{"title":"推进医疗保健分析：机器学习，健康信息学和现实世界数据应用的专题审查。","authors":"Maria I Arias, Lorena Cadavid, Juan D Velásquez","doi":"10.1016/j.jbi.2025.104934","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To map the conceptual and methodological landscape of healthcare analytics by identifying dominant thematic clusters, synthesizing key trends, and outlining translational challenges and research opportunities in the field.</p><p><strong>Methods: </strong>A total of 2,281 Scopus-indexed publications were analyzed using unsupervised text mining and clustering techniques. The analysis focused on identifying recurring themes, methodological innovations, and gaps within healthcare analytics literature across clinical, administrative, and public health contexts.</p><p><strong>Results: </strong>Eight dominant themes were identified: intelligent systems for predictive healthcare, patient-centered health analytics, adaptive AI for clinical insights, demographic health analytics, digital mental health surveillance, ethical analytics for health surveillance, personalized care through data analytics, and AI-driven insights for outbreak response. These reflect a transition toward real-time, multimodal, and ethically grounded analytics ecosystems. Persistent challenges include data interoperability, algorithmic opacity, standardization of evaluation, and demographic bias.</p><p><strong>Conclusions: </strong>The review highlights emerging priorities, including explainable AI, federated learning, and context-aware modeling, as well as ethical considerations related to data privacy and digital equity. Practical recommendations include co-designing with healthcare professionals, investing in infrastructure, and deploying real-time clinical decision support. Healthcare analytics is positioned as a foundational pillar of learning health systems with broad implications for translational research and precision health.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104934"},"PeriodicalIF":4.5000,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing healthcare analytics: a thematic review of machine learning, health informatics, and real-world data applications.\",\"authors\":\"Maria I Arias, Lorena Cadavid, Juan D Velásquez\",\"doi\":\"10.1016/j.jbi.2025.104934\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To map the conceptual and methodological landscape of healthcare analytics by identifying dominant thematic clusters, synthesizing key trends, and outlining translational challenges and research opportunities in the field.</p><p><strong>Methods: </strong>A total of 2,281 Scopus-indexed publications were analyzed using unsupervised text mining and clustering techniques. The analysis focused on identifying recurring themes, methodological innovations, and gaps within healthcare analytics literature across clinical, administrative, and public health contexts.</p><p><strong>Results: </strong>Eight dominant themes were identified: intelligent systems for predictive healthcare, patient-centered health analytics, adaptive AI for clinical insights, demographic health analytics, digital mental health surveillance, ethical analytics for health surveillance, personalized care through data analytics, and AI-driven insights for outbreak response. These reflect a transition toward real-time, multimodal, and ethically grounded analytics ecosystems. Persistent challenges include data interoperability, algorithmic opacity, standardization of evaluation, and demographic bias.</p><p><strong>Conclusions: </strong>The review highlights emerging priorities, including explainable AI, federated learning, and context-aware modeling, as well as ethical considerations related to data privacy and digital equity. Practical recommendations include co-designing with healthcare professionals, investing in infrastructure, and deploying real-time clinical decision support. Healthcare analytics is positioned as a foundational pillar of learning health systems with broad implications for translational research and precision health.</p>\",\"PeriodicalId\":15263,\"journal\":{\"name\":\"Journal of Biomedical Informatics\",\"volume\":\" \",\"pages\":\"104934\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biomedical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jbi.2025.104934\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomedical