 5G technology and the integration of other digital technologies, such as artificial intelligence (AI) and machine learning, IoT, big data analytics, cloud computing, robotics, and other digital platforms into new healthcare applications. Now, the healthcare industry is implementing 5G-enabled technology to improve health services, medical research, quality of life, and medical professionals’ and patients’ experiences everywhere, at any time. Technology can facilitate faster medical research progress and better clinical and social services management. Furthermore, AI approaches with 5G connectivity may be able to combat the epidemic challenges with minimal resources. This book chapter underlines how 5G technology is growing to address epidemic concerns. The study highlights many technical issues and future developments for creating 5G-powered healthcare solutions. This chapter also addresses the key challenges AI and 5G technology face in emerging healthcare solutions. In addition, this book chapter highlights perspective, policy recommendations, and future research directions of AI and 5G-enabled technologies in confronting future pandemics. More research will be incorporated into future projects, including studies on developing a digital society based on 5G technology in healthcare emergencies.
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Chen S, Zhu P, Mao L, Wu W, Lin H, Xu D, Lu X, Shi J. Piezocatalytic Medicine: An Emerging Frontier using Piezoelectric Materials for Biomedical Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2208256. [PMID: 36634150 DOI: 10.1002/adma.202208256] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Emerging piezocatalysts have demonstrated their remarkable application potential in diverse medical fields. In addition to their ultrahigh catalytic activities, their inherent and unique charge-carrier-releasing properties can be used to initiate various redox catalytic reactions, displaying bright prospects for future medical applications. Triggered by mechanical energy, piezocatalytic materials can release electrons/holes, catalyze redox reactions of substrates, or intervene in biological processes to promote the production of effector molecules for medical purposes, such as decontamination, sterilization, and therapy. Such a medical application of piezocatalysis is termed as piezocatalytic medicine (PCM) herein. To pioneer novel medical technologies, especially therapeutic modalities, this review provides an overview of the state-of-the-art research progress in piezocatalytic medicine. First, the principle of piezocatalysis and the preparation methodologies of piezoelectric materials are introduced. Then, a comprehensive summary of the medical applications of piezocatalytic materials in tumor treatment, antisepsis, organic degradation, tissue repair and regeneration, and biosensing is provided. Finally, the main challenges and future perspectives in piezocatalytic medicine are discussed and proposed, expecting to fuel the development of this emerging scientific discipline.
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Affiliation(s)
- Si Chen
- Shanghai Tenth People's Hospital, Clinical Center For Brain And Spinal Cord Research, Shanghai Frontiers Science Center of Nanocatalytic Medicine, The Institute for Biomedical Engineering and Nano Science, School of Medicine, Tongji University, Shanghai, 200092, P. R. China
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics Chinese Academy of Sciences Research Unit of Nanocatalytic Medicine in Specific Therapy for Serious Disease, Chinese Academy of Medical Sciences (2021RU012), Shanghai, 200050, P. R. China
| | - Piao Zhu
- Shanghai Tenth People's Hospital, Clinical Center For Brain And Spinal Cord Research, Shanghai Frontiers Science Center of Nanocatalytic Medicine, The Institute for Biomedical Engineering and Nano Science, School of Medicine, Tongji University, Shanghai, 200092, P. R. China
| | - Lijie Mao
- Shanghai Tenth People's Hospital, Clinical Center For Brain And Spinal Cord Research, Shanghai Frontiers Science Center of Nanocatalytic Medicine, The Institute for Biomedical Engineering and Nano Science, School of Medicine, Tongji University, Shanghai, 200092, P. R. China
| | - Wencheng Wu
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics Chinese Academy of Sciences Research Unit of Nanocatalytic Medicine in Specific Therapy for Serious Disease, Chinese Academy of Medical Sciences (2021RU012), Shanghai, 200050, P. R. China
| | - Han Lin
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics Chinese Academy of Sciences Research Unit of Nanocatalytic Medicine in Specific Therapy for Serious Disease, Chinese Academy of Medical Sciences (2021RU012), Shanghai, 200050, P. R. China
| | - Deliang Xu
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics Chinese Academy of Sciences Research Unit of Nanocatalytic Medicine in Specific Therapy for Serious Disease, Chinese Academy of Medical Sciences (2021RU012), Shanghai, 200050, P. R. China
| | - Xiangyu Lu
- Shanghai Tenth People's Hospital, Clinical Center For Brain And Spinal Cord Research, Shanghai Frontiers Science Center of Nanocatalytic Medicine, The Institute for Biomedical Engineering and Nano Science, School of Medicine, Tongji University, Shanghai, 200092, P. R. China
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics Chinese Academy of Sciences Research Unit of Nanocatalytic Medicine in Specific Therapy for Serious Disease, Chinese Academy of Medical Sciences (2021RU012), Shanghai, 200050, P. R. China
| | - Jianlin Shi
- Shanghai Tenth People's Hospital, Clinical Center For Brain And Spinal Cord Research, Shanghai Frontiers Science Center of Nanocatalytic Medicine, The Institute for Biomedical Engineering and Nano Science, School of Medicine, Tongji University, Shanghai, 200092, P. R. China
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics Chinese Academy of Sciences Research Unit of Nanocatalytic Medicine in Specific Therapy for Serious Disease, Chinese Academy of Medical Sciences (2021RU012), Shanghai, 200050, P. R. China
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Wittemans LBL, Nellaker C, Holmes C, Lindgren CM, Nicholson G. The genetic architecture of changes in adiposity during adulthood. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.09.23284364. [PMID: 36711652 PMCID: PMC9882550 DOI: 10.1101/2023.01.09.23284364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.
