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Zhang L, Yang Z, Yin Y, Huang W, Yi T, Ping J, Liu L, Shen P, Sun Y, Lin H. Using big data to analyze the vaccination status of children with congenital heart disease in Yinzhou District, China. Hum Vaccin Immunother 2024; 20:2319967. [PMID: 38465660 PMCID: PMC10936686 DOI: 10.1080/21645515.2024.2319967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/14/2024] [Indexed: 03/12/2024] Open
Abstract
Congenital heart disease (CHD) represents a significant population warranting particular attention concerning vaccination coverage. To comprehend the vaccination status of CHD within Yinzhou District, Ningbo City, China, and to facilitate the formulation of preventive, control, and immunization strategies against vaccine-preventable diseases in children with congenital heart conditions. Using the China Yinzhou Electronic Health Record Study (CHERRY) database, we analyzed the vaccination coverage of children with CHD born between January 1, 2016 and September 20, 2021, and analyzed the influencing factors associated with the level of vaccination coverage. This study involved 762 children diagnosed with CHD at the age of 12 months, revealing that 86.74% of these children had received at least one dose of the National Immunization Program (NIP) vaccines. The coverage for non-NIP vaccines, such as the rotavirus vaccine, influenza vaccine, Influenza Haemophilus influenzae Type b (Hib) Conjugate Vaccine, 13-valent pneumococcal conjugate vaccine (PCV13), and inactivated enterovirus type 71 vaccine (EV71), stood at 27.30%, 7.74%, 63.25%, 33.76%, and 34.51%, respectively. The completion coverage for the entire vaccination schedule were 27.30%, 5.51%, 55.77%, 34.25%, and 25.59%, respectively. There was a statistically significant correlation between vaccination coverage in classification of diagnostic medical institutions and the types of diagnosed diseases. Compared to their typically developing counterparts, 12-month-old children afflicted with CHD exhibit a slightly diminished vaccination coverage, alongside a discernible inclination toward delayed vaccination. Notably, the determination to undergo vaccinations seems predominantly influenced by the classification of diagnostic medical institutions. In practical terms, proactive measures involving early diagnosis, comprehensive health assessments, and timely interventions ought to be implemented to enhance vaccination rates while prioritizing safety.
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Affiliation(s)
- Lin Zhang
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Ziliang Yang
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Yueqi Yin
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Wenzan Huang
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Tianfei Yi
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Jianming Ping
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Liya Liu
- Medical School, Department of Preventive Medicine, Ningbo University, Ningbo, Zhejiang, PR China
| | - Peng Shen
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Yexiang Sun
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Hongbo Lin
- Yinzhou District Center for Disease Control and Prevention, Ningbo, Zhejiang, China
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Dong W, Da Roza CC, Cheng D, Zhang D, Xiang Y, Seto WK, Wong WCW. Development and validation of HBV surveillance models using big data and machine learning. Ann Med 2024; 56:2314237. [PMID: 38340309 PMCID: PMC10860422 DOI: 10.1080/07853890.2024.2314237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND The construction of a robust healthcare information system is fundamental to enhancing countries' capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China's rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization's (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. METHODS Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital's Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. RESULTS The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients' physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). CONCLUSIONS Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264).
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Affiliation(s)
- Weinan Dong
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Cecilia Clara Da Roza
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Dandan Cheng
- Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Dahao Zhang
- Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yuling Xiang
- Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Wai Kay Seto
- Department of Medicine and State Key Laboratory of Liver Research, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - William C. W. Wong
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
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Kotikam G, Selvaraj L. Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques. Network 2024; 35:154-189. [PMID: 38155542 DOI: 10.1080/0954898x.2023.2293895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/06/2023] [Indexed: 12/30/2023]
Abstract
The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.
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Affiliation(s)
- Gnanendra Kotikam
- Research Scholar, Department of Information and Communication Engineering, Anna University, Chennai, India
| | - Lokesh Selvaraj
- Department of Computer Science and Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India
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Li X, Olatosi B, Zhang J. Harnessing Big Data to end HIV. AIDS Care 2024; 36:581-582. [PMID: 36585935 PMCID: PMC10310878 DOI: 10.1080/09540121.2022.2159000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Xiaoming Li
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Bankole Olatosi
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- Department of Health Service, Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Jiajia Zhang
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- Department of Biostatistics and Epidemiology, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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Qiao S, Li X, Olatosi B, Young SD. Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review. AIDS Care 2024; 36:583-603. [PMID: 34260325 DOI: 10.1080/09540121.2021.1948499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/22/2021] [Indexed: 01/07/2023]
Abstract
Propelled by the transformative power of modern information and communication technologies, digitalization of data, and the increasing affordability of high-performance computing, Big Data science has brought forth revolutionary advancement in many areas of business, industry, health, and medicine. The HIV research and care service community is no exception to the benefits from the availability and utilization of Big Data analytics. Electronic health record (EHR) data (e.g., administrative and billing data, electronic medical records, or other digital records of information pertinent to individual or population health) are an essential source of health and disease outcome data because of the large amount of real-world, comprehensive, and often longitudinal data, which provide a good opportunity for leveraging advanced Big Data analytics in addressing challenges in HIV prevention, treatment, and care. This review focuses on studies that apply Big Data analytics to EHR data with aims to synthesize the HIV-related issues that EHR data studies can tackle, identify challenges in the utilization of EHR data in HIV research and practice, and discuss future needs and directions that can realize the promising potential role of Big Data in ending the HIV epidemic.
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Affiliation(s)
- Shan Qiao
- South Carolina SmartState Center for Healthcare Quality (CHQ), Columbia, SC, USA
- University of South Carolina Big Data Health Science Center, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Xiaoming Li
- South Carolina SmartState Center for Healthcare Quality (CHQ), Columbia, SC, USA
- University of South Carolina Big Data Health Science Center, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Bankole Olatosi
- South Carolina SmartState Center for Healthcare Quality (CHQ), Columbia, SC, USA
- University of South Carolina Big Data Health Science Center, Columbia, SC, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Sean D Young
- Department of Emergency Medicine, Department of Informatics, Institute for Prediction Technology, University of California, Irvine, CA, USA
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Gandaglia G, Pellegrino F, Golozar A, De Meulder B, Abbott T, Achtman A, Imran Omar M, Alshammari T, Areia C, Asiimwe A, Beyer K, Bjartell A, Campi R, Cornford P, Falconer T, Feng Q, Gong M, Herrera R, Hughes N, Hulsen T, Kinnaird A, Lai LYH, Maresca G, Mottet N, Oja M, Prinsen P, Reich C, Remmers S, Roobol MJ, Sakalis V, Seager S, Smith EJ, Snijder R, Steinbeisser C, Thurin NH, Hijazy A, van Bochove K, Van den Bergh RCN, Van Hemelrijck M, Willemse PP, Williams AE, Zounemat Kermani N, Evans-Axelsson S, Briganti A, N'Dow J. Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data. Eur Urol 2024; 85:457-465. [PMID: 37414703 DOI: 10.1016/j.eururo.2023.06.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/18/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. OBJECTIVE To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. DESIGN, SETTING, AND PARTICIPANTS From an initial cohort of >100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. RESULTS AND LIMITATIONS The most common comorbidities were hypertension (35-73%), obesity (9.2-54%), and type 2 diabetes (11-28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12-25%) and emergency department visits (10-14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. CONCLUSIONS Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. PATIENT SUMMARY Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.
