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Harrigian K, Tran D, Tang T, Gonzales A, Nagy P, Kharrazi H, Dredze M, Cai CX. Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods. OPHTHALMOLOGY SCIENCE 2024; 4:100578. [PMID: 39253550 PMCID: PMC11382176 DOI: 10.1016/j.xops.2024.100578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/04/2024] [Accepted: 07/12/2024] [Indexed: 09/11/2024]
Abstract
Purpose To compare the performance of 3 phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions. Design Three phenotyping methods were used to identify clinical conditions including unspecified DR, nonproliferative DR (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative DR, diabetic macular edema (DME), vitreous hemorrhage, retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next 2 methods used a Bidirectional Encoder Representations from Transformers with a dense Multilayer Perceptron output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (Text-Only NLP System) or both free-text and ICD-10 diagnosis codes (Text-and-International Classification of Diseases [ICD] NLP System). Subjects Adults ≥18 years with diabetes mellitus seen at the Wilmer Eye Institute. Methods We compared the performance of the 3 phenotyping methods in identifying the DR related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method. Main Outcome Measures Performance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method. Results A total of 91 097 patients and 692 486 office visits were included in the study. Compared with the gold standard, the Text-and-ICD NLP System had the highest F1 score for most clinical conditions (range 0.39-0.64). The agreement between the ICD-10 Lookup System and Text-Only NLP System varied (kappa of 0.21-0.81). The prevalence of DR and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the Text-and-ICD NLP System). Conclusions The prevalence of DR and related conditions varied significantly depending on the methodology of identifying cases. The best performing phenotyping method was the Text-and-ICD NLP System that used information in both diagnosis codes as well as free-text notes. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Keith Harrigian
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
| | - Diep Tran
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Tina Tang
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Anthony Gonzales
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Paul Nagy
- Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Hadi Kharrazi
- Center for Population Health Information Technology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Mark Dredze
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
| | - Cindy X Cai
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland
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Ortiz BL, Gupta V, Kumar R, Jalin A, Cao X, Ziegenbein C, Singhal A, Tewari M, Choi SW. Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care. JMIR Mhealth Uhealth 2024; 12:e59587. [PMID: 38626290 DOI: 10.2196/59587] [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: 04/16/2024] [Revised: 06/12/2024] [Accepted: 08/27/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. OBJECTIVE This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. METHODS We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. RESULTS The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. CONCLUSIONS While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.
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Affiliation(s)
- Bengie L Ortiz
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Vibhuti Gupta
- School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States
| | - Rajnish Kumar
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Aditya Jalin
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Xiao Cao
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Charles Ziegenbein
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
- Autonomous Systems Research Department, Peraton Labs, Basking Ridge, NJ, United States
| | - Ashutosh Singhal
- School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States
| | - Muneesh Tewari
- Department of Biomedical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, United States
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, United States
- VA Ann Arbor Healthcare System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Sung Won Choi
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, United States
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3
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Lumaca M, Keller PE, Baggio G, Pando-Naude V, Bajada CJ, Martinez MA, Hansen JH, Ravignani A, Joe N, Vuust P, Vulić K, Sandberg K. Frontoparietal network topology as a neural marker of musical perceptual abilities. Nat Commun 2024; 15:8160. [PMID: 39289390 PMCID: PMC11408523 DOI: 10.1038/s41467-024-52479-z] [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: 05/09/2024] [Accepted: 09/05/2024] [Indexed: 09/19/2024] Open
Abstract
Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.
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Affiliation(s)
- M Lumaca
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.
| | - P E Keller
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, Australia
| | - G Baggio
- Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology, Trondheim, Norway
| | - V Pando-Naude
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - C J Bajada
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta / University of Malta Magnetic Resonance Imaging Research Platform, Msida, Malta
| | - M A Martinez
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - J H Hansen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - A Ravignani
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - N Joe
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - P Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - K Vulić
- Department for Human Neuroscience, Institute for Medical Research, University of Belgrade, Belgrade, Serbia
| | - K Sandberg
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
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Kuhlemeier A, Jaki T, Witkiewitz K, Stuart EA, Van Horn ML. Validation of predicted individual treatment effects in out of sample respondents. Stat Med 2024. [PMID: 39075029 DOI: 10.1002/sim.10187] [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: 09/29/2023] [Revised: 07/11/2024] [Accepted: 07/16/2024] [Indexed: 07/31/2024]
Abstract
Personalized medicine promises the ability to improve patient outcomes by tailoring treatment recommendations to the likelihood that any given patient will respond well to a given treatment. It is important that predictions of treatment response be validated and replicated in independent data to support their use in clinical practice. In this paper, we propose and test an approach for validating predictions of individual treatment effects with continuous outcomes across samples that uses matching in a test (validation) sample to match individuals in the treatment and control arms based on their predicted treatment response and their predicted response under control. To examine the proposed validation approach, we conducted simulations where test data is generated from either an identical, similar, or unrelated process to the training data. We also examined the impact of nuisance variables. To demonstrate the use of this validation procedure in the context of predicting individual treatment effects in the treatment of alcohol use disorder, we apply our validation procedure using data from a clinical trial of combined behavioral and pharmacotherapy treatments. We find that the validation algorithm accurately confirms validation and lack of validation, and also provides insights into cases where test data were generated under similar, but not identical conditions. We also show that the presence of nuisance variables detrimentally impacts algorithm performance, which can be partially reduced though the use of variable selection methods. An advantage of the approach is that it can be widely applied to different predictive methods.
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Affiliation(s)
- Alena Kuhlemeier
- Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, New Mexico, USA
| | - Thomas Jaki
- Chair for Computational Statistics, University of Regensburg, Regensburg, Germany
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Katie Witkiewitz
- Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, New Mexico, USA
| | - Elizabeth A Stuart
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - M Lee Van Horn
- Department of Individual, Family, & Community Education, College of Education and Human Sciences, University of New Mexico, Albuquerque, New Mexico, USA
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Liu X, Shi J, Jiao Y, An J, Tian J, Yang Y, Zhuo L. Integrated multi-omics with machine learning to uncover the intricacies of kidney disease. Brief Bioinform 2024; 25:bbae364. [PMID: 39082652 PMCID: PMC11289682 DOI: 10.1093/bib/bbae364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/20/2024] [Accepted: 07/17/2024] [Indexed: 08/03/2024] Open
Abstract
The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.
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Affiliation(s)
| | | | | | | | | | | | - Li Zhuo
- Corresponding author. Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Clinic Medical College, Beijing University of Chinese Medicine, 100029 Beijing, China. E-mail:
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6
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Ieracitano C, Zhang X. Editorial Topical Collection: "Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment". Bioengineering (Basel) 2024; 11:726. [PMID: 39061808 PMCID: PMC11273676 DOI: 10.3390/bioengineering11070726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
The integration of biomedical imaging techniques with advanced data analytics is at the forefront of a transformative era in healthcare [...].
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Affiliation(s)
- Cosimo Ieracitano
- DICEAM Department, University Mediterranea of Reggio Calabria, via Zehender, Feo di Vito, 89122 Reggio Calabria, Italy
| | - Xuejun Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China;
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7
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Avnat E, Samin M, Ben Joya D, Schneider E, Yanko E, Eshel D, Ovadia S, Lev Y, Souroujon D. The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study. J Med Internet Res 2024; 26:e49570. [PMID: 39012659 PMCID: PMC11289572 DOI: 10.2196/49570] [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: 06/17/2023] [Revised: 02/24/2024] [Accepted: 05/27/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Evidence-based clinical intake tools (EBCITs) are structured assessment tools used to gather information about patients and help health care providers make informed decisions. The growing demand for personalized medicine, along with the big data revolution, has rendered EBCITs a promising solution. EBCITs have the potential to provide comprehensive and individualized assessments of symptoms, enabling accurate diagnosis, while contributing to the grounding of medical care. OBJECTIVE This work aims to examine whether EBCITs cover data concerning disorders and symptoms to a similar extent as physicians, and thus can reliably address medical conditions in clinical settings. We also explore the potential of EBCITs to discover and ground the real prevalence of symptoms in different disorders thereby expanding medical knowledge and further supporting medical diagnoses made by physicians. METHODS Between August 1, 2022, and January 15, 2023, patients who used the services of a digital health care (DH) provider in the United States were first assessed by the Kahun EBCIT. Kahun platform gathered and analyzed the information from the sessions. This study estimated the prevalence of patients' symptoms in medical disorders using 2 data sets. The first data set analyzed symptom prevalence, as determined by Kahun's knowledge engine. The second data set analyzed symptom prevalence, relying solely on data from the DH patients gathered by Kahun. The variance difference between these 2 prevalence data sets helped us assess Kahun's ability to incorporate new data while integrating existing knowledge. To analyze the comprehensiveness of Kahun's knowledge engine, we compared how well it covers weighted data for the symptoms and disorders found in the 2019 National Ambulatory Medical Care Survey (NMCAS). To assess Kahun's diagnosis accuracy, physicians independently diagnosed 250 of Kahun-DH's sessions. Their diagnoses were compared with Kahun's diagnoses. RESULTS In this study, 2550 patients used Kahun to complete a full assessment. Kahun proposed 108,523 suggestions related to symptoms during the intake process. At the end of the intake process, 6496 conditions were presented to the caregiver. Kahun covered 94% (526,157,569/562,150,572) of the weighted symptoms and 91% (1,582,637,476/173,4783,244) of the weighted disorders in the 2019 NMCAS. In 90% (224/250) of the sessions, both physicians and Kahun suggested at least one identical disorder, with a 72% (367/507) total accuracy rate. Kahun's engine yielded 519 prevalences while the Kahun-DH cohort yielded 599; 156 prevalences were unique to the latter and 443 prevalences were shared by both data sets. CONCLUSIONS ECBITs, such as Kahun, encompass extensive amounts of knowledge and could serve as a reliable database for inferring medical insights and diagnoses. Using this credible database, the potential prevalence of symptoms in different disorders was discovered or grounded. This highlights the ability of ECBITs to refine the understanding of relationships between disorders and symptoms, which further supports physicians in medical diagnosis.