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Affiliation(s)
- Samvida S. Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | | | - Duncan S. Palmer
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, UK
| | - Laura B. L. Wittemans
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Christoffer Nellaker
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Chris Holmes
- Department of Statistics, University of Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M. Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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Li MP, Liu WC, Sun BL, Zhong NS, Liu ZL, Huang SH, Zhang ZH, Liu JM. Prediction of bone metastasis in non-small cell lung cancer based on machine learning. Front Oncol 2023; 12:1054300. [PMID: 36698411 PMCID: PMC9869148 DOI: 10.3389/fonc.2022.1054300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/21/2022] [Indexed: 01/12/2023] Open
Abstract
Objective The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm. Methods Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models. Results A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC. Conclusion This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.
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Affiliation(s)
- Meng-Pan Li
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,The First Clinical Medical College of Nanchang University, Nanchang, China
| | - Wen-Cai Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,The First Clinical Medical College of Nanchang University, Nanchang, China,Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Bo-Lin Sun
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Nan-Shan Zhong
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Zhi-Li Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Shan-Hu Huang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Zhi-Hong Zhang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China,*Correspondence: Jia-Ming Liu, ; Zhi-Hong Zhang,
| | - Jia-Ming Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China,*Correspondence: Jia-Ming Liu, ; Zhi-Hong Zhang,
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Improving child health through Big Data and data science. Pediatr Res 2023; 93:342-349. [PMID: 35974162 PMCID: PMC9380977 DOI: 10.1038/s41390-022-02264-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/10/2022] [Accepted: 06/28/2022] [Indexed: 12/04/2022]
Abstract
Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.
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Xia J, Luo X, Li J, Zhu L, Wang ZL. Wear-Resisting and Stable 4H-SiC/Cu-Based Tribovoltaic Nanogenerators for Self-Powered Sensing in a Harsh Environment. ACS APPLIED MATERIALS & INTERFACES 2022; 14:55192-55200. [PMID: 36461926 DOI: 10.1021/acsami.2c15781] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Tribovoltaic nanogenerators (TVNGs) are an emerging class of devices for high-entropy energy conversion and mechanical sensing that benefit from their outstanding real-time direct current output characteristics. Here, a self-powered TVNG was fabricated using a small-area 4H-SiC semiconductor wafer and a large-area copper foil. Thus, the cost of materials remains low compared to devices employing large-scale semiconductors. The 4H-SiC/metal-TVNGs (SM-TVNGs) presented here are sensitive to vertical force and sliding velocity, making them appropriate for mechanical sensing. Notably, owing to the modulated bindingtons and surface states, these SM-TVNGs performed well in a harsh environment, namely, in high-temperature and high-humidity conditions. In addition, the SM-TVNGs exhibited an excellent wear-resisting property. On these bases, we designed a self-powered and real-time monitoring device able to estimate the number of staff present in various areas of a deep mining site, a high-temperature and high-humidity environment. This work not only discloses basic physics behind the tribovoltaic effect but also sheds light on possible applications of SM-TVNGs for wear-resisting and stable mechanical sensors in harsh environments.