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Affiliation(s)
- Giorgio Gandaglia
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy.
| | - Francesco Pellegrino
- Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy
| | - Asieh Golozar
- Odysseus Data Services, New York, NY, USA; OHDSI Center, Northeastern University, Boston, MA, USA
| | | | | | | | - Muhammad Imran Omar
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Academic Urology Unit, University of Aberdeen, Scotland, UK
| | | | | | | | - Katharina Beyer
- Translational Oncology and Urology Research, King's College London, London, UK
| | - Anders Bjartell
- Department of Translational Medicine, Lund University, Lund, Sweden
| | - Riccardo Campi
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | | | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Qi Feng
- Astellas Pharma, Inc., Northbrook, IL, USA
| | - Mengchun Gong
- Nanfang Hospital, Southern Medical University, Guangzhou, China; DHC Technologies, Beijing, China
| | | | | | - Tim Hulsen
- Philips Research, Department of Hospital Services & Informatics, Eindhoven, The Netherlands
| | | | | | | | - Nicolas Mottet
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands
| | - Marek Oja
- Institute of Computer Science, University of Tartu, Tartu, Estonia; STACC, Tartu, Estonia
| | - Peter Prinsen
- Netherlands Comprehensive Cancer Organization, Eindhoven, The Netherlands
| | | | - Sebastiaan Remmers
- Erasmus University Medical Centre, Cancer Institute, Rotterdam, The Netherlands
| | - Monique J Roobol
- Erasmus University Medical Centre, Cancer Institute, Rotterdam, The Netherlands
| | - Vasileios Sakalis
- Department of Urology, General Hospital of Thessaloniki Agios Pavlos, Thessaloniki, Greece
| | | | - Emma J Smith
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands
| | | | | | - Nicolas H Thurin
- INSERM CIC-P 1401, Bordeaux PharmacoEpi, Université de Bordeaux, Bordeaux, France
| | | | | | | | | | - Peter-Paul Willemse
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Department of Urology, Cancer Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Andrew E Williams
- The Institute for Clinical Research and Health Policy Studies at Tufts Medical Center, Boston, MA, USA
| | | | | | - Alberto Briganti
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy
| | - James N'Dow
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Academic Urology Unit, University of Aberdeen, Scotland, UK
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Wang X, Zhang R, Zhao B, Yao Y, Zhao H, Zhu X. Medical knowledge graph completion via fusion of entity description and type information. Artif Intell Med 2024; 151:102848. [PMID: 38658132 DOI: 10.1016/j.artmed.2024.102848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 02/05/2024] [Accepted: 03/19/2024] [Indexed: 04/26/2024]
Abstract
Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely solely on triplet structural information, overlooking the rich information hidden within entity property sets, leading to bottlenecks in performance enhancement when dealing with the intricate relations of MKGs. Inspired by the semantic sensitivity and explicit type constraints unique to the medical domain, we propose BioBERT-based graph embedding model. This model represents an evolvable framework that integrates graph embedding, language embedding, and type information, thereby optimizing the utility of MKGs. Our study utilizes not only WordNet as a benchmark dataset but also incorporates MedicalKG to compare and corroborate the specificity of medical knowledge. Experimental results on these datasets indicate that the proposed fusion framework achieves state-of-art (SOTA) performance compared to other baselines. We believe that this incremental improvement provides promising insights for future medical knowledge graph completion endeavors.
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Affiliation(s)
- Xiaochen Wang
- Department of Information Management, Beijing Jiaotong University, Beijing, 100044, China
| | - Runtong Zhang
- Department of Information Management, Beijing Jiaotong University, Beijing, 100044, China.
| | - Butian Zhao
- Department of Information Management, Beijing Jiaotong University, Beijing, 100044, China
| | - Yuhan Yao
- Department of Finance, Imperial College London, SW7 2AZ, United Kingdom
| | - Hongmei Zhao
- Peking University People's Hospital, Beijing, 100044, China
| | - Xiaomin Zhu
- Department of Industrial Engineering, Beijing Jiaotong University, Beijing, 100044, China
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Villaizán-Vallelado M, Salvatori M, Carro B, Sanchez-Esguevillas AJ. Graph Neural Network contextual embedding for Deep Learning on tabular data. Neural Netw 2024; 173:106180. [PMID: 38447303 DOI: 10.1016/j.neunet.2024.106180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/29/2024] [Accepted: 02/13/2024] [Indexed: 03/08/2024]
Abstract
All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modeling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on seven public datasets, also achieving competitive results when compared to boosted-tree solutions.
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Affiliation(s)
- Mario Villaizán-Vallelado
- Artificial Intelligence Laboratory (AI-Lab), Telefonica I+D, Spain; Universidad de Valladolid, Valladolid, 47011, Spain.
| | - Matteo Salvatori
- Artificial Intelligence Laboratory (AI-Lab), Telefonica I+D, Spain.
| | - Belén Carro
- Universidad de Valladolid, Valladolid, 47011, Spain.
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Hou X, Tang Y. Big data empowerment: Digital transformation and governance of minority shareholders: Evidence from China. PLoS One 2024; 19:e0302268. [PMID: 38625977 PMCID: PMC11020940 DOI: 10.1371/journal.pone.0302268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/31/2024] [Indexed: 04/18/2024] Open
Abstract
Based on the analysis of data from listed enterprises in China between 2011 and 2022, we investigate the influence of digital transformation on the governance efficiency for minority shareholders. The results show that the extent of digital transformation exert a negative effect on the agency costs incurred from related-party transactions. The mechanism examination elucidates that digital transformation augments the governance efficiency for minority shareholders by boosting attendance at shareholders' meetings and enhancing the exit threat for minority shareholders. Subsequent analysis reveals that non-state-owned enterprises, compared to state-owned enterprises, exhibit a more pronounced effect in diminishing the second type of agency costs through digital transformation. Furthermore, the impact of digital transformation in curtailing agency costs is more significant in the eastern regions than central and western regions. The better the equity checks and balances in listed enterprises, the more effective digital transformation is in reducing agency costs. This study offers valuable insights for bolstering the governance capacity of minority shareholders in the context of digital transformation.
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Affiliation(s)
- Xinhao Hou
- Business School, University of International Business and Economics, Beijing, China
| | - Yao Tang
- Business School, University of International Business and Economics, Beijing, China
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Lui R. Deus Ex Machina? The Rise of Artificial Intelligence in Toxicology. Chem Res Toxicol 2024; 37:525-527. [PMID: 38506041 DOI: 10.1021/acs.chemrestox.4c00050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Artificial intelligence (AI) is rising rapidly, driven by big data, complex algorithms, and computing resources. Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting tool across all facets of toxicology.
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Affiliation(s)
- Raymond Lui
- Computational Pharmacology and Toxicology Laboratory, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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Kulkarni C, Quraishi A, Raparthi M, Shabaz M, Khan MA, Varma RA, Keshta I, Soni M, Byeon H. Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer. BMC Med Inform Decis Mak 2024; 24:92. [PMID: 38575951 PMCID: PMC10996111 DOI: 10.1186/s12911-024-02495-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024] Open
Abstract
Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.
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Affiliation(s)
- Chaitanya Kulkarni
- Department of Computer Engineering, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Pune, 413133, Maharashtra, India
| | - Aadam Quraishi
- M.D. Research, Intervention Treatment Institute, Houston, TX, USA
| | - Mohan Raparthi
- Software Engineer, Alphabet Life Science, Dallas, TX, 75063, USA
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India.
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Raj A Varma
- Symbiosis Law School (SLS), Symbiosis International (Deemed University) (SIU), Vimannagar, Pune, Maharashtra, India
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Dr D Y Patil Vidyapeeth, Dr. D. Y. Patil School of Science and Technology, Pune, 411033, India
| | - Haewon Byeon
- Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, Republic of Korea, 50834
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Fan S. Influencing factors and countermeasures on intelligent transformation and upgrading of logistics firms: A case study in China. PLoS One 2024; 19:e0297663. [PMID: 38573886 PMCID: PMC10994377 DOI: 10.1371/journal.pone.0297663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/09/2024] [Indexed: 04/06/2024] Open
Abstract
This study explores the influencing factors on intelligent transformation and upgrading of China's logistics firms under smart logistics, and designs the corresponding framework to guide the practice of firms. By analyzing the characteristics of smart logistics and the transformation and upgrading needs of traditional logistics, from the micro perspective of logistics firms, this paper constructs influencing factor index system of smart transformation and development from four dimensions: logistics technology innovation, logistics big data sharing, logistics management upgrading and logistics decision-making transformation. Logistics firms are divided into firms with medium scale and above and small and medium-sized firms according to their scale. Then EWIF-AHP model is proposed to measure the weight of index system and score the decision-making, so as to evaluate the impact of various influencing factors on transformation and development of logistics firms. The results show that, for logistics firms above medium scale, logistics technology innovation and logistics big data sharing have the most significant impact on transformation and development, followed by logistics management upgrading and logistics decision-making transformation. For small and medium-sized logistics firms, the biggest factor is the upgrading of logistics management, followed by the upgrading of logistics technology, which is almost as important as the influencing factors of the upgrading of logistics management, and followed by the sharing of logistics big data and the transformation of logistics decision-making. Therefore, corresponding countermeasures and suggestions for intelligent transformation of logistics firms have been put forward.