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Affiliation(s)
- Eden Avnat
- Kahun Medical Ltd, Givatayim, Israel
- Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Michael Samin
- Kahun Medical Ltd, Givatayim, Israel
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Daniel Ben Joya
- Kahun Medical Ltd, Givatayim, Israel
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Eyal Schneider
- Kahun Medical Ltd, Givatayim, Israel
- Department of Otolaryngology-Head and Neck Surgery, Samson Assuta Ashdod University Hospital, Ben Gurion University Faculty of Health Sciences, Ashdod, Israel
| | - Elia Yanko
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | | | | | - Yossi Lev
- Kahun Medical Ltd, Givatayim, Israel
| | - Daniel Souroujon
- Kahun Medical Ltd, Givatayim, Israel
- Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
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8
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Yu H, Zhang Q, Yang LT. An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1083-1092. [PMID: 37018339 DOI: 10.1109/tcbb.2022.3233380] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges on clustering. Specifically, it is hard for traditional approaches to obtain desirable results for healthcare data clustering since they are not able to work for multi-modal data. This paper presents a new high-order multi-modal learning approach using multimodal deep learning and the Tucker decomposition (F- HoFCM). Furthermore, we propose an edge-cloud-aided private scheme to facilitate the clustering efficiency for its embedding in edge resources. Specifically, the computationally intensive tasks, such as parameter updating with high-order back propagation algorithm and clustering through high-order fuzzy c-means, are processed in a centralized location with cloud computing. The other tasks such as multi-modal data fusion and Tucker decomposition are performed at the edge resources. Since the feature fusion and Tucker decomposition are nonlinear operations, the cloud cannot obtain the raw data, thus protecting the privacy. Experimental results state that the presented approach produces significantly more accurate results than the existing high-order fuzzy c-means (HOFCM) on multi-modal healthcare datasets and furthermore the clustering efficiency are significantly improved by the developed edge-cloud-aided private healthcare system.
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9
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Lu B, Chen X, Xavier Castellanos F, Thompson PM, Zuo XN, Zang YF, Yan CG. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration. Sci Bull (Beijing) 2024; 69:1536-1555. [PMID: 38519398 DOI: 10.1016/j.scib.2024.03.006] [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: 08/17/2023] [Revised: 12/12/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg 10962, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China
| | - Yu-Feng Zang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310004, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 310030, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou 311121, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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Di Sarno L, Caroselli A, Tonin G, Graglia B, Pansini V, Causio FA, Gatto A, Chiaretti A. Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives. Biomedicines 2024; 12:1220. [PMID: 38927427 PMCID: PMC11200597 DOI: 10.3390/biomedicines12061220] [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: 04/23/2024] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.
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Affiliation(s)
- Lorenzo Di Sarno
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
| | - Anya Caroselli
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Giovanna Tonin
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Benedetta Graglia
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Valeria Pansini
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Francesco Andrea Causio
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Section of Hygiene and Public Health, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Antonio Gatto
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Antonio Chiaretti
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
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11
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Savchenko E, Bunimovich-Mendrazitsky S. Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer. Front Med (Lausanne) 2024; 11:1388685. [PMID: 38808135 PMCID: PMC11130437 DOI: 10.3389/fmed.2024.1388685] [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: 02/20/2024] [Accepted: 04/26/2024] [Indexed: 05/30/2024] Open
Abstract
In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.
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12
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Yan JK. A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients. PeerJ Comput Sci 2024; 10:e2017. [PMID: 38855224 PMCID: PMC11157615 DOI: 10.7717/peerj-cs.2017] [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: 11/22/2023] [Accepted: 04/03/2024] [Indexed: 06/11/2024]
Abstract
The scarcity of data is likely to have a negative effect on machine learning (ML). Yet, in the health sciences, data is diverse and can be costly to acquire. Therefore, it is critical to develop methods that can reach similar accuracy with minimal clinical features. This study explores a methodology that aims to build a model using minimal clinical parameters to reach comparable performance to a model trained with a more extensive list of parameters. To develop this methodology, a dataset of over 1,000 COVID-19-positive patients was used. A machine learning model was built with over 90% accuracy when combining 24 clinical parameters using Random Forest (RF) and logistic regression. Furthermore, to obtain minimal clinical parameters to predict the mortality of COVID-19 patients, the features were weighted using both Shapley values and RF feature importance to get the most important factors. The six most highly weighted features that could produce the highest performance metrics were combined for the final model. The accuracy of the final model, which used a combination of six features, is 90% with the random forest classifier and 91% with the logistic regression model. This performance is close to that of a model using 24 combined features (92%), suggesting that highly weighted minimal clinical parameters can be used to reach similar performance. The six clinical parameters identified here are acute kidney injury, glucose level, age, troponin, oxygen level, and acute hepatic injury. Among those parameters, acute kidney injury was the highest-weighted feature. Together, a methodology was developed using significantly minimal clinical parameters to reach performance metrics similar to a model trained with a large dataset, highlighting a novel approach to address the problems of clinical data collection for machine learning.
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13
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Jentsch M, Schneider-Lunitz V, Taron U, Braun M, Ishaque N, Wagener H, Conrad C, Twardziok S. Creating cloud platforms for supporting FAIR data management in biomedical research projects. F1000Res 2024; 13:8. [PMID: 38779317 PMCID: PMC11109697 DOI: 10.12688/f1000research.140624.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/25/2024] [Indexed: 05/25/2024] Open
Abstract
Biomedical research projects are becoming increasingly complex and require technological solutions that support all phases of the data lifecycle and application of the FAIR principles. At the Berlin Institute of Health (BIH), we have developed and established a flexible and cost-effective approach to building customized cloud platforms for supporting research projects. The approach is based on a microservice architecture and on the management of a portfolio of supported services. On this basis, we created and maintained cloud platforms for several international research projects. In this article, we present our approach and argue that building customized cloud platforms can offer multiple advantages over using multi-project platforms. Our approach is transferable to other research environments and can be easily adapted by other projects and other service providers.
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Affiliation(s)
- Marcel Jentsch
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
| | - Valentin Schneider-Lunitz
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
| | - Ulrike Taron
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
| | - Martin Braun
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
| | - Naveed Ishaque
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
| | - Harald Wagener
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
| | - Christian Conrad
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
| | - Sven Twardziok
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital Health, Berlin, 10117, Germany
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14
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Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, Lewis JM, Gabr AE, Smiley A, Tiwari RK, Etienne M. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel) 2024; 14:557. [PMID: 38792579 PMCID: PMC11122160 DOI: 10.3390/life14050557] [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: 03/11/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.
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Affiliation(s)
- Rahim Hirani
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Kaleb Noruzi
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Hassan Khuram
- College of Medicine, Drexel University, Philadelphia, PA 19129, USA
| | - Anum S. Hussaini
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Esewi Iyobosa Aifuwa
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Kencie E. Ely
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89106, USA
| | - Joshua M. Lewis
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Ahmed E. Gabr
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Abbas Smiley
- School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
| | - Raj K. Tiwari
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Mill Etienne
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Department of Neurology, New York Medical College, Valhalla, NY 10595, USA
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15
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Badr Y, Abdul Kader L, Shamayleh A. The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment. J Pers Med 2024; 14:383. [PMID: 38673011 PMCID: PMC11051308 DOI: 10.3390/jpm14040383] [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: 01/28/2024] [Revised: 03/18/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.
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Affiliation(s)
- Yara Badr
- Department of Biomedical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (Y.B.); (L.A.K.)
| | - Lamis Abdul Kader
- Department of Biomedical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (Y.B.); (L.A.K.)
| | - Abdulrahim Shamayleh
- Department of Industrial Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
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16
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Gallardo-Pizarro A, Peyrony O, Chumbita M, Monzo-Gallo P, Aiello TF, Teijon-Lumbreras C, Gras E, Mensa J, Soriano A, Garcia-Vidal C. Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning. Expert Rev Anti Infect Ther 2024; 22:179-187. [PMID: 38457198 DOI: 10.1080/14787210.2024.2322445] [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: 07/26/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine. AREAS COVERED In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality. EXPERT OPINION There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.
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Affiliation(s)
| | - Olivier Peyrony
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Mariana Chumbita
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | | | | | | | - Emmanuelle Gras
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Josep Mensa
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Alex Soriano
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
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17
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Wojcik KM, Kamil D, Zhang J, Wilson OWA, Smith L, Butera G, Isaacs C, Kurian A, Jayasekera J. A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care. J Cancer Surviv 2024:10.1007/s11764-024-01567-6. [PMID: 38538922 PMCID: PMC11436482 DOI: 10.1007/s11764-024-01567-6] [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: 08/29/2023] [Accepted: 03/12/2024] [Indexed: 09/29/2024]
Abstract
PURPOSE We reviewed existing personalized, web-based, interactive decision-making tools available to guide breast cancer treatment and survivorship care decisions in clinical settings. METHODS The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched PubMed and related databases for interactive web-based decision-making tools developed to support breast cancer treatment and survivorship care from 2013 to 2023. Information on each tool's purpose, target population, data sources, individual and contextual characteristics, outcomes, validation, and usability testing were extracted. We completed a quality assessment for each tool using the International Patient Decision Aid Standard (IPDAS) instrument. RESULTS We found 54 tools providing personalized breast cancer outcomes (e.g., recurrence) and treatment recommendations (e.g., chemotherapy) based on individual clinical (e.g., stage), genomic (e.g., 21-gene-recurrence score), behavioral (e.g., smoking), and contextual (e.g., insurance) characteristics. Forty-five tools were validated, and nine had undergone usability testing. However, validation and usability testing included mostly White, educated, and/or insured individuals. The average quality assessment score of the tools was 16 (range: 6-46; potential maximum: 63). CONCLUSIONS There was wide variation in the characteristics, quality, validity, and usability of the tools. Future studies should consider diverse populations for tool development and testing. IMPLICATIONS FOR CANCER SURVIVORS There are tools available to support personalized breast cancer treatment and survivorship care decisions in clinical settings. It is important for both cancer survivors and physicians to carefully consider the quality, validity, and usability of these tools before using them to guide care decisions.