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Affiliation(s)
- Jinchao Xia
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences (CAS), Beijing101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing100049, People's Republic of China
| | - Xiongxin Luo
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences (CAS), Beijing101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing100049, People's Republic of China
| | - Jiayu Li
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences (CAS), Beijing101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing100049, People's Republic of China
| | - Laipan Zhu
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences (CAS), Beijing101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing100049, People's Republic of China
| | - Zhong Lin Wang
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences (CAS), Beijing101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing100049, People's Republic of China
- School of Material Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia30332, United States
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Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data. Diagnostics (Basel) 2022; 12:diagnostics12123148. [PMID: 36553154 PMCID: PMC9777462 DOI: 10.3390/diagnostics12123148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/24/2022] [Accepted: 10/29/2022] [Indexed: 12/15/2022] Open
Abstract
Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, patient follow-ups, and overall patient care, and should therefore have underlying standardized, evidence-based recommendations. Currently, laboratory parameters and their significance are treated differently depending on expert opinions, clinical environment, and varying hospital guidelines. In our study, we aimed to demonstrate the capability of a set of algorithms to identify predictive analytes for a specific diagnosis. As an illustration of our proposed methodology, we examined the analytes associated with myocardial ischemia; it was a well-researched diagnosis and provides a substrate for comparison. We intend to present a toolset that will boost the evolution of evidence-based laboratory diagnostics and, therefore, improve patient care. Methods: The data we used consisted of preexisting, anonymized recordings from the emergency ward involving all patient cases with a measured value for troponin T. We used multiple imputation technique, orthogonal data augmentation, and Bayesian Model Averaging to create predictive models for myocardial ischemia. Each model incorporated different analytes as cofactors. In examining these models further, we could then conclude the predictive importance of each analyte in question. Results: The used algorithms extracted troponin T as a highly predictive analyte for myocardial ischemia. As this is a known relationship, we saw the predictive importance of troponin T as a proof of concept, suggesting a functioning method. Additionally, we could demonstrate the algorithm's capabilities to extract known risk factors of myocardial ischemia from the data. Conclusion: In this pilot study, we chose an assembly of algorithms to analyze the value of analytes in predicting myocardial ischemia. By providing reliable correlations between the analytes and the diagnosis of myocardial ischemia, we demonstrated the possibilities to create unbiased computational-based guidelines for laboratory diagnostics by using computational power in today's era of digitalization.
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Shi Z, Qian H, Li Y, Wu F, Wu L. Machine learning based regional epidemic transmission risks precaution in digital society. Sci Rep 2022; 12:20499. [PMID: 36443350 PMCID: PMC9705289 DOI: 10.1038/s41598-022-24670-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2022] Open
Abstract
The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.
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Affiliation(s)
- Zhengyu Shi
- School of Data Science, Fudan University, Shanghai, 200433, China
| | - Haoqi Qian
- Institute for Global Public Policy, Fudan University, Shanghai, 200433, China.
- LSE-Fudan Research Centre for Global Public Policy, Fudan University, Shanghai, 200433, China.
- MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, 200433, China.
| | - Yao Li
- Shanghai Ideal Information Industry (Group) Co., Ltd, Fudan University, Shanghai, 200120, China
| | - Fan Wu
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, 200032, China
- Key Laboratory of Medical Molecular Virology, Fudan University, Shanghai, 200032, China
| | - Libo Wu
- MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, 200433, China.
- School of Economics, Fudan University, Shanghai, 200433, China.
- Institute for Big Data, Fudan University, Shanghai, 200433, China.
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Zaghlool SB, Halama A, Stephan N, Gudmundsdottir V, Gudnason V, Jennings LL, Thangam M, Ahlqvist E, Malik RA, Albagha OME, Abou-Samra AB, Suhre K. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population. Nat Commun 2022; 13:7121. [PMID: 36402758 PMCID: PMC9675829 DOI: 10.1038/s41467-022-34754-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/07/2022] [Indexed: 11/20/2022] Open
Abstract
Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.
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Affiliation(s)
- Shaza B Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Nisha Stephan
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Emma Ahlqvist
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | | | - Omar M E Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar.