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Affiliation(s)
- Sixia Fan
- School of Business, Shanghai Dianji University, Shanghai, China
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13
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Ahmed R, Sharma R, Chahal CAA. Trends and Disparities Around Cardiovascular Mortality in Sarcoidosis: Does Big Data Have the Answers? J Am Heart Assoc 2024; 13:e034073. [PMID: 38533935 DOI: 10.1161/jaha.124.034073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 01/17/2024] [Indexed: 03/28/2024]
Affiliation(s)
- Raheel Ahmed
- Heart Division Royal Brompton Hospital, Guy's and St Thomas' NHS Trust London United Kingdom
- National Heart and Lung Institute, Imperial College London London United Kingdom
| | - Rakesh Sharma
- Heart Division Royal Brompton Hospital, Guy's and St Thomas' NHS Trust London United Kingdom
- National Heart and Lung Institute, Imperial College London London United Kingdom
| | - C Anwar A Chahal
- Department of Cardiology Barts Heart Centre London United Kingdom
- Department of Cardiovascular Medicine Mayo Clinic Rochester MN USA
- Center for Inherited Cardiovascular Diseases, Department of Cardiology WellSpan Health York PA USA
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14
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Wu DY, Vo DT, Seiler SJ. Opinion: Big Data Elements Key to Medical Imaging Machine Learning Tool Development. J Breast Imaging 2024; 6:217-219. [PMID: 38271153 DOI: 10.1093/jbi/wbad102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Indexed: 01/27/2024]
Affiliation(s)
- Dolly Y Wu
- UT Southwestern Medical Center, Volunteer Services, Dallas, TX, USA
| | - Dat T Vo
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Stephen J Seiler
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
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15
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Oh R, Woo SJ, Joo K. Whole genome sequencing for inherited retinal diseases in the Korean National Project of Bio Big Data. Graefes Arch Clin Exp Ophthalmol 2024; 262:1351-1359. [PMID: 37947821 DOI: 10.1007/s00417-023-06309-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/22/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023] Open
Abstract
PURPOSE This study aimed to analyze the genetic results of inherited retinal diseases (IRDs) and evaluate the diagnostic usefulness of whole genome sequencing (WGS) in the Korean National Project of Bio Big Data. METHODS As part of the Korean National Project of Bio Big Data, WGS was performed on 32 individuals with IRDs with no identified pathogenic variants through whole or targeted exome sequencing. RESULTS Individuals with retinitis pigmentosa (n = 23), cone dystrophy (n = 2), cone-rod dystrophy (n = 2), familial exudative vitreoretinopathy (n = 2), pigmented paravenous chorioretinal atrophy (n = 1), North Carolina macular dystrophy (n = 1), and bull's-eye macular dystrophy (n = 1) were included. WGS revealed genetic mutations in the IQCB1, PRPF31, USH2A, and GUCY2D genes in five cases (15.6%). Two large structural variations and an intronic variant were newly detected in three cases. Two individuals had biallelic missense mutations that were not identified in previous exome sequencing. CONCLUSION With WGS, the causative variants in 15.6% of unsolved IRDs from the Korean National Project of Bio Big Data were identified. Further research with a larger cohort might unveil the diagnostic usefulness of WGS in IRDs and other diseases.
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Affiliation(s)
- Richul Oh
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggido, Republic of Korea, 13620
| | - Se Joon Woo
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggido, Republic of Korea, 13620
| | - Kwangsic Joo
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggido, Republic of Korea, 13620.
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16
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Namasudra S, Dhamodharavadhani S, Rathipriya R, Crespo RG, Moparthi NR. Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data. Big Data 2024; 12:83-99. [PMID: 36827458 DOI: 10.1089/big.2022.0155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Big data is a combination of large structured, semistructured, and unstructured data collected from various sources that must be processed before using them in many analytical applications. Anomalies or inconsistencies in big data refer to the occurrences of some data that are in some way unusual and do not fit the general patterns. It is considered one of the major problems of big data. Data trust method (DTM) is a technique used to identify and replace anomaly or untrustworthy data using the interpolation method. This article discusses the DTM used for univariate time series (UTS) forecasting algorithms for big data, which is considered the preprocessing approach by using a neural network (NN) model. In this work, DTM is the combination of statistical-based untrustworthy data detection method and statistical-based untrustworthy data replacement method, and it is used to improve the forecast quality of UTS. In this study, an enhanced NN model has been proposed for big data that incorporates DTMs with the NN-based UTS forecasting model. The coefficient variance root mean squared error is utilized as the main characteristic indicator in the proposed work to choose the best UTS data for model development. The results show the effectiveness of the proposed method as it can improve the prediction process by determining and replacing the untrustworthy big data.
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Affiliation(s)
- Suyel Namasudra
- Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India
| | | | - R Rathipriya
- Department of Computer Science, Periyar University, Salem, India
| | - Ruben Gonzalez Crespo
- Department of Computer Science and Technology, International University of La Rioja (UNIR), Logroño, Spain
| | - Nageswara Rao Moparthi
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
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17
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Williams CJM, Seligmann JF. Big Data and Colorectal Cancer: the Revolution will be Personalised. Clin Oncol (R Coll Radiol) 2024; 36:206-210. [PMID: 38281865 DOI: 10.1016/j.clon.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Affiliation(s)
- C J M Williams
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - J F Seligmann
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
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18
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Janjua HM, Rogers M, Read M, Grimsley EA, Kuo PC. A of analytics and B of big data in healthcare research: Telling the tale of health outcomes research from the eyes of data. Am J Surg 2024; 230:105-107. [PMID: 38092643 DOI: 10.1016/j.amjsurg.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 11/22/2023] [Indexed: 03/22/2024]
Affiliation(s)
- Haroon M Janjua
- Department of Surgery, University of South Florida, Tampa, FL, USA; OnetoMap Analytics, University of South Florida, Tampa, FL, USA
| | - Michael Rogers
- Department of Surgery, University of South Florida, Tampa, FL, USA; OnetoMap Analytics, University of South Florida, Tampa, FL, USA
| | - Meagan Read
- Department of Surgery, University of South Florida, Tampa, FL, USA; OnetoMap Analytics, University of South Florida, Tampa, FL, USA
| | - Emily A Grimsley
- Department of Surgery, University of South Florida, Tampa, FL, USA; OnetoMap Analytics, University of South Florida, Tampa, FL, USA
| | - Paul C Kuo
- Department of Surgery, University of South Florida, Tampa, FL, USA; OnetoMap Analytics, University of South Florida, Tampa, FL, USA.
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19
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Shi S, Ming Y, Wu H, Zhi C, Yang L, Meng S, Si Y, Wang D, Fei B, Hu J. A Bionic Skin for Health Management: Excellent Breathability, In Situ Sensing, and Big Data Analysis. Adv Mater 2024; 36:e2306435. [PMID: 37607262 DOI: 10.1002/adma.202306435] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/11/2023] [Indexed: 08/24/2023]
Abstract
Developing an intelligent wearable system is of great significance to human health management. An ideal health-monitoring patch should possess key characteristics such as high air permeability, moisture-wicking function, high sensitivity, and a comfortable user experience. However, such a patch that encompasses all these functions is rarely reported. Herein, an intelligent bionic skin patch for health management is developed by integrating bionic structures, nano-welding technology, flexible circuit design, multifunctional sensing functions, and big data analysis using advanced electrospinning technology. By controlling the preparation of nanofibers and constructing bionic secondary structures, the resulting nanofiber membrane closely resembles human skin, exhibiting excellent air/moisture permeability, and one-side sweat-wicking properties. Additionally, the bionic patch is endowed with a high-precision signal acquisition capabilities for sweat metabolites, including glucose, lactic acid, and pH; skin temperature, skin impedance, and electromyographic signals can be precisely measured through the in situ sensing electrodes and flexible circuit design. The achieved intelligent bionic skin patch holds great potential for applications in health management systems and rehabilitation engineering management. The design of the smart bionic patch not only provides high practical value for health management but also has great theoretical value for the development of the new generation of wearable electronic devices.
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Affiliation(s)
- Shuo Shi
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Yang Ming
- School of Fashion and Textiles, The Hong Kong Polytechnic University, 999077, Hong Kong SAR, China
| | - Hanbai Wu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Chuanwei Zhi
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Liangtao Yang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, 518055, China
| | - Shuo Meng
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Yifan Si
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Dong Wang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
- College of Textile Science and Engineering, Key Laboratory of Eco-Textile Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Bin Fei
- School of Fashion and Textiles, The Hong Kong Polytechnic University, 999077, Hong Kong SAR, China
| | - Jinlian Hu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, 518057, P. R. China
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20
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Chappel JR, Kirkwood-Donelson KI, Reif DM, Baker ES. From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome. Anal Bioanal Chem 2024; 416:2189-2202. [PMID: 37875675 PMCID: PMC10954412 DOI: 10.1007/s00216-023-04991-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/01/2023] [Accepted: 10/05/2023] [Indexed: 10/26/2023]
Abstract
The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.
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Affiliation(s)
- Jessie R Chappel
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27606, USA
| | - Kaylie I Kirkwood-Donelson
- Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, 27709, USA
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, 27709, USA.
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA.
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21
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Aljofan M, Gaipov A. Drug discovery and development: the role of artificial intelligence in drug repurposing. Future Med Chem 2024; 16:583-585. [PMID: 38426289 DOI: 10.4155/fmc-2024-0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Affiliation(s)
- Mohamad Aljofan
- Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan
- Drug Discovery & Development Laboratory, Center for Life Sciences, National Laboratory, Astana, 010000, Kazakhstan
| | - Abduzhappar Gaipov
- Department of Medicine, School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan
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22
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Ijaz M, Anwar N, Safran M, Alfarhood S, Sadad T, Imran. Domain adaptive learning for multi realm sentiment classification on big data. PLoS One 2024; 19:e0297028. [PMID: 38557742 PMCID: PMC10984522 DOI: 10.1371/journal.pone.0297028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/25/2023] [Indexed: 04/04/2024] Open
Abstract
Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.
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Affiliation(s)
- Maha Ijaz
- Department of Computer Science Faculty of Computing and Information Technology University of Gujrat, Gujrat, Pakistan
| | - Naveed Anwar
- Department of Computer Science Faculty of Computing and Information Technology University of Gujrat, Gujrat, Pakistan
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Tariq Sadad
- Department of Computer Science, University of Engineering and Technology Mardan, Mardan, Pakistan
| | - Imran
- Department of Biomedical Engineering, Gachon University, Incheon, Republic of Korea
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23
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Farina L. [Patients torn to pieces.]. Recenti Prog Med 2024; 115:170-174. [PMID: 38526380 DOI: 10.1701/4246.42228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Dissecting bodies is a common practice in many cultures. But in "big data medicine", the art of dissecting the human body has become an obsession. Indeed, modern biotechnology allows us to see and measure the molecular components of every single cell. But how can we put this immense number of bits and pieces back together again and see the patient as a whole? The first turning point is that proposed by René Descartes, who, inspired by dreams and visions, conceived the idea of unifying all scientific disciplines through the pervasive application of mathematics. Descartes formulates four basic rules, the second (top-down method) and third (bottom-up method) of which become crucial in modern data analysis. An instructive case study considered here is that of pulmonary tuberculosis, where the Cartesian approach of decomposing problems into smaller and smaller "pieces" - from organism to organ and from cellular lesion to the microscopic level - has led to the cure of the disease through antibiotics. This success story inspired Paul Ehrlich who, with the concept of the "magic bullet", defined modern pharmacology. However, this paradigm is being challenged today by multifactorial diseases and big data medicine, where the enormous availability of clinical and molecular data must be integrated to arrive at a therapeutic decision. The Cartesian approach shows its limitations today, as witnessed by the similar difficulty in fields other than medicine, illustrated here by the case of choosing to produce a successful television series based on user profiling. The take-home message is that the amount of data collected does not automatically guarantee success but that, instead of being data-driven, a collective "human" overview and assessment is inevitable. That is, close collaboration between clinicians and data analysts, integrating expertise, is needed to address challenges in the diagnosis and treatment of complex diseases through imagination and not mere extrapolation.
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Affiliation(s)
- Lorenzo Farina
- Dipartimento di Ingegneria informatica, automatica e gestionale Antonio Ruberti, Sapienza Università di Roma
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24
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Roman-Urrestarazu A, van Kessel R. The Global Burden of Disease Epidemiology-When Big Data Impute the Nonexistent. JAMA Pediatr 2024; 178:331-332. [PMID: 38372992 DOI: 10.1001/jamapediatrics.2023.6507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This Viewpoint discusses concerns about the data quality of the Global Burden of Disease study with respect to incidence estimates of child and adolescent mental health disorders, such as autism and attention-deficit/hyperactivity disorder, in low- and middle-income countries.
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Affiliation(s)
- Andres Roman-Urrestarazu
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Robin van Kessel
- Department of Health Policy, LSE Health, London School of Economics and Political Science, London, United Kingdom
- Department of International Health, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, the Netherlands
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25
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Jain S, Krumholz HM. Patient Privacy and Data Provenance in Pulmonary and Critical Care Research Using Big Data. Ann Am Thorac Soc 2024; 21:538-540. [PMID: 38259228 PMCID: PMC10995548 DOI: 10.1513/annalsats.202305-497ip] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/22/2024] [Indexed: 01/24/2024] Open
Affiliation(s)
- Snigdha Jain
- Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut; and
| | - Harlan M. Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
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26
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Pu X, Jiang R, Song Z, Liang Z, Yang L. A medical big data access control model based on smart contracts and risk in the blockchain environment. Front Public Health 2024; 12:1358184. [PMID: 38605878 PMCID: PMC11007037 DOI: 10.3389/fpubh.2024.1358184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/04/2024] [Indexed: 04/13/2024] Open
Abstract
The rapid development of the Hospital Information System has significantly enhanced the convenience of medical research and the management of medical information. However, the internal misuse and privacy leakage of medical big data are critical issues that need to be addressed in the process of medical research and information management. Access control serves as a method to prevent data misuse and privacy leakage. Nevertheless, traditional access control methods, limited by their single usage scenario and susceptibility to single point failures, fail to adapt to the polymorphic, real-time, and sensitive characteristics of medical big data scenarios. This paper proposes a smart contracts and risk-based access control model (SCR-BAC). This model integrates smart contracts with traditional risk-based access control and deploys risk-based access control policies in the form of smart contracts into the blockchain, thereby ensuring the protection of medical data. The model categorizes risk into historical and current risk, quantifies the historical risk based on the time decay factor and the doctor's historical behavior, and updates the doctor's composite risk value in real time. The access control policy, based on the comprehensive risk, is deployed into the blockchain in the form of a smart contract. The distributed nature of the blockchain is utilized to automatically enforce access control, thereby resolving the issue of single point failures. Simulation experiments demonstrate that the access control model proposed in this paper effectively curbs the access behavior of malicious doctors to a certain extent and imposes a limiting effect on the internal abuse and privacy leakage of medical big data.
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Affiliation(s)
- Xuetao Pu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, China
| | - Rong Jiang
- Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, China
- Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming, China
| | - Zhiming Song
- Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, China
- Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming, China
| | - Zhihong Liang
- Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, China
| | - Liang Yang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, China
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27
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Rodemund N, Wernly B, Jung C, Cozowicz C, Koköfer A. Harnessing Big Data in Critical Care: Exploring a new European Dataset. Sci Data 2024; 11:320. [PMID: 38548745 PMCID: PMC10978926 DOI: 10.1038/s41597-024-03164-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 03/01/2024] [Indexed: 04/01/2024] Open
Abstract
Freely available datasets have become an invaluable tool to propel data-driven research, especially in the field of critical care medicine. However, the number of datasets available is limited. This leads to the repeated reuse of datasets, inherently increasing the risk of selection bias. Additionally, the need arose to validate insights derived from one dataset with another. In 2023, the Salzburg Intensive Care database (SICdb) was introduced. SICdb offers insights in currently 27,386 intensive care admissions from 21,583 patients. It contains cases of general and surgical intensive care from all disciplines. Amongst others SICdb contains information about: diagnosis, therapies (including data on preceding surgeries), scoring, laboratory values, respiratory and vital signals, and configuration data. Data for SICdb (1.0.6) was collected at one single tertiary care institution of the Department of Anesthesiology and Intensive Care Medicine at the Salzburger Landesklinik (SALK) and Paracelsus Medical University (PMU) between 2013 and 2021. This article aims to elucidate on the characteristics of the dataset, the technical implementation, and provides analysis of its strengths and limitations.
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Affiliation(s)
- Niklas Rodemund
- Department of Anesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Bernhard Wernly
- Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University Salzburg, Oberndorf, Austria
- Center for Public Health and Healthcare Research, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Christian Jung
- Division of Cardiology, Pulmonary Diseases, Vascular Medicine Medical Faculty, University Dusseldorf, University Hospital Dusseldorf, Dusseldorf, Germany
| | - Crispiana Cozowicz
- Department of Anesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Andreas Koköfer
- Department of Anesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Lu S, Yang J, Gu Y, He D, Wu H, Sun W, Xu D, Li C, Guo C. Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors. ACS Sens 2024; 9:1134-1148. [PMID: 38363978 DOI: 10.1021/acssensors.3c02670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.
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Affiliation(s)
- Shasha Lu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Jianyu Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Yu Gu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Dongyuan He
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Haocheng Wu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Wei Sun
- College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Changming Li
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Chunxian Guo
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
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Cai J, Yang J, Liu M, Fang W, Ma Z, Bi J. Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation. Environ Sci Technol 2024; 58:4617-4626. [PMID: 38419288 DOI: 10.1021/acs.est.3c03145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.
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Affiliation(s)
- Jiacong Cai
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jianxun Yang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
| | - Miaomiao Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
| | - Wen Fang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
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Steiner M, Huettmann F, Bryans N, Barker B. With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots. Sci Rep 2024; 14:5204. [PMID: 38433273 PMCID: PMC10909860 DOI: 10.1038/s41598-024-55173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm ('Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.
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Affiliation(s)
- Moriz Steiner
- IUCN Small Mammal Specialist Group (SMSG), IUCN, Rue Mauverney 28, 1196, Gland, Switzerland.
- IUCN Species Survival Commission (SSC), IUCN, Rue Mauverney 28, 1196, Gland, Switzerland.
- EWHALE Lab-Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska Fairbanks (UAF), Fairbanks, AK, USA.
| | - F Huettmann
- EWHALE Lab-Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska Fairbanks (UAF), Fairbanks, AK, USA
| | - N Bryans
- Oracle for Research, 2300 Oracle Wy, Austin, TX, 78741, USA
| | - B Barker
- Oracle for Research, 2300 Oracle Wy, Austin, TX, 78741, USA
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Gonyo SB, Burkart H, Regan S. Leveraging big data for outdoor recreation management: A case study from the York river in Virginia. J Environ Manage 2024; 354:120482. [PMID: 38402789 DOI: 10.1016/j.jenvman.2024.120482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024]
Abstract
Outdoor recreation is important for improving quality of life, well-being, and local economies, but quantifying its value without direct monetary transactions can be challenging. This study explores combining non-market valuation techniques with emerging big data sources to estimate the value of recreation for the York River and surrounding parks in Virginia. By applying the travel cost method to anonymous human mobility data, we gain deeper insights into the significance of recreational experiences for visitors and the local economy. Results of a zero-inflated Negative Binomial model show a mean consumer surplus value of $26.91 per trip, totaling $15.5 million across nearly 600,000 trips observed in 2022. Further, weekends, holidays, and the summer and fall months are found to be peak visitation times, whereas those with young children and who are Hispanic or over 64 years old are less likely to visit. These findings shed light on various factors influencing visitation patterns and recreation values, including temporal effects and socio-demographics, revealing disparities that warrant targeted efforts for inclusivity and accessibility. Policymakers can use these insights to make informed and sustainable choices in outdoor recreation management, fostering the preservation of natural resources for the benefit of both visitors and the environment.
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Affiliation(s)
- Sarah Ball Gonyo
- Marine Spatial Ecology Division, National Centers for Coastal Ocean Science, National Oceanic and Atmospheric Administration, 1305 East-West Highway, Silver Spring, MD 20910, USA.
| | - Heidi Burkart
- CSS-Inc., Under NOAA National Centers for Coastal Ocean Science Contract No. EA133C-14-NC-1384, 2750 Prosperity Ave STE 220, Fairfax, VA 22031, USA
| | - Seann Regan
- CSS-Inc., Under NOAA National Centers for Coastal Ocean Science Contract No. EA133C-14-NC-1384, 2750 Prosperity Ave STE 220, Fairfax, VA 22031, USA
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Coombs A, Ong S. Four change-makers seek impact in medical research. Nature 2024; 627:S8-S10. [PMID: 38480969 DOI: 10.1038/d41586-024-00754-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
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Kohjitani H, Koshimizu H, Nakamura K, Okuno Y. Recent developments in machine learning modeling methods for hypertension treatment. Hypertens Res 2024; 47:700-707. [PMID: 38216731 DOI: 10.1038/s41440-023-01547-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/22/2023] [Accepted: 11/09/2023] [Indexed: 01/14/2024]
Abstract
Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalization of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clarified. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to efficiently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.