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Affiliation(s)
- Kaitlyn M Wojcik
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute On Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Dalya Kamil
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute On Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, 20892, USA
| | | | - Oliver W A Wilson
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute On Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Laney Smith
- Frederick P. Whiddon College of Medicine, Mobile, AL, USA
| | - Gisela Butera
- Office of Research Services, National Institutes of Health Library, Bethesda, MD, USA
| | - Claudine Isaacs
- Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Allison Kurian
- Departments of Medicine and Epidemiology and Population Health at Stanford University School of Medicine, Stanford, CA, USA
| | - Jinani Jayasekera
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute On Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, 20892, USA.
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18
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Zhang S, Li H, Jing Q, Shen W, Luo W, Dai R. Anesthesia decision analysis using a cloud-based big data platform. Eur J Med Res 2024; 29:201. [PMID: 38528564 DOI: 10.1186/s40001-024-01764-0] [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: 11/26/2023] [Accepted: 03/01/2024] [Indexed: 03/27/2024] Open
Abstract
Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.
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Affiliation(s)
- Shuiting Zhang
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Hui Li
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Qiancheng Jing
- Department of Otolaryngology Head and Neck Surgery, Hengyang Medical School, The Affiliated Changsha Central Hospital, University of South China, Changsha, 410000, Hunan, China
| | - Weiyun Shen
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Wei Luo
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Ruping Dai
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China.
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19
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Beck TL, Carloni P, Asthagiri DN. All-Atom Biomolecular Simulation in the Exascale Era. J Chem Theory Comput 2024; 20:1777-1782. [PMID: 38382017 DOI: 10.1021/acs.jctc.3c01276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.
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Affiliation(s)
- Thomas L Beck
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Paolo Carloni
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- Department of Physics, RWTH Aachen University, D-52078 Aachen, Germany
| | - Dilipkumar N Asthagiri
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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20
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Niemann U, Boecking B, Brueggemann P, Spiliopoulou M, Mazurek B. Heterogeneity in response to treatment across tinnitus phenotypes. Sci Rep 2024; 14:2111. [PMID: 38267701 PMCID: PMC10808188 DOI: 10.1038/s41598-024-52651-x] [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: 12/30/2022] [Accepted: 01/22/2024] [Indexed: 01/26/2024] Open
Abstract
The clinical heterogeneity of chronic tinnitus poses major challenges to patient management and prompts the identification of distinct patient subgroups (or phenotypes) that respond more predictable to a particular treatment. We model heterogeneity in treatment response among phenotypes of tinnitus patients concerning their change in self-reported health burden, psychological characteristics, and tinnitus characteristics. Before and after a 7-day multimodal treatment, 989 tinnitus patients completed 14 assessment questionnaires, from which 64 variables measured general tinnitus characteristics, quality of life, pain experiences, somatic expressions, affective symptoms, tinnitus-related distress, internal resources, and perceived stress. Our approach encompasses mechanisms for patient phenotyping, visualizations of the phenotypes and their change with treatment in a projected space, and the extraction of patient subgroups based on their change with treatment. On average, all four distinct phenotypes identified at the pre-intervention baseline showed improved values for nearly all the considered variables following the intervention. However, a considerable intra-phenotype heterogeneity was noted. Five clusters of change reflected variations in the observed improvements among individuals. These patterns of treatment effects were identified to be associated with baseline phenotypes. Our exploratory approach establishes a groundwork for future studies incorporating control groups to pinpoint patient subgroups that are more likely to benefit from specific treatments. This strategy not only has the potential to advance personalized medicine but can also be extended to a broader spectrum of patients with various chronic conditions.
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Affiliation(s)
- Uli Niemann
- University Library, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany.
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany.
| | - Benjamin Boecking
- Charité-Universitaetsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Petra Brueggemann
- Charité-Universitaetsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Myra Spiliopoulou
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, 39106, Germany
| | - Birgit Mazurek
- Charité-Universitaetsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany
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21
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Bernier A, Knoppers BM, Bermudez P, Beauvais MJS, Thorogood A. Open Data governance at the Canadian Open Neuroscience Platform (CONP): From the Walled Garden to the Arboretum. Gigascience 2024; 13:giad114. [PMID: 38217404 PMCID: PMC10787360 DOI: 10.1093/gigascience/giad114] [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: 07/17/2023] [Revised: 11/14/2023] [Accepted: 12/10/2023] [Indexed: 01/15/2024] Open
Abstract
Scientific research communities pursue dual imperatives in implementing strategies to share their data. These communities attempt to maximize the accessibility of biomedical data for downstream research use, in furtherance of open science objectives. Simultaneously, such communities safeguard the interests of research participants through data stewardship measures and the integration of suitable risk disclosures to the informed consent process. The Canadian Open Neuroscience Platform (CONP) convened an Ethics and Governance Committee composed of experts in bioethics, neuroethics, and law to develop holistic policy tools, organizational approaches, and technological supports to align the open governance of data with ethical and legal norms. The CONP has adopted novel platform governance methods that favor full data openness, legitimated through the use of robust deidentification processes and informed consent practices. The experience of the CONP is articulated as a potential template for other open science efforts to further build upon. This experience highlights informed consent guidance, deidentification practices, ethicolegal metadata, platform-level norms, and commercialization and publication policies as the principal pillars of a practicable approach to the governance of open data. The governance approach adopted by the CONP stands as a viable model for the broader neuroscience and open science communities to adopt for sharing data in full open access.
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Affiliation(s)
- Alexander Bernier
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, 740, Dr Penfield Ave, suite 5200, Montréal, Québec H3A 0G1, Canada
| | - Bartha M Knoppers
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, 740, Dr Penfield Ave, suite 5200, Montréal, Québec H3A 0G1, Canada
| | - Patrick Bermudez
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Michael J S Beauvais
- Faculty of Law, University of Toronto, Falconer Hall, 84 Queens Park, Toronto, Ontario M5S 2C5, Canada
| | - Adrian Thorogood
- The Terry Fox Research Institute, 110 Pine Ave W, Montreal, Quebec H2W IR7, Canada
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22
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Abdullah N, Husin NF, Goh YX, Kamaruddin MA, Abdullah MS, Yusri AF, Kamalul Arifin AS, Jamal R. Development of digital health management systems in longitudinal study: The Malaysian cohort experience. Digit Health 2024; 10:20552076241277481. [PMID: 39281044 PMCID: PMC11402075 DOI: 10.1177/20552076241277481] [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: 02/12/2024] [Accepted: 08/07/2024] [Indexed: 09/18/2024] Open
Abstract
Background The management of extensive longitudinal data in cohort studies presents significant challenges, particularly in middle-income countries like Malaysia where technological resources may be limited. These challenges include ensuring data integrity, security, and scalability of storage solutions over extended periods. Objective This article outlines innovative methods developed and implemented by The Malaysian Cohort project to effectively manage and maintain large-scale databases from project inception through the follow-up phase, ensuring robust data privacy and security. Methods We describe the comprehensive strategies employed to develop and sustain the database infrastructure necessary for handling large volumes of data collected during the study. This includes the integration of advanced information management systems and adherence to stringent data security protocols. Outcomes Key achievements include the establishment of a scalable database architecture and an effective data privacy framework that together support the dynamic requirements of longitudinal healthcare research. The solutions implemented serve as a model for similar cohort studies in resource-limited settings. The article also explores the broader implications of these methodologies for public health and personalized medicine, addressing both the challenges posed by big data in healthcare and the opportunities it offers for enhancing disease prevention and management strategies. Conclusion By sharing these insights, we aim to contribute to the global discourse on improving data management practices in cohort studies and to assist other researchers in overcoming the complexities associated with longitudinal health data.
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Affiliation(s)
- Noraidatulakma Abdullah
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Nurul Faeizah Husin
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Ying-Xian Goh
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Mohd Arman Kamaruddin
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Mohd Shaharom Abdullah
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Aiman Fitri Yusri
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | - Rahman Jamal
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300163. [PMID: 38223896 PMCID: PMC10784210 DOI: 10.1002/gch2.202300163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/20/2023] [Indexed: 01/16/2024]
Abstract
The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Kexin Huang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Dewei Yang
- College of Advanced Manufacturing EngineeringChongqing University of Posts and TelecommunicationsChongqingChongqing400000China
| | - Weiling Zhao
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
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Murillo Carrasco AG, Furuya TK, Uno M, Citrangulo Tortelli T, Chammas R. deltaXpress (ΔXpress): a tool for mapping differentially correlated genes using single-cell qPCR data. BMC Bioinformatics 2023; 24:402. [PMID: 37884889 PMCID: PMC10605457 DOI: 10.1186/s12859-023-05541-4] [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: 03/16/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND High-throughput experiments provide deep insight into the molecular biology of different species, but more tools need to be developed to handle this type of data. At the transcriptomics level, quantitative Polymerase Chain Reaction technology (qPCR) can be affordably adapted to produce high-throughput results through a single-cell approach. In addition to comparative expression profiles between groups, single-cell approaches allow us to evaluate and propose new dependency relationships among markers. However, this alternative has not been explored before for large-scale qPCR-based experiments. RESULTS Herein, we present deltaXpress (ΔXpress), a web app for analyzing data from single-cell qPCR experiments using a combination of HTML and R programming languages in a friendly environment. This application uses cycle threshold (Ct) values and categorical information for each sample as input, allowing the best pair of housekeeping genes to be chosen to normalize the expression of target genes. ΔXpress emulates a bulk analysis by observing differentially expressed genes, but in addition, it allows the discovery of pairwise genes differentially correlated when comparing two experimental conditions. Researchers can download normalized data or use subsequent modules to map differentially correlated genes, perform conventional comparisons between experimental groups, obtain additional information about their genes (gene glossary), and generate ready-to-publication images (600 dots per inch). CONCLUSIONS ΔXpress web app is freely available to non-commercial users at https://alexismurillo.shinyapps.io/dXpress/ and can be used for different experiments in all technologies involving qPCR with at least one housekeeping region.
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Affiliation(s)
- Alexis Germán Murillo Carrasco
- Center for Translational Research in Oncology (LIM24), Instituto Do Cancer Do Estado de Sao Paulo (ICESP), Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), São Paulo, SP, CEP 01246-000, Brazil.
- Comprehensive Center for Precision Oncology, Universidade de Sao Paulo, São Paulo, Brazil.
| | - Tatiane Katsue Furuya
- Center for Translational Research in Oncology (LIM24), Instituto Do Cancer Do Estado de Sao Paulo (ICESP), Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), São Paulo, SP, CEP 01246-000, Brazil
- Comprehensive Center for Precision Oncology, Universidade de Sao Paulo, São Paulo, Brazil
| | - Miyuki Uno
- Center for Translational Research in Oncology (LIM24), Instituto Do Cancer Do Estado de Sao Paulo (ICESP), Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), São Paulo, SP, CEP 01246-000, Brazil
- Comprehensive Center for Precision Oncology, Universidade de Sao Paulo, São Paulo, Brazil
| | - Tharcisio Citrangulo Tortelli
- Center for Translational Research in Oncology (LIM24), Instituto Do Cancer Do Estado de Sao Paulo (ICESP), Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), São Paulo, SP, CEP 01246-000, Brazil
- Comprehensive Center for Precision Oncology, Universidade de Sao Paulo, São Paulo, Brazil
| | - Roger Chammas
- Center for Translational Research in Oncology (LIM24), Instituto Do Cancer Do Estado de Sao Paulo (ICESP), Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP), São Paulo, SP, CEP 01246-000, Brazil.
- Comprehensive Center for Precision Oncology, Universidade de Sao Paulo, São Paulo, Brazil.
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Proudfoot KL. ADSA Foundation Scholar Award: What makes for a good life for transition dairy cows? Current research and future directions. J Dairy Sci 2023; 106:5896-5907. [PMID: 37479580 DOI: 10.3168/jds.2022-23194] [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: 12/23/2022] [Accepted: 03/20/2023] [Indexed: 07/23/2023]
Abstract
Dairy cows experience several challenges during the transition period, historically defined as the 3 wk before to 3 wk after calving. During this 6-wk window, cows undergo a series of social, nutritional, and physical changes that affect their quality of life. Cows are also at the highest risk of becoming ill in the days and weeks after calving compared with any other period in their adult life. Because of this, the transition cow has been a central focus of dairy cattle research for at least the last 50 yr, with much of this work targeted at identifying, treating, and preventing postpartum disease. However, understanding what makes for a good life for transition cows requires consideration of more than just their health. When considering a cow's welfare, we must also include her emotional experiences and ability to live a reasonably natural life. To gain a broader perspective on the welfare of transition cows that goes beyond their health, continued inter- and transdisciplinary approaches are needed. The aims of this narrative review are to (1) describe a framework used to study animal welfare, which includes different perspectives on what makes for a good life for animals using examples from transition cow research, (2) summarize the advancements we have made in developing our understanding of the welfare of transition dairy cows over the last several decades, (3) identify gaps in the literature and propose new and continued topics for research, and (4) suggest a path forward for researchers, including the use of methods from both the natural and social sciences to rethink existing problems, understanding barriers to adoption of evidence-based practice, and prepare for future challenges.
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Affiliation(s)
- K L Proudfoot
- Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A4P3, Canada.
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Mayo KR, Basford MA, Carroll RJ, Dillon M, Fullen H, Leung J, Master H, Rura S, Sulieman L, Kennedy N, Banks E, Bernick D, Gauchan A, Lichtenstein L, Mapes BM, Marginean K, Nyemba SL, Ramirez A, Rotundo C, Wolfe K, Xia W, Azuine RE, Cronin RM, Denny JC, Kho A, Lunt C, Malin B, Natarajan K, Wilkins CH, Xu H, Hripcsak G, Roden DM, Philippakis AA, Glazer D, Harris PA. The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research. Annu Rev Biomed Data Sci 2023; 6:443-464. [PMID: 37561600 PMCID: PMC11157478 DOI: 10.1146/annurev-biodatasci-122120-104825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.
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Affiliation(s)
- Kelsey R Mayo
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Melissa A Basford
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert J Carroll
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Moira Dillon
- Verily Life Sciences, South San Francisco, California, USA
| | - Heather Fullen
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jesse Leung
- Verily Life Sciences, South San Francisco, California, USA
| | - Hiral Master
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shimon Rura
- Verily Life Sciences, South San Francisco, California, USA
| | - Lina Sulieman
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric Banks
- Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - David Bernick
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Asmita Gauchan
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lee Lichtenstein
- Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kayla Marginean
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Steve L Nyemba
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Andrea Ramirez
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Charissa Rotundo
- Vanderbilt University Medical Center Enterprise Cybersecurity, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Keri Wolfe
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Weiyi Xia
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Romuladus E Azuine
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Robert M Cronin
- Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Joshua C Denny
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Abel Kho
- Department of Medicine and Institute for Augmented Intelligence in Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Christopher Lunt
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Bradley Malin
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Consuelo H Wilkins
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Hua Xu
- Section of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, Connecticut, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Dan M Roden
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - David Glazer
- Verily Life Sciences, South San Francisco, California, USA
| | - Paul A Harris
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
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Gupta NS, Kumar P. Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Comput Biol Med 2023; 162:107051. [PMID: 37271113 DOI: 10.1016/j.compbiomed.2023.107051] [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: 04/11/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.
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Affiliation(s)
- Nancy Sanjay Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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Wu J, Li J, Eickhoff SB, Scheinost D, Genon S. The challenges and prospects of brain-based prediction of behaviour. Nat Hum Behav 2023; 7:1255-1264. [PMID: 37524932 DOI: 10.1038/s41562-023-01670-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/27/2023] [Indexed: 08/02/2023]
Abstract
Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.
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Affiliation(s)
- Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, New Haven, CT, USA
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
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Nakamura K, Uchino E, Sato N, Araki A, Terayama K, Kojima R, Murashita K, Itoh K, Mikami T, Tamada Y, Okuno Y. Individual health-disease phase diagrams for disease prevention based on machine learning. J Biomed Inform 2023; 144:104448. [PMID: 37467834 DOI: 10.1016/j.jbi.2023.104448] [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: 01/07/2023] [Revised: 07/09/2023] [Accepted: 07/16/2023] [Indexed: 07/21/2023]
Abstract
Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in chronic disease development. Machine-learning techniques have enabled precise personal-level disease prediction by capturing individual differences in multivariate data. However, it is challenging to identify what aspects should be improved for disease prevention based on future disease-onset prediction because of the complex relationships among multiple biomarkers. Here, we present a health-disease phase diagram (HDPD) that represents an individual's health state by visualizing the future-onset boundary values of multiple biomarkers that fluctuate early in the disease progression process. In HDPDs, future-onset predictions are represented by perturbing multiple biomarker values while accounting for dependencies among variables. We constructed HDPDs for 11 diseases using longitudinal health checkup cohort data of 3,238 individuals, comprising 3,215 measurement items and genetic data. The improvement of biomarker values to the non-onset region in HDPD remarkably prevented future disease onset in 7 out of 11 diseases. HDPDs can represent individual physiological states in the onset process and be used as intervention goals for disease prevention.
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Affiliation(s)
- Kazuki Nakamura
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Research and Business Development Department, Kyowa Hakko Bio Co., Ltd., Tokyo 100-0004, Japan
| | - Eiichiro Uchino
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Noriaki Sato
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Ayano Araki
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Kei Terayama
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Graduate School of Medical Life Science, Yokohama City University, Kanagawa 230-0045, Japan
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Koichi Murashita
- Center of Innovation Research Initiatives Organization (The Center of Healthy Aging Innovation), Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Ken Itoh
- Department of Stress Response Science, Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Tatsuya Mikami
- Innovation Center for Health Promotion, Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Yoshinori Tamada
- Innovation Center for Health Promotion, Graduate School of Medicine, Hirosaki University, Aomori 036-8562, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
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Baird A, Westphalen C, Blum S, Nafria B, Knott T, Sargeant I, Harnik H, Brooke N, Wicki N, Wong‐Rieger D. How can we deliver on the promise of precision medicine in oncology and beyond? A practical roadmap for action. Health Sci Rep 2023; 6:e1349. [PMID: 37359405 PMCID: PMC10286856 DOI: 10.1002/hsr2.1349] [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: 03/02/2023] [Revised: 05/24/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023] Open
Abstract
Background Precision medicine (PM) is a form of personalized medicine that recognizes that individuals with the same condition may have different underlying factors and uses molecular information to provide tailored treatments. This approach can improve treatment outcomes and transform lives through favorable risk/benefit ratios, avoidance of ineffective interventions, and possible cost savings, as evidenced in the field of lung cancer and other oncology/therapeutic settings, including cardiac disease, diabetes, and rare diseases. However, the potential benefits of PM have yet to be fully realized. Discussion There are many barriers to the implementation of PM in clinical practice, including fragmentation of the PM landscape, siloed approaches to address shared challenges, unwarranted variation in availability and access to PM, lack of standardization, and limited understanding of patients' experience and needs throughout the PM pathway. We believe that a diverse, intersectoral multistakeholder collaboration, with three main pillars of activity: generation of data to demonstrate the benefit of PM, education to support informed decision-making, and addressing barriers across the patient pathway, is necessary to reach the shared goal of making PM an accessible and sustainable reality. Besides healthcare providers, researchers, policymakers/regulators/payers, and industry representatives, patients in particular must be equal partners and should be central to the PM approach-from early research through to clinical trials and approval of new treatments-to ensure it represents their entire experience and identifies barriers, solutions, and opportunities at the point of delivery. Conclusion We propose a practical and iterative roadmap to advance PM and call for all stakeholders across the healthcare system to employ a collaborative, cocreated, patient-centered methodology to close gaps and fully realize the potential of PM.
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Affiliation(s)
- Anne‐Marie Baird
- Lung Cancer Europe (LuCE)BernSwitzerland
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
| | - C. Benedikt Westphalen
- Comprehensive Cancer Center Munich and Department of Medicine IIIUniversity Hospital, LMU MunichMunichGermany
| | - Sandra Blum
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- RocheBaselSwitzerland
| | - Begonya Nafria
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- Institut de Recerca Sant Joan de DéuBarcelonaSpain
- Innovation and Research Department, Hospital Sant Joan de Déu PgBarcelonaSpain
| | - Tanya Knott
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- Sarah Jennifer Knott (SJK) FoundationDublinRepublic of Ireland
| | | | - Helena Harnik
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- The SynergistBrusselsBelgium
| | - Nicholas Brooke
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- The SynergistBrusselsBelgium
| | - Nicole Wicki
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- The SynergistBrusselsBelgium
| | - Durhane Wong‐Rieger
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- Canadian Organization for Rare DisordersTorontoOntarioCanada
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Schmid N, Ghinescu M, Schanz M, Christ M, Schricker S, Ketteler M, Alscher MD, Franke U, Goebel N. Algorithm-based detection of acute kidney injury according to full KDIGO criteria including urine output following cardiac surgery: a descriptive analysis. BioData Min 2023; 16:12. [PMID: 36927544 PMCID: PMC10022284 DOI: 10.1186/s13040-023-00323-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/17/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Automated data analysis and processing has the potential to assist, improve and guide decision making in medical practice. However, by now it has not yet been fully integrated in a clinical setting. Herein we present the first results of applying algorithm-based detection to the diagnosis of postoperative acute kidney injury (AKI) comprising patient data from a cardiac surgical intensive care unit (ICU). METHODS First, we generated a well-defined study population of cardiac surgical ICU patients by implementing an application programming interface (API) to extract, clean and select relevant data from the archived digital patient management system. Health records of N = 21,045 adult patients admitted to the ICU following cardiac surgery between 2012 and 2022 were analyzed. Secondly, we developed a software functionality to detect the incidence of AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria, including urine output. Incidence, severity, and temporal evolution of AKI were assessed. RESULTS With the use of our automated data analyzing model the overall incidence of postoperative AKI was 65.4% (N = 13,755). Divided by stages, AKI 2 was the most frequent maximum disease stage with 30.5% of patients (stage 1 in 17.6%, stage 3 in 17.2%). We observed considerable temporal divergence between first detections and maximum AKI stages: 51% of patients developed AKI stage 2 or 3 after a previously identified lower stage. Length of ICU stay was significantly prolonged in AKI patients (8.8 vs. 6.6 days, p < 0.001) and increased for higher AKI stages up to 10.1 days on average. In terms of AKI criteria, urine output proved to be most relevant, contributing to detection in 87.3% (N = 12,004) of cases. CONCLUSION The incidence of postoperative AKI following cardiac surgery is strikingly high with 65.4% when using full KDIGO-criteria including urine output. Automated data analysis demonstrated reliable early detection of AKI with progressive deterioration of renal function in the majority of patients, therefore allowing for potential earlier therapeutic intervention for preventing or lessening disease progression, reducing the length of ICU stay, and ultimately improving overall patient outcomes.
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Affiliation(s)
- Nico Schmid
- Department of Medical Informatics, Robert Bosch Society for Medical Research, Stuttgart, Germany
| | - Mihnea Ghinescu
- Department of Cardiovascular Surgery, Robert Bosch Hospital, Stuttgart, Germany
| | - Moritz Schanz
- Division of General Internal Medicine and Nephrology, Department of Internal Medicine, Robert Bosch Hospital, Stuttgart, Germany.
| | - Micha Christ
- Department of Medical Informatics, Robert Bosch Society for Medical Research, Stuttgart, Germany
| | - Severin Schricker
- Division of General Internal Medicine and Nephrology, Department of Internal Medicine, Robert Bosch Hospital, Stuttgart, Germany
| | - Markus Ketteler
- Division of General Internal Medicine and Nephrology, Department of Internal Medicine, Robert Bosch Hospital, Stuttgart, Germany
| | - Mark Dominik Alscher
- Executive Chief Physician of Robert Bosch Hospital and director of Robert Bosch Society for Medical Research, Stuttgart, Germany
| | - Ulrich Franke
- Department of Cardiovascular Surgery, Robert Bosch Hospital, Stuttgart, Germany
| | - Nora Goebel
- Department of Cardiovascular Surgery, Robert Bosch Hospital, Stuttgart, Germany
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Mc Cormack BA, González-Cantó E, Tomás-Pérez S, Aghababyan C, Marí-Alexandre J, Götte M, Gilabert-Estellés J. Special Issue "miRNAs in the Era of Personalized Medicine: From Biomarkers to Therapeutics 2.0". Int J Mol Sci 2023; 24:ijms24031951. [PMID: 36768275 PMCID: PMC9916445 DOI: 10.3390/ijms24031951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
Personalized medicine has become a new paradigm in the management of a variety of diseases [...].
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Affiliation(s)
- Bárbara Andrea Mc Cormack
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain
| | - Eva González-Cantó
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain
| | - Sarai Tomás-Pérez
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain
| | - Cristina Aghababyan
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain
- Department of Obstetrics and Gynaecology, General University Hospital of Valencia Consortium, 46014 Valencia, Spain
| | - Josep Marí-Alexandre
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain
- Department of Pathology, General University Hospital of Valencia Consortium, 46014 Valencia, Spain
- Correspondence: (J.M.-A.); (M.G.)
| | - Martin Götte
- Research Laboratory, Department of Gynecology and Obstetrics, Münster University Hospital, 48149 Münster, Germany
- Correspondence: (J.M.-A.); (M.G.)
| | - Juan Gilabert-Estellés
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain
- Department of Obstetrics and Gynaecology, General University Hospital of Valencia Consortium, 46014 Valencia, Spain
- Department of Pediatrics, Obstetrics and Gynaecology, University of Valencia, 46010 Valencia, Spain
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Zaizar-Fregoso SA, Lara-Esqueda A, Hernández-Suarez CM, Delgado-Enciso J, Garcia-Nevares A, Canseco-Avila LM, Guzman-Esquivel J, Rodriguez-Sanchez IP, Martinez-Fierro ML, Ceja-Espiritu G, Ochoa-Díaz-Lopez H, Espinoza-Gomez F, Sanchez-Diaz I, Delgado-Enciso I. Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective. J Diabetes Res 2023; 2023:8898958. [PMID: 36846513 PMCID: PMC9949947 DOI: 10.1155/2023/8898958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Diabetes mellitus is a disease with no cure that can cause complications and even death. Moreover, over time, it will lead to chronic complications. Predictive models have been used to identify people with a tendency to develop diabetes mellitus. At the same time, there is limited information regarding the chronic complications of patients with diabetes. Our study is aimed at creating a machine-learning model that will be able to identify the risk factors of a diabetic patient developing chronic complications such as amputations, myocardial infarction, stroke, nephropathy, and retinopathy. The design is a national nested case-control study with 63,776 patients and 215 predictors with four years of data. Using an XGBoost model, the prediction of chronic complications has an AUC of 84%, and the model has identified the risk factors for chronic complications in patients with diabetes. According to the analysis, the most crucial risk factors based on SHAP values (Shapley additive explanations) are continued management, metformin treatment, age between 68 and 104 years, nutrition consultation, and treatment adherence. But we highlight two exciting findings. The first is a reaffirmation that high blood pressure figures across patients with diabetes without hypertension become a significant risk factor at diastolic > 70 mmHg (OR: 1.095, 95% CI: 1.078-1.113) or systolic > 120 mmHg (OR: 1.147, 95% CI: 1.124-1.171). Furthermore, people with diabetes with a BMI > 32 (overall obesity) (OR: 0.816, 95% CI: 0.8-0.833) have a statistically significant protective factor, which the paradox of obesity may explain. In conclusion, the results we have obtained show that artificial intelligence is a powerful and feasible tool to use for this type of study. However, we suggest that more studies be conducted to verify and elaborate upon our findings.
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Affiliation(s)
| | - Agustin Lara-Esqueda
- Facultad de Psicología y Terapia de la Comunicación Humana de la Universidad Juárez del Estado Durango, Durango 81301, Mexico
| | | | - Josuel Delgado-Enciso
- Fundacion para la Etica Educacion e Investigacion del Cancer del Instituto Estatal de Cancerologia de Colima AC, Colima 28085, Mexico
| | | | - Luis M. Canseco-Avila
- Facultad de Ciencias Químicas Campus IV, Universidad Autónoma de Chiapas, Tapachula, 30700 Chiapas, Mexico
| | - Jose Guzman-Esquivel
- Instituto Mexicano del Seguro Social, Delegación Colima, Villa de Alvarez, 28983 Colima, Mexico
| | - Iram P. Rodriguez-Sanchez
- Facultad de Ciencias Biológicas, Universidad Autonoma de Nuevo Leon, San Nicolás de los Garza, 66455 Nuevo Leon, Mexico
| | | | | | - Hector Ochoa-Díaz-Lopez
- Departamento de Salud, El Colegio de La Frontera Sur, San Cristóbal de Las Casas, 29290 Chiapas, Mexico
| | | | - Iyari Sanchez-Diaz
- Subdirección de Prevención y Protección a la Salud, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Ciudad de Mexico, 14070, Mexico
| | - Ivan Delgado-Enciso
- Facultad de Medicina, Universidad de Colima, Colima 28040, Mexico
- Instituto Estatal de Cancerología, Servicios de Salud del Estado de Colima, Colima 28085, Mexico
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Wolfsberger W, Chhugani K, Shchubelka K, Frolova A, Salyha Y, Zlenko O, Arych M, Dziuba D, Parkhomenko A, Smolanka V, Gümüş ZH, Sezgin E, Diaz-Lameiro A, Toth VR, Maci M, Bortz E, Kondrashov F, Morton PM, Łabaj PP, Romero V, Hlávka J, Mangul S, Oleksyk TK. Scientists without borders: lessons from Ukraine. Gigascience 2022; 12:giad045. [PMID: 37496156 PMCID: PMC10372202 DOI: 10.1093/gigascience/giad045] [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: 05/05/2023] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 07/28/2023] Open
Abstract
Conflicts and natural disasters affect entire populations of the countries involved and, in addition to the thousands of lives destroyed, have a substantial negative impact on the scientific advances these countries provide. The unprovoked invasion of Ukraine by Russia, the devastating earthquake in Turkey and Syria, and the ongoing conflicts in the Middle East are just a few examples. Millions of people have been killed or displaced, their futures uncertain. These events have resulted in extensive infrastructure collapse, with loss of electricity, transportation, and access to services. Schools, universities, and research centers have been destroyed along with decades' worth of data, samples, and findings. Scholars in disaster areas face short- and long-term problems in terms of what they can accomplish now for obtaining grants and for employment in the long run. In our interconnected world, conflicts and disasters are no longer a local problem but have wide-ranging impacts on the entire world, both now and in the future. Here, we focus on the current and ongoing impact of war on the scientific community within Ukraine and from this draw lessons that can be applied to all affected countries where scientists at risk are facing hardship. We present and classify examples of effective and feasible mechanisms used to support researchers in countries facing hardship and discuss how these can be implemented with help from the international scientific community and what more is desperately needed. Reaching out, providing accessible training opportunities, and developing collaborations should increase inclusion and connectivity, support scientific advancements within affected communities, and expedite postwar and disaster recovery.
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Affiliation(s)
- Walter Wolfsberger
- Department of Biological Sciences, Oakland University,
Rochester, MI 48309-4479, USA
| | - Karishma Chhugani
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and
Pharmaceutical Sciences, University of Southern California,
Los Angeles, CA 90033, USA
| | - Khrystyna Shchubelka
- Department of Biological Sciences, Oakland University,
Rochester, MI 48309-4479, USA
| | - Alina Frolova
- Institute of Molecular Biology and Genetics of National Academy of Sciences
of Ukraine, Kyiv Academic University, Kyiv 03143,
Ukraine
| | - Yuriy Salyha
- Institute of Animal Biology, National Academy of Agrarian Sciences (NAAS)
of Ukraine, Lviv 79034, Ukraine
| | - Oksana Zlenko
- National Scientific Center “Institute of Experimental and Clinical
Veterinary Medicine,” Kharkiv 61023, Ukraine
| | - Mykhailo Arych
- Institute of Economics and Management, National University of Food
Technologies (NUFT) of Ukraine, Kyiv 01601,
Ukraine
| | - Dmytro Dziuba
- Department of Anesthesiology and Intensive Care, P.L. Shpyk
NUHC Ukraine, Kyiv 04112, Ukraine
| | - Andrii Parkhomenko
- Department of Finance and Business Economics, Marshall School
of Business, University of Southern California, Los Angeles, CA 90089, USA
| | - Volodymyr Smolanka
- Department of Medicine, Uzhhorod National University,
Uzhhorod 88000, Ukraine
| | - Zeynep H Gümüş
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at
Mount Sinai, New York, NY 10029, USA
| | - Efe Sezgin
- Department of Food Engineering, Izmir Institute of
Technology, Urla, Izmir 35430, Turkey
| | - Alondra Diaz-Lameiro
- Department of Biology, University of Puerto Rico at Mayagüez,
Mayagüez 00681, Puerto
Rico
| | - Viktor R Toth
- Aquatic Botany and Microbial Ecology Research Group, Balaton Limnological
Research Institute, Tihany 8237, Hungary
| | - Megi Maci
- Stritch School of Medicine, Loyola University Chicago,
Maywood, IL 60153, USA
| | - Eric Bortz
- Department of Biological Sciences, University of Alaska,
Anchorage, AK 99508, USA
| | - Fyodor Kondrashov
- Institute of Science and Technology Austria,
Klosterneuburg 3400, Austria
| | - Patricia M Morton
- Department of Sociology, Department of Public Health, Wayne State
University, Detroit, MI 48202, USA
| | - Paweł P Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University,
Kraków 30-348, Poland
| | - Veronika Romero
- Department of Neurobiology, University of Utah, Salt Lake
City, UT 84112, USA
| | - Jakub Hlávka
- Price School of Public Policy, University of Southern
California, Los Angeles, CA 90089-3333, USA
- Masaryk University, Brno 6017, Czech Republic
| | - Serghei Mangul
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and
Pharmaceutical Sciences, University of Southern California,
Los Angeles, CA 90033, USA
- Department of Computational Biology, University of Southern
California, Los Angeles, CA 90033, USA
| | - Taras K Oleksyk
- Department of Biological Sciences, Oakland University,
Rochester, MI 48309-4479, USA
- Department of Biology, Uzhhorod National University, Uzhhorod
88000, Ukraine
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Khan S, Khan HU, Nazir S. Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing. Sci Rep 2022; 12:22377. [PMID: 36572709 PMCID: PMC9792582 DOI: 10.1038/s41598-022-26090-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/09/2022] [Indexed: 12/27/2022] Open
Abstract
Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.
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Affiliation(s)
- Sulaiman Khan
- Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Habib Ullah Khan
- Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Swabi, Pakistan
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Huang X, Chen Z, Xiang X, Liu Y, Long X, Li K, Qin M, Long C, Mo X, Tang W, Liu J. Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine. EPMA J 2022; 13:671-697. [PMID: 36505892 PMCID: PMC9727047 DOI: 10.1007/s13167-022-00305-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 11/01/2022] [Indexed: 11/24/2022]
Abstract
Background The N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM). Methods The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment. Results The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/. Conclusion The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00305-1.
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Affiliation(s)
- Xiaoliang Huang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Zuyuan Chen
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Xiaoyun Xiang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Yanling Liu
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Xingqing Long
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Kezhen Li
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Mingjian Qin
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Chenyan Long
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Xianwei Mo
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Weizhong Tang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Jungang Liu
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
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Schwartz CE, Rapkin BD, Borowiec K, Finkelstein JA. Cognitive Processes during Recovery: Moving toward Personalized Spine Surgery Outcomes. J Pers Med 2022; 12:jpm12101545. [PMID: 36294682 PMCID: PMC9605664 DOI: 10.3390/jpm12101545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/13/2022] [Accepted: 09/16/2022] [Indexed: 11/24/2022] Open
Abstract
This paper focuses on a novel application of personalized medicine: the ways one thinks about health (i.e., appraisal processes) as relevant predictors of spine-surgery response. This prospective longitudinal cohort study (n = 235) investigated how appraisal processes relate to outcomes of spinal decompression and/or fusion surgery, from pre-surgery through one-year post-surgery. Patient-reported outcomes assessed spine-specific disability (Oswestry Disability Index (ODI)), mental health functioning (Rand-36 Mental Component Score (MCS)), and cognitive appraisal processes (how people recall past experiences and to whom they compare themselves). Analysis of Variance examined the appraisal-outcomes association in separate models at pre-surgery, 3 months, and 12 months. We found that appraisal processes explained less variance at pre-surgery than later and were differentially relevant to health outcomes at different times in the spine-surgery recovery trajectory. For the ODI, recall of the seriousness of their condition was most prominent early in recovery, and comparing themselves to positive standards was most prominent later. For the MCS, not focusing on the negative aspects of their condition and/or on how others see them was associated with steady improvement and higher scores at 12 months. Appraisal processes are relevant to both spine-specific disability and mental-health functioning. Such processes are modifiable objects of attention for personalizing spine-surgery outcomes.
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Affiliation(s)
- Carolyn E. Schwartz
- DeltaQuest Foundation, Inc., Concord, MA 02111, USA
- Departments of Medicine and Orthopaedic Surgery, Tufts University School of Medicine, Boston, MA 02111, USA
- Correspondence: ; Tel.: +1-978-318-7914
| | - Bruce D. Rapkin
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Katrina Borowiec
- DeltaQuest Foundation, Inc., Concord, MA 02111, USA
- Department of Measurement, Evaluation, Statistics & Assessment, Boston College Lynch School of Education and Human Development, Chestnut Hill, MA 02467, USA
| | - Joel A. Finkelstein
- Department of Surgery, University of Toronto, Toronto, ON M4N 3M5, Canada
- Division of Orthopedic Surgery, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
- Division of Spine Surgery, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
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El Khatib M, Hamidi S, Al Ameeri I, Al Zaabi H, Al Marqab R. Digital Disruption and Big Data in Healthcare - Opportunities and Challenges. CLINICOECONOMICS AND OUTCOMES RESEARCH 2022; 14:563-574. [PMID: 36052095 PMCID: PMC9426864 DOI: 10.2147/ceor.s369553] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/15/2022] [Indexed: 11/23/2022] Open
Abstract
Background As the amount of medical data in the electronic medical records system (EMR) is increasing tremendously, the required time to read it by health providers is growing by the exact proportionality. This means that physicians must increase the time spared for each patient again by the precise proportionality. This may lead to exposing the accuracy and quality of the course of action to be taken for the patients. Increasing the physician’s required time for one patient means that the physician can see fewer patients. This will create an issue with the medical management authority as more physicians are needed, and higher expenses will be required. Purpose The two questions that arise here are 1. Identify the potential opportunities and challenges for extensive data analysis in the healthcare sector. 2. Evaluate different ways in which big medical data can be analyzed? Methods The authors identified the four concerned parties representing the four potential solutions dimensions to answer these two questions. These parties are 1. physicians, 2. health information systems management (HISM) departments, mainly the EMR system, and 3. Health management departments 4. Relevant Health Information Systems (HIS) parties. A literature review and 25 interviews were conducted. The interviews covered 1: Two global organizations: John Hopkins and Joint Commission International (JCI), 2: Three United Arab Emirates-based health organizations: Department of health in Abu Dhabi, SEHA in Abu Dhabi, Dubai health Authority (DHA) in Dubai, 3: 10 Physicians from different specialties, 4: Five EMR managers and 5: Five IT (Information Technology) professionals representing the HIS parties. Qualitative analysis is used as the approach for data analysis. Results Identifying the managerial and the technical recommendations to be utilized mainly based on digital disruption technologies, tools, and processes. Conclusion Healthcare has been slow in embracing digital disruption and transformation. In most areas, it is still in the initial stages. Recommendations are based on the UAE cases, highlighting the specific technologies and their features.
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Affiliation(s)
- Mounir El Khatib
- School of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
| | - Samer Hamidi
- School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
| | - Ishaq Al Ameeri
- School of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
| | - Hamad Al Zaabi
- School of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
| | - Rehab Al Marqab
- School of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
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Wardill HR, Sonis ST, Blijlevens NMA. Using real world data to advance the provision of supportive cancer care: mucositis as a case study. Curr Opin Support Palliat Care 2022; 16:161-167. [PMID: 35929562 DOI: 10.1097/spc.0000000000000600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
PURPOSE OF REVIEW For decades, clinical decision making and practice has been largely informed by data generated through randomized clinical trials (RCTs). By design, RCTs are highly restricted in both scope and scale, resulting in narrow indications and iterative advances in clinical practice. With the transition to electronic health records, there are now endless opportunities to utilize these 'real world' data (RWD) to make more substantive advances in our understanding that are, by nature, more applicable to reality. This review discusses the current paradigm of using big data to advance and inform the provision of supportive cancer care, using mucositis as a case study. RECENT FINDINGS Global efforts to synthesize RWD in cancer have almost exclusively focused on tumor classification and treatment efficacy, leveraging on routine tumor pathology and binary response outcomes. In contrast, clinical notes and billing codes are not as applicable to treatment side effects which require integration of both clinical and biological data, as well as patient-reported outcomes. SUMMARY Cancer treatment-induced toxicities are heterogeneous and complex, and as such, the use of RWD to better understand their etiology and interaction is challenging. Multidisciplinary cooperation and leadership are needed to improve data collection and governance to ensure the right data is accessible and reliable.
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Affiliation(s)
- Hannah R Wardill
- School of Biomedicine, The Faculty of health and Medical Sciences, The University of Adelaide
- Supportive Oncology Research Group, Precision Medicine (Cancer), The South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Steve T Sonis
- Division of Oral Medicine, Brigham and Women's Hospital and the Dana-Farber Cancer Institute; Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston
- Primary Endpoint Solutions, Waltham, Massachusetts, USA
| | - Nicole M A Blijlevens
- Department of Hematology
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
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Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:57-100. [PMID: 36008002 DOI: 10.1016/bs.pmbts.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India.
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Augustine CA, Keikhosrokiani P. A Hospital Information Management System With Habit-Change Features and Medial Analytical Support for Decision Making. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2022. [DOI: 10.4018/ijitsa.307019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A hospital information management system (Doctive) with habit-change features and medial analytical support for decision making is developed in this study to reduce the risks of heart diseases. Doctive is targeted for hospital authorities to monitor patients’ habits and to prescribe medication and advice accordingly. Furthermore, this system provides emergency assistance for patients based on their current location. The proposed system would be beneficial for monitoring and organizing patients’ information to ease data entry, data management, data access, data retrieval and finally decision making. Doctive is tested and evaluated by 41 people who are either medical experts or professionals in the field of data analytics and visualization. The results indicate a high acceptance rate towards using Doctive system in hospitals and very good usability of the system. Doctive can be useful for healthcare providers and developers to track users’ habits for reducing the risk of heart disease. In the future.
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Hiram Guzzi P, Petrizzelli F, Mazza T. Disease spreading modeling and analysis: a survey. Brief Bioinform 2022; 23:6606045. [PMID: 35692095 DOI: 10.1093/bib/bbac230] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization. RESULTS Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88110, Italy
| | - Francesco Petrizzelli
- Bioinformatics unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013, Italy
| | - Tommaso Mazza
- Bioinformatics unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013, Italy
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Matabuena M, Félix P, García-Meixide C, Gude F. Kernel machine learning methods to handle missing responses with complex predictors. Application in modelling five-year glucose changes using distributional representations. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106905. [PMID: 35649295 DOI: 10.1016/j.cmpb.2022.106905] [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: 10/14/2021] [Revised: 05/11/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Missing data is a ubiquitous problem in longitudinal studies due to the number of patients lost to follow-up. Kernel methods have enriched the machine learning field by successfully managing non-vectorial predictors, such as graphs, strings, and probability distributions, and have emerged as a promising tool for the analysis of complex data stemming from modern healthcare. This paper proposes a new set of kernel methods to handle missing data in the response variables. These methods will be applied to predict long-term changes in glycated haemoglobin (A1c), the primary biomarker used to diagnose and monitor the progression of diabetes mellitus, making emphasis on exploring the predictive potential of continuous glucose monitoring (CGM). METHODS We propose a new framework of non-linear kernel methods for testing statistical independence, selecting relevant predictors, and quantifying the uncertainty of the resultant predictive models. As a novelty in the clinical analysis, we used a distributional representation of CGM as a predictor and compared its performance with that of traditional diabetes biomarkers. RESULTS The results show that, after the incorporation of CGM information, predictive ability increases from R2=0.61 to R2=0.71. In addition, uncertainty analysis is useful for characterising some subpopulations where predictivity is worsened, and a more personalised clinical follow-up is advisable according to expected patient uncertainty in glucose values. CONCLUSIONS The proposed methods have proven to deal effectively with missing data. They also have the potential to improve the results of predictive tasks by including new complex objects as explanatory variables and modelling arbitrary dependence relations. The application of these methods to a longitudinal study of diabetes showed that the inclusion of a distributional representation of CGM data provides greater sensitivity in predicting five-year A1c changes than classical diabetes biomarkers and traditional CGM metrics.
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Affiliation(s)
- Marcos Matabuena
- CiTIUS (Centro Singular de Investigación en Tecnoloxías Intelixentes), Universidade de Santiago de Compostela, Santiago de Compostela 15782, Spain.
| | - Paulo Félix
- CiTIUS (Centro Singular de Investigación en Tecnoloxías Intelixentes), Universidade de Santiago de Compostela, Santiago de Compostela 15782, Spain
| | | | - Francisco Gude
- Unidade de Epidemioloxía Clínica, Complexo Hospitalario Universidade de Santiago (CHUS), Travesía da Choupana, Santiago de Compostela 15706, Spain
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Valcárcel LV, San José-Enériz E, Cendoya X, Rubio Á, Agirre X, Prósper F, Planes FJ. BOSO: A novel feature selection algorithm for linear regression with high-dimensional data. PLoS Comput Biol 2022; 18:e1010180. [PMID: 35639775 PMCID: PMC9187084 DOI: 10.1371/journal.pcbi.1010180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 06/10/2022] [Accepted: 05/07/2022] [Indexed: 11/18/2022] Open
Abstract
With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism. We present BOSO (Bilevel Optimization Selector Operator), a novel method to conduct feature selection in linear regression models. In machine learning, feature selection consists of identifying the subset of input variables (features) that are correctly associated with the response variable that is aimed to be predicted. An adequate feature selection is particularly relevant for high-dimensional datasets, commonly encountered in biomedical research questions that rely on -omics data, e.g. predictive models of drug sensitivity, resistance or toxicity, construction of gene regulatory networks, biomarker selection or association studies. The need of feature selection is emphasized in many of these complex problems, since the number of features is greater than the number of samples, which makes it harder to obtain accurate and general predictive models. In this context, we show that the models derived by BOSO make a better combination of accuracy and simplicity than competing approaches in the literature. The relevance of BOSO is illustrated in the prediction of drug sensitivity of cancer cell lines, using RNA-seq data and drug screenings from GDSC (Genomics of Drug Sensitivity in Cancer) database. BOSO obtains linear regression models with a similar level of accuracy but involving a substantially lower number of features, which simplifies the interpretation and validation of predictive models.