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Su Y, Wang M, Wang P, Zheng C, Liu Y, Zeng X. Deep learning joint models for extracting entities and relations in biomedical: a survey and comparison. Brief Bioinform 2022; 23:6686739. [PMID: 36125190 DOI: 10.1093/bib/bbac342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/20/2022] [Accepted: 07/25/2022] [Indexed: 12/14/2022] Open
Abstract
The rapid development of biomedicine has produced a large number of biomedical written materials. These unstructured text data create serious challenges for biomedical researchers to find information. Biomedical named entity recognition (BioNER) and biomedical relation extraction (BioRE) are the two most fundamental tasks of biomedical text mining. Accurately and efficiently identifying entities and extracting relations have become very important. Methods that perform two tasks separately are called pipeline models, and they have shortcomings such as insufficient interaction, low extraction quality and easy redundancy. To overcome the above shortcomings, many deep learning-based joint name entity recognition and relation extraction models have been proposed, and they have achieved advanced performance. This paper comprehensively summarize deep learning models for joint name entity recognition and relation extraction for biomedicine. The joint BioNER and BioRE models are discussed in the light of the challenges existing in the BioNER and BioRE tasks. Five joint BioNER and BioRE models and one pipeline model are selected for comparative experiments on four biomedical public datasets, and the experimental results are analyzed. Finally, we discuss the opportunities for future development of deep learning-based joint BioNER and BioRE models.
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Affiliation(s)
- Yansen Su
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone, 230601, Hefei, China
| | - Minglu Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone, 230601, Hefei, China
| | - Pengpeng Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone, 230601, Hefei, China
| | - Chunhou Zheng
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Economic and Technological Development Zone, 230601, Hefei, China
| | - Yuansheng Liu
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
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Medicine and health of 21st Century: Not just a high biotech-driven solution. NPJ Genom Med 2022; 7:67. [DOI: 10.1038/s41525-022-00336-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractMany biotechnological innovations have shaped the contemporary healthcare system (CHS) with significant progress to treat or cure several acute conditions and diseases of known causes (particularly infectious, trauma). Some have been successful while others have created additional health care challenges. For example, a reliance on drugs has not been a panacea to meet the challenges related to multifactorial noncommunicable diseases (NCDs)—the main health burden of the 21st century. In contrast, the advent of omics-based and big data technologies has raised global hope to predict, treat, and/or cure NCDs, effectively fight even the current COVID-19 pandemic, and improve overall healthcare outcomes. Although this digital revolution has introduced extensive changes on all aspects of contemporary society, economy, firms, job market, and healthcare management, it is facing and will face several intrinsic and extrinsic challenges, impacting precision medicine implementation, costs, possible outcomes, and managing expectations. With all of biotechnology’s exciting promises, biological systems’ complexity, unfortunately, continues to be underestimated since it cannot readily be compartmentalized as an independent and segregated set of problems, and therefore is, in a number of situations, not readily mimicable by the current algorithm-building proficiency tools. Although the potential of biotechnology is motivating, we should not lose sight of approaches that may not seem as glamorous but can have large impacts on the healthcare of many and across disparate population groups. A balanced approach of “omics and big data” solution in CHS along with a large scale, simpler, and suitable strategies should be defined with expectations properly managed.
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Bai Y, Du J. Measuring the impact of health research data in terms of data citations by scientific publications. Scientometrics 2022; 127:6881-6893. [PMCID: PMC9661461 DOI: 10.1007/s11192-022-04559-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 10/10/2022] [Indexed: 11/16/2022]
Abstract
Health is a representative domain data-driven research since health research data are growingly generated at a massive scale. There is an intuitive logic that the degree to which disease burden and the number of data resources align. In order to figure out disease-specific data sharing and reuse level, we took the number of data records and their citations in the scientific literature in the Data Citation Index platform as approximate indicators. The results indicated that only a small percentage (7.5%) of health data records had received documented citations by scientific publications. We find the level of data sharing and reuse varies across diseases. Our study suggested that the more socioeconomic burden and the more research funding, the more likely scientific data for diseases will be produced and made available. But such a correlation could not be observed for the activity of data reuse. Secondary reuse of scientific data is a complex behavior.
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Affiliation(s)
- Yongmei Bai
- grid.11135.370000 0001 2256 9319Institute of Medical Technology, Peking University Health Science Center, Peking University, No. 38 Xueyuan Road, Beijing, China ,grid.11135.370000 0001 2256 9319National Institute of Health Data Science, Peking University, No. 38 Xueyuan Road, Beijing, China
| | - Jian Du
- grid.11135.370000 0001 2256 9319National Institute of Health Data Science, Peking University, No. 38 Xueyuan Road, Beijing, China
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