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Affiliation(s)
- Hirohiko Kohjitani
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Hiroshi Koshimizu
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuki Nakamura
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
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Li X, Tian Y, Li S, Dai Y, Chen Y, Li L. Optimization analysis of surgical lumen instrument cleaning management path under the background of medical big data. Minerva Gastroenterol (Torino) 2024; 70:133-135. [PMID: 37477170 DOI: 10.23736/s2724-5985.23.03452-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Affiliation(s)
- Xiaohua Li
- Sterilization and Supply Center, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui, China
| | - Yuquan Tian
- Operating Room, Shandong Provincial Third Hospital, Jinan, Shandong, China
| | - Suting Li
- Teaching and Research Office, Binzhou Polytechnic Department of Internal Medicine, Binzhou, Shandong, China
| | - Ying Dai
- Sterilization and Supply Center, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui, China
| | - Yufeng Chen
- Sterilization and Supply Center, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui, China
| | - Li Li
- Sterilization and Supply Center, The Third People's Hospital of Liaocheng City, Liaocheng, Shandong, China -
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Tanuseputro P, Webber C, Downar J. Illness trajectories in the age of big data. BMJ 2024; 384:q510. [PMID: 38428967 DOI: 10.1136/bmj.q510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Affiliation(s)
| | - Colleen Webber
- Investigator, Bruyere Research Institute, Ottawa ON, Canada
- Ottawa Hospital Research Institute, Ottawa ON, Canada
| | - James Downar
- Investigator, Bruyere Research Institute, Ottawa ON, Canada
- Ottawa Hospital Research Institute, Ottawa ON, Canada
- Department of Medicine, University of Ottawa, Ottawa ON, Canada
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37
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Wang CH, Lv CY, Lin YF, Zhang WH, Tang XL, Zhao LX. Effect of Esketamine on perioperative anxiety and depression in women with systemic tumors based on big data medical background. Eur Rev Med Pharmacol Sci 2024; 28:1797-1811. [PMID: 38497863 DOI: 10.26355/eurrev_202403_35594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
OBJECTIVE Perioperative anxiety and depression syndrome (PADS) is a common clinical concern among women with systemic tumors. Esketamine has been considered for its potential to alleviate anxiety and depressive symptoms. However, its specific application and effectiveness in PADS among women with systemic tumors remain unclear. This study aimed to analyze the utility of Machine Learning (ML) algorithms based on electroencephalogram (EEG) signals in evaluating perioperative anxiety and depression in women with systemic tumors treated with Esketamine, utilizing a large-scale medical data background. PATIENTS AND METHODS A single-center, randomized, placebo-controlled (SC-RPC) trial design was adopted. A total of 112 female patients with systemic tumors and PADS who received Esketamine treatment were included as study participants. A moderate dose (0.7 mg/kg) of Esketamine was administered through intravenous infusion over a duration of 60 minutes. EEG signals were collected from all patients, and the EEG signal features of individuals with depression were compared to those without depression. In this study, a Support Vector Machine (SVM)-K-Nearest Neighbour (KNN) hybrid classifier was constructed based on SVM and KNN algorithms. Using the EEG signals, the classifier was utilized to assess the anxiety and depression status of the patients. The predictive performance of the classifier was evaluated using accuracy, sensitivity, and specificity measures. RESULTS The C2 correntropy feature of the delta rhythm in the left-brain EEG signal was significantly higher in individuals with depression compared to those without depression (p<0.05). Moreover, the C2 correntropy feature of the Alpha, Beta, and Gamma rhythms in the left-brain EEG signal was significantly lower in individuals with depression compared to those without depression (p<0.05). In the right brain EEG signal, the C2 correntropy feature of the delta rhythm was significantly higher in individuals with depression (p<0.05), while the C2 correntropy feature of the alpha and gamma rhythms was significantly lower in individuals with depression compared to those without depression (p<0.05). Additionally, the C1 correntropy feature of the Gamma rhythm in the right brain EEG signal was significantly higher in individuals with depression compared to those without depression (p<0.05). The SVM classifier achieved accuracy, sensitivity, and specificity of 98.23%, 98.10%, and 98.56%, respectively, in recognizing the left-brain EEG signals, with a correlation coefficient of 0.95. In recognizing the right brain EEG signals, the SVM classifier achieved accuracy, sensitivity, and specificity of 98.74%, 98.43%, and 99.03%, respectively, with a correlation coefficient of 0.96. The improved SVM-KNN approach yielded an accuracy, recall, precision, F-score, area over the curve (AOC), and Receiver Operation Characteristics (ROC) of 0.829, 0.811, 0.791, 0.853, 0.787, and 0.877, respectively, in predicting anxiety. For predicting depression, the accuracy, recall, precision, F-score, AOC, and ROC were 0.869, 0.842, 0.831, 0.893, 0.827, and 0.917, respectively. CONCLUSIONS Significant differences were observed in the brain EEG signals between individuals with depression and those without depression. The improved SVM-KNN algorithm developed in this study demonstrates good predictive capability for anxiety and depression.
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Affiliation(s)
- C-H Wang
- Department of Anesthesiology, Yantaishan Hospital, Yantai, China.
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Huo L. Haze pollution and urban sprawl: An empirical analysis based on panel simultaneous equation model. PLoS One 2024; 19:e0296814. [PMID: 38421968 PMCID: PMC10903875 DOI: 10.1371/journal.pone.0296814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 12/19/2023] [Indexed: 03/02/2024] Open
Abstract
Based on the panel data of 227 prefecture-level and above cities in China from 2002 to 2018, a panel linkage equation model is constructed to explore the bidirectional influence relationship between haze pollution and urban sprawl, and the results of the study find that, firstly, there is a bidirectional promotion of causality between haze pollution and urban sprawl. That is, PM2.5 not only has a significant positive effect on urban sprawl, but also urban sprawl has a significant positive correlation with haze pollution, which is further strengthened by adding the air flow coefficient instrumental variable. Second, the heterogeneity analysis yields that haze pollution has different effects on urban sprawl in different regions. Under the sub-regional samples, haze pollution and urban sprawl have a bi-directional significant negative impact relationship in the eastern region, none of the haze pollution and urban sprawl have a bi-directional significant impact relationship in the western region, but both the central region and the northeastern region have a significant positive impact relationship. Under different city sizes, haze pollution and urban sprawl in large, medium and small cities have a bi-directional significant positive impact relationship, and from the numerical size, the degree of influence of haze pollution on urban sprawl in large cities is greater than that in small and medium cities; while the degree of influence of urban sprawl on haze pollution in medium cities is greater than that in large and small cities. Accordingly, it is proposed that urban governance should be adapted to local conditions, focus on innovative technologies to reduce energy consumption, and utilize big data to manage cities.
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Affiliation(s)
- Luping Huo
- College of Economics and Finance, Xi’an International Studies University, Xi’an, China
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Kim S, Heo S. An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture. Nat Commun 2024; 15:1561. [PMID: 38378798 PMCID: PMC10879191 DOI: 10.1038/s41467-024-45725-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 02/02/2024] [Indexed: 02/22/2024] Open
Abstract
A digital twin is a digital representation that closely resembles or replicates a real world object by combining interdisciplinary knowledge and advanced technologies. Digital twins have been applied to various fields, including to the agricultural field. Given big data and systematic data management, digital twins can be used for predicting future outcomes. In this study, we endeavor to create an agricultural digital twin using mandarins as a model crop. We employ an Open API to aggregate data from various sources across Jeju Island, covering an area of approximately 185,000 hectares. The collected data are visualized and analyzed at regional, inter-orchard, and intra-orchard scales. We observe that the intra-orchard analysis explains the variation of fruit quality substantially more than the inter-orchard analysis. Our data visualization and analysis, incorporating statistical models and machine learning algorithms, demonstrate the potential use of agricultural digital twins in the future, particularly in the context of micro-precision and individualized agriculture. This concept extends the current management practices based on data-driven decisions, and it offers a glimpse into the future of individualized agriculture by enabling customized treatment for plants, akin to personalized medicine for humans.
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Affiliation(s)
- Steven Kim
- Department of Mathematics and Statistics, California State University, Monterey Bay, Seaside, CA, 93955, USA.
| | - Seong Heo
- Department of Horticulture, Kongju National University, Yesan, 32439, Republic of Korea.
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Al-Ani A, Rayyan A, Maswadeh A, Sultan H, Alhammouri A, Asfour H, Alrawajih T, Al Sharie S, Al Karmi F, Al-Azzam AM, Mansour A, Al-Hussaini M. Evaluating the understanding of the ethical and moral challenges of Big Data and AI among Jordanian medical students, physicians in training, and senior practitioners: a cross-sectional study. BMC Med Ethics 2024; 25:18. [PMID: 38368332 PMCID: PMC10873950 DOI: 10.1186/s12910-024-01008-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/29/2024] [Indexed: 02/19/2024] Open
Abstract
AIMS To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners. METHODS We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants' responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA. RESULTS We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of "broad consent", respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI's abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare. CONCLUSION Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology.