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Affiliation(s)
- Luis V. Valcárcel
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
| | - Edurne San José-Enériz
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
| | - Xabier Cendoya
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
| | - Ángel Rubio
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica, Pamplona, Spain
- Universidad de Navarra, DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, Pamplona, Spain
| | - Xabier Agirre
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
| | - Felipe Prósper
- Universidad de Navarra, CIMA Centro de Investigación de Medicina Aplicada, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, Pamplona, Spain
- IdiSNA Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
- Clínica Universidad de Navarra, Pamplona, Spain
| | - Francisco J. Planes
- Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain
- Universidad de Navarra, Centro de Ingeniería Biomédica, Pamplona, Spain
- Universidad de Navarra, DATAI Instituto de Ciencia de los Datos e Inteligencia Artificial, Pamplona, Spain
- * E-mail:
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Abstract
Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA; .,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA;
| | - Guy Nir
- Department of Biochemistry and Molecular Biology, Department of Neuroscience, Cell Biology and Anatomy, and Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, Texas, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA;
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.,Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
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Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
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47
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Serelli-Lee V, Ito K, Koibuchi A, Tanigawa T, Ueno T, Matsushima N, Imai Y. A State-of-the-Art Roadmap for Biomarker-Driven Drug Development in the Era of Personalized Therapies. J Pers Med 2022; 12:jpm12050669. [PMID: 35629092 PMCID: PMC9143954 DOI: 10.3390/jpm12050669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/30/2022] [Accepted: 04/15/2022] [Indexed: 02/05/2023] Open
Abstract
Advances in biotechnology have enabled us to assay human tissue and cells to a depth and resolution that was never possible before, redefining what we know as the “biomarker”, and how we define a “disease”. This comes along with the shift of focus from a “one-drug-fits-all” to a “personalized approach”, placing the drug development industry in a highly dynamic landscape, having to navigate such disruptive trends. In response to this, innovative clinical trial designs have been key in realizing biomarker-driven drug development. Regulatory approvals of cancer genome sequencing panels and associated targeted therapies has brought personalized medicines to the clinic. Increasing availability of sophisticated biotechnologies such as next-generation sequencing (NGS) has also led to a massive outflux of real-world genomic data. This review summarizes the current state of biomarker-driven drug development and highlights examples showing the utility and importance of the application of real-world data in the process. We also propose that all stakeholders in drug development should (1) be conscious of and efficiently utilize real-world evidence and (2) re-vamp the way the industry approaches drug development in this era of personalized medicines.
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Affiliation(s)
- Victoria Serelli-Lee
- Clinical Evaluation Sub-Committee, Medicinal Evaluation Committee, Japan Pharmaceuticals Manufacturers Association, 2-3-11, Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan; (A.K.); (T.T.); (T.U.); (N.M.)
- Eli Lilly Japan K.K., 5-1-28 Isogamidori, Chuo-ku, Kobe 651-0086, Japan
- Correspondence: (V.S.-L.); (Y.I.)
| | - Kazumi Ito
- Clinical Evaluation Sub-Committee, Medicinal Evaluation Committee, Japan Pharmaceuticals Manufacturers Association, 2-3-11, Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan; (A.K.); (T.T.); (T.U.); (N.M.)
- Daiichi Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa-ku, Tokyo 140-8710, Japan;
| | - Akira Koibuchi
- Clinical Evaluation Sub-Committee, Medicinal Evaluation Committee, Japan Pharmaceuticals Manufacturers Association, 2-3-11, Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan; (A.K.); (T.T.); (T.U.); (N.M.)
- Astellas Pharma Inc., 2-5-1 Nihonbashi-Honcho, Chuo-ku, Tokyo 103-8411, Japan
| | - Takahiko Tanigawa
- Clinical Evaluation Sub-Committee, Medicinal Evaluation Committee, Japan Pharmaceuticals Manufacturers Association, 2-3-11, Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan; (A.K.); (T.T.); (T.U.); (N.M.)
- Bayer Yakuhin Ltd., 2-4-9, Umeda, Kita-ku, Osaka 530-0001, Japan
| | - Takayo Ueno
- Clinical Evaluation Sub-Committee, Medicinal Evaluation Committee, Japan Pharmaceuticals Manufacturers Association, 2-3-11, Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan; (A.K.); (T.T.); (T.U.); (N.M.)
- Bristol Myers Squibb K.K., 6-5-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo 163-1334, Japan
| | - Nobuko Matsushima
- Clinical Evaluation Sub-Committee, Medicinal Evaluation Committee, Japan Pharmaceuticals Manufacturers Association, 2-3-11, Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan; (A.K.); (T.T.); (T.U.); (N.M.)
- Janssen Pharmaceutical K.K., 3-5-2, Nishikanda, Chiyoda-ku, Tokyo 101-0065, Japan
| | - Yasuhiko Imai
- Clinical Evaluation Sub-Committee, Medicinal Evaluation Committee, Japan Pharmaceuticals Manufacturers Association, 2-3-11, Nihonbashi Honcho, Chuo-ku, Tokyo 103-0023, Japan; (A.K.); (T.T.); (T.U.); (N.M.)
- Bristol Myers Squibb K.K., 6-5-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo 163-1334, Japan
- Correspondence: (V.S.-L.); (Y.I.)
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Ayensa-Jiménez J, Doweidar MH, Sanz-Herrera JA, Doblare M. Understanding glioblastoma invasion using physically-guided neural networks with internal variables. PLoS Comput Biol 2022; 18:e1010019. [PMID: 35377875 PMCID: PMC9009781 DOI: 10.1371/journal.pcbi.1010019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 04/14/2022] [Accepted: 03/15/2022] [Indexed: 11/18/2022] Open
Abstract
Microfluidic capacities for both recreating and monitoring cell cultures have opened the door to the use of Data Science and Machine Learning tools for understanding and simulating tumor evolution under controlled conditions. In this work, we show how these techniques could be applied to study Glioblastoma, the deadliest and most frequent primary brain tumor. In particular, we study Glioblastoma invasion using the recent concept of Physically-Guided Neural Networks with Internal Variables (PGNNIV), able to combine data obtained from microfluidic devices and some physical knowledge governing the tumor evolution. The physics is introduced in the network structure by means of a nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution. On the other hand, multilayer perceptrons combined with a nodal deconvolution technique are used for learning the go or grow metabolic behavior which characterises the Glioblastoma invasion. The PGNNIV is here trained using synthetic data obtained from in silico tests created under different oxygenation conditions, using a previously validated model. The unravelling capacity of PGNNIV enables discovering complex metabolic processes in a non-parametric way, thus giving explanatory capacity to the networks, and, as a consequence, surpassing the predictive power of any parametric approach and for any kind of stimulus. Besides, the possibility of working, for a particular tumor, with different boundary and initial conditions, permits the use of PGNNIV for defining virtual therapies and for drug design, thus making the first steps towards in silico personalised medicine. In this work, we apply Physically-Guided Neural Networks with Internal Variables (PGNNIV) to the understanding of the Glioblastoma evolution process. We explain the metabolic changes between the proliferative and migrative activity of Glioblastoma cell cultures by using the go or grow activation functions as a pair of internal variables, whose dependence on the oxygen level is unravelled by some building blocks of the whole PGNNIV. Due to its model-free nature, our method is able to identify different classical mechanistic approaches and to outperform cell culture evolution predictions, as we demonstrate in the paper. Unlike Biologically-Informed Neural Networks we can assimilate data obtained from different boundary conditions and under different external stimuli to simulate the tumor progression under arbitrary conditions. We demonstrate this ability by comparing the predictions with different boundary conditions, resulting in different oxygenation conditions. This flexibility enables the use of our proposed method for personalised medical purposes, as the cell culture metabolic information, for a particular tumor, is encapsulated in a sub-network and may be used for arbitrary in silico tests.
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Affiliation(s)
- Jacobo Ayensa-Jiménez
- Mechanical Engineering Department, School of Engineering and Architecture, University of Zaragoza, Spain
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Spain
- Aragón Institute of Health Research (IIS Aragón), Spain
| | - Mohamed H. Doweidar
- Mechanical Engineering Department, School of Engineering and Architecture, University of Zaragoza, Spain
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Spain
| | - Jose A. Sanz-Herrera
- Mechanical Engineering Department, School of Engineering, University of Sevilla, Spain
| | - Manuel Doblare
- Mechanical Engineering Department, School of Engineering and Architecture, University of Zaragoza, Spain
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Spain
- Aragón Institute of Health Research (IIS Aragón), Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Spain
- * E-mail:
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49
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Kiryu Y. Medical Big Data Analysis Using Machine Learning Algorithms in the Field of Clinical Pharmacy. YAKUGAKU ZASSHI 2022; 142:319-326. [DOI: 10.1248/yakushi.21-00178-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Yoshihiro Kiryu
- Department of Pharmacy, M&B Collaboration Medical corporation Hokuetsu Hospital
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50
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Li J, Bzdok D, Chen J, Tam A, Ooi LQR, Holmes AJ, Ge T, Patil KR, Jabbi M, Eickhoff SB, Yeo BTT, Genon S. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. SCIENCE ADVANCES 2022; 8:eabj1812. [PMID: 35294251 PMCID: PMC8926333 DOI: 10.1126/sciadv.abj1812] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.
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Affiliation(s)
- Jingwei Li
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal
Neurological Institute (MNI), McConnell Brain Imaging Institute (BIC), McGill
University, Montreal, QC, Canada
- Mila-Quebec Artificial Intelligence Institute,
Montreal, QC, Canada
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Avram J. Holmes
- Departments of Psychology and Psychiatry, Yale
University, New Haven, CT, USA
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
- Stanley Center for Psychiatric Research, Broad
Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mbemba Jabbi
- Department of Psychiatry, Dell Medical School,
University of Texas at Austin, Austin, TX, USA
- The Mulva Clinic for Neurosciences, Dell Medical
School, University of Texas at Austin, Austin, TX, USA
- Institute of Neuroscience, University of Texas at
Austin, Austin, TX, USA
- Department of Psychology, University of Texas at
Austin, Austin, TX, USA
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme
(ISEP), National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
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