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Affiliation(s)
- Abdallah Al-Ani
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Abdallah Rayyan
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Ahmad Maswadeh
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Hala Sultan
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | | | - Hadeel Asfour
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Tariq Alrawajih
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | | | - Fahed Al Karmi
- Faculty of Medicine, University of Jordan, Amman, Jordan
| | | | - Asem Mansour
- Office of Director General, King Hussein Cancer Center, Amman, Jordan
| | - Maysa Al-Hussaini
- Department of Pathology and Laboratory Medicine, King Hussein Cancer Center, 202 Queen Rania Street, Amman, 11941, Jordan.
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Jensen PM, Danielsen F, Jacobsen SK, Vikstrøm T. Fair concordance between Google Trends and Danish ornithologists in the assessment of temporal trends in Danish bird populations highlights the informational value of big data. Environ Monit Assess 2024; 196:276. [PMID: 38366261 PMCID: PMC10873222 DOI: 10.1007/s10661-024-12439-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/03/2024] [Indexed: 02/18/2024]
Abstract
The ongoing depletion of natural systems and associated biodiversity decline is of growing international concern. Climate change is expected to exacerbate anthropogenic impacts on wild populations. The scale of impact on ecosystems and ecosystem services will be determined by the impact on a multitude of species and functional groups, which due to their biology and numbers are difficult to monitor. The IPCC has argued that surveillance or monitoring is critical and proposed that monitoring systems should be developed, which not only track developments but also function as "early warning systems." Human populations are already generating large continuous datasets on multiple taxonomic groups through internet searches. These time series could in principle add substantially to current monitoring if they reflect true changes in the natural world. We here examined whether information on internet search frequencies delivered by the Danish population and captured by Google Trends (GT) appropriately informs on population trends in 106 common Danish bird species. We compared the internet search activity with independent equivalent population trend assessments from the Danish Ornithological Society (BirdLife Denmark/DOF). We find a fair concordance between the GT trends and the assessments by DOF. A substantial agreement can be obtained by omitting species without clear temporal trends. Our findings suggest that population trend proxies from internet search frequencies can be used to supplement existing wildlife population monitoring and to ask questions about an array of ecological phenomena, which potentially can be integrated into an early warning system for biodiversity under climate change.
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Affiliation(s)
- Per M Jensen
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg, Denmark.
| | | | - Stine K Jacobsen
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg, Denmark
| | - Thomas Vikstrøm
- DOF/BirdLife Denmark, Vesterbrogade 140A, 1620, Copenhagen V, Denmark
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He Y, Jiang Z, Zeng M, Cao S, Wu N, Liu X. Unraveling potential mechanism of different metal ions effect on anammox through big data analysis, molecular docking and molecular dynamics simulation. J Environ Manage 2024; 352:120092. [PMID: 38232596 DOI: 10.1016/j.jenvman.2024.120092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/27/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
Heavy metals (HMs) have been widely reported to pose an adverse effect on anaerobic ammonia oxidation (anammox) bacteria, yet the underlying mechanisms remain unclear. This study provides new insights into the potential mechanisms of interaction between HMs and functional enzymes through big date analysis, molecular docking and molecular dynamics simulation. The statistical analysis indicated that 10 mg/L Cu(II) and Cd(II) reduced nitrogen removal rate (NRR) by 85% and 43%, while 5 mg/L Fe(II) enhanced NRR by 29%. Additionally, the results of molecular simulations provided a microscopic interpretation for these macroscopic data. Molecular docking revealed that Hg(II) formed a distinctive binding site on ferritin, while other HMs resided at iron oxidation sites. Furthermore, HMs exhibited distinct binding sites on hydrazine dehydrogenase. Concurrently, the molecular dynamics simulation results further substantiated their capacity to form complexes. Cu(II) displayed the strongest binding affinity with ferritin for -1576 ± 79 kJ/mol in binding free energy calculation. Moreover, Cd(II) bound to ferritin and HDH for -1052.67 ± 58.49 kJ/mol, -290.02 ± 49.68 kJ/mol, respectively. This research addressed a crucial knowledge gap, shedding light on potential applications for remediating heavy metal-laden industrial wastewater.
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Affiliation(s)
- Yuhang He
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457 Tianjin, China
| | - Zhicheng Jiang
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457 Tianjin, China
| | - Ming Zeng
- College of Marine and Environmental Sciences, Tianjin University of Science & Technology, 300457 Tianjin, China.
| | - Shenbin Cao
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, China; College of Architecture and Civil Engineering, Faculty of Architecture, Civil and Transportation Engineering (FACTE), Beijing University of Technology, Beijing 100124, China.
| | - Nan Wu
- College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China
| | - Xinyuan Liu
- College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China
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Executive summary of the meeting of the 2023 ASHP Commission on Goals: Optimizing Medication Therapy Through Advanced Analytics and Data-Driven Healthcare. Am J Health Syst Pharm 2024; 81:159-64. [PMID: 37971063 DOI: 10.1093/ajhp/zxad261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
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Lun R, Siegal D, Ramsay T, Stotts G, Dowlatshahi D. Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. PLoS One 2024; 19:e0295921. [PMID: 38324588 PMCID: PMC10849264 DOI: 10.1371/journal.pone.0295921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 12/01/2023] [Indexed: 02/09/2024] Open
Abstract
OBJECTIVES Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke. DESIGN retrospective cohort study. SETTING We used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse). OUTCOME MEASURES We compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population. RESULTS Using synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, p<0.0001), and a higher prevalence of chronic obstructive pulmonary disease (COPD: 8.5% vs 4.7%, p<0.0001), prior ischemic stroke (1.7% vs 0.1%, p<0.001), and prior venous thromboembolism (VTE: 8.2% vs 1.5%, p<0.0001). They also had a longer length of stay (8 days [IQR 3-16] vs 6 days [IQR 3-13], p = 0.011), and higher costs associated with their stroke encounters: $11,498 (IQR $4,440 -$20,668) in the cancer cohort vs $8,084 (IQR $3,947 -$16,706) in the non-cancer cohort (p = 0.0061). A multivariable logistic regression model identified 5 predictors for recurrent ischemic stroke in the cancer cohort using synthetic data; 3 of the same predictors identified using real patient data with similar effect measures. Summary statistics between synthetic and original datasets did not significantly differ, other than slight differences in the distributions of frequencies for numeric data. CONCLUSION We demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion.
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Affiliation(s)
- Ronda Lun
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, Canada
| | - Deborah Siegal
- School of Epidemiology, University of Ottawa, Ottawa, Canada
- Division of Hematology, Department of Medicine, The Ottawa Hospital, Ottawa, Canada
| | - Tim Ramsay
- School of Epidemiology, University of Ottawa, Ottawa, Canada
| | - Grant Stotts
- Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, Canada
| | - Dar Dowlatshahi
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, Canada
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Zhu W, Han R, Shang X, Zhou T, Liang C, Qin X, Chen H, Feng Z, Zhang H, Fan X, Li W, Li L. The CropGPT project: Call for a global, coordinated effort in precision design breeding driven by AI using biological big data. Mol Plant 2024; 17:215-218. [PMID: 38140725 DOI: 10.1016/j.molp.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/24/2023] [Accepted: 12/20/2023] [Indexed: 12/24/2023]
Affiliation(s)
- Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Rui Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Tao Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Chengyong Liang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaomeng Qin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hong Chen
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zaiwen Feng
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xingming Fan
- Institute of Food Crops Sciences, Yunnan Academy of Agricultural Sciences, 2238 Beijing Road, Kunming, Yunnan 650200, China
| | - Weifu Li
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
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Yan X, Qi Y, Yao X, Zhou N, Ye X, Chen X. DNMT3L inhibits hepatocellular carcinoma progression through DNA methylation of CDO1: insights from big data to basic research. J Transl Med 2024; 22:128. [PMID: 38308276 PMCID: PMC10837993 DOI: 10.1186/s12967-024-04939-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND DNMT3L is a crucial DNA methylation regulatory factor, yet its function and mechanism in hepatocellular carcinoma (HCC) remain poorly understood. Bioinformatics-based big data analysis has increasingly gained significance in cancer research. Therefore, this study aims to elucidate the role of DNMT3L in HCC by integrating big data analysis with experimental validation. METHODS Dozens of HCC datasets were collected to analyze the expression of DNMT3L and its relationship with prognostic indicators, and were used for molecular regulatory relationship evaluation. The effects of DNMT3L on the malignant phenotypes of hepatoma cells were confirmed in vitro and in vivo. The regulatory mechanisms of DNMT3L were explored through MSP, western blot, and dual-luciferase assays. RESULTS DNMT3L was found to be downregulated in HCC tissues and associated with better prognosis. Overexpression of DNMT3L inhibits cell proliferation and metastasis. Additionally, CDO1 was identified as a target gene of DNMT3L and also exhibits anti-cancer effects. DNMT3L upregulates CDO1 expression by competitively inhibiting DNMT3A-mediated methylation of CDO1 promoter. CONCLUSIONS Our study revealed the role and epi-transcriptomic regulatory mechanism of DNMT3L in HCC, and underscored the essential role and applicability of big data analysis in elucidating complex biological processes.
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Affiliation(s)
- Xiaokai Yan
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
| | - Yao Qi
- Shanghai Molecular Medicine Engineering Technology Research Center, Shanghai, 201203, China
- Shanghai National Engineering Research Center of Biochip, Shanghai, 201203, China
| | - Xinyue Yao
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Nanjing Zhou
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xinxin Ye
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xing Chen
- Department of Hepatopancreatobiliary Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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Xue H, Zhang B, Rong J. Effect of extracorporeal shock wave therapy on carpal tunnel syndrome based on medical big data. Minerva Surg 2024; 79:115-117. [PMID: 34889568 DOI: 10.23736/s2724-5691.21.09290-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Hao Xue
- Department of Rehabilitation, Shanghai No.1 Rehabilitation Hospital, Shanghai, China
| | - Beicheng Zhang
- Department of Rehabilitation, Shanghai No.1 Rehabilitation Hospital, Shanghai, China
| | - Jifeng Rong
- Department of Rehabilitation, Shanghai No.1 Rehabilitation Hospital, Shanghai, China -
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Alizadeh M, Sampaio Moura N, Schledwitz A, Patil SA, El-Serag H, Ravel J, Raufman JP. A Practical Guide to Evaluating and Using Big Data in Digestive Disease Research. Gastroenterology 2024; 166:240-247. [PMID: 38052336 PMCID: PMC10872385 DOI: 10.1053/j.gastro.2023.11.292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 11/01/2023] [Accepted: 11/27/2023] [Indexed: 12/07/2023]
Affiliation(s)
- Madeline Alizadeh
- The Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Natalia Sampaio Moura
- Department of Medicine, Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Alyssa Schledwitz
- Department of Medicine, Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Seema A Patil
- Department of Medicine, Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Hashem El-Serag
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Jacques Ravel
- The Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Jean-Pierre Raufman
- Department of Medicine, Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, Maryland; VA Maryland Healthcare System, Baltimore, Maryland; Marlene and Stewart Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, Maryland; Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, Maryland.
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Mitelpunkt A, Stodola MA, Vargus-Adams J, Kurowski BG, Greve K, Bhatnagar S, Aronow B, Zahner J, Bailes AF. A big data approach to evaluate receipt of optimal care in childhood cerebral palsy. Disabil Rehabil 2024; 46:723-730. [PMID: 36755522 PMCID: PMC10406971 DOI: 10.1080/09638288.2023.2175919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 12/20/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023]
Abstract
PURPOSE Through automated electronic health record (EHR) data extraction and analysis, this project systematically quantified actual care delivery for children with cerebral palsy (CP) and evaluated alignment with current evidence-based recommendations. METHODS Utilizing EHR data for over 8000 children with CP, we developed an approach to define and quantify receipt of optimal care, and pursued proof-of-concept with two children with unilateral CP, Gross Motor Function Classification System (GMFCS) Level II. Optimal care was codified as a cluster of four components including physical medicine and rehabilitation (PMR) care, spasticity management, physical therapy (PT), and occupational therapy (OT). A Receipt of Care Score (ROCS) quantified the degree of adherence to recommendations and was compared with the Pediatric Outcomes Data Collection Instrument (PODCI) and Pediatric Quality of Life Inventory (PEDS QL). RESULTS The two children (12 year old female, 13 year old male) had nearly identical PMR and spasticity component scores while PT and OT scores were more divergent. Functional outcomes were higher for the child who had higher adjusted ROCS. CONCLUSIONS ROCSs demonstrate variation in real-world care delivered over time and differentiate between components of care. ROCSs reflect overall function and quality of life. The ROCS methods developed are novel, robust, and scalable and will be tested in a larger sample.IMPLICATIONS FOR REHABILITATIONOptimal practice, with an emphasis on integrated multidisciplinary care, can be defined and quantified utilizing evidence-based recommendations.Receipt of optimal care for childhood cerebral palsy can be scored using existing electronic health record data.Big Data approaches can contribute to the understanding of current care and inform approaches for improved care.
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Affiliation(s)
- Alexis Mitelpunkt
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Pediatric Rehabilitation, Department of Rehabilitation, Dana-Dwek Children’s Hospital, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | | | - Jilda Vargus-Adams
- Division of Pediatric Rehabilitation Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, USA
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Brad G. Kurowski
- Division of Pediatric Rehabilitation Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, USA
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kelly Greve
- Division of Occupational Therapy and Physical Therapy, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Rehabilitation, Exercise and Nutrition Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Surbhi Bhatnagar
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Bruce Aronow
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Janet Zahner
- Department of Information Services, University of Cincinnati, Cincinnati, OH, USA
| | - Amy F. Bailes
- Division of Occupational Therapy and Physical Therapy, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Rehabilitation, Exercise and Nutrition Sciences, University of Cincinnati, Cincinnati, OH, USA
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Liu P, Zeng B, Wu X, Zheng F, Zhang Y, Liao X. Risk exploration and prediction model construction for linezolid-resistant Enterococcus faecalis based on big data in a province in southern China. Eur J Clin Microbiol Infect Dis 2024; 43:259-268. [PMID: 38032514 PMCID: PMC10821975 DOI: 10.1007/s10096-023-04717-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/15/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Enterococcus faecalis is a common cause of healthcare-associated infections. Its resistance to linezolid, the antibiotic of last resort for vancomycin-resistant enterococci, has become a growing threat in healthcare settings. METHODS We analyzed the data of E. faecalis isolates from 26 medical institutions between 2018 and 2020 and performed univariate and multivariate logistic regression analyses to determine the independent predictors for linezolid-resistant E. faecalis (LREFs). Then, we used the artificial neural network (ANN) and logistic regression (LR) to build a prediction model for linezolid resistance and performed a performance evaluation and comparison. RESULTS Of 12,089 E. faecalis strains, 755 (6.25%) were resistant to linezolid. Among vancomycin-resistant E. faecalis, the linezolid-resistant rate was 24.44%, higher than that of vancomycin-susceptible E. faecalis (p < 0.0001). Univariate and multivariate regression analyses showed that gender, age, specimen type, length of stay before culture, season, region, GDP (gross domestic product), number of beds, and hospital level were predictors of linezolid resistance. Both the ANN and LR models constructed in the study performed well in predicting linezolid resistance in E. faecalis, with AUCs of 0.754 and 0.741 in the validation set, respectively. However, synthetic minority oversampling technique (SMOTE) did not improve the prediction ability of the models. CONCLUSION E. faecalis linezolid-resistant rates varied by specimen site, geographic region, GDP level, facility level, and the number of beds. At the same time, community-acquired E. faecalis with linezolid resistance should be monitored closely. We can use the prediction model to guide clinical medication and take timely prevention and control measures.
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Affiliation(s)
- Peijun Liu
- Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China
| | - Bangwei Zeng
- Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China.
| | - Xiaoyan Wu
- Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China
| | - Feng Zheng
- Information Department, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China
| | - Yangmei Zhang
- Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China
| | - Xiaohua Liao
- Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China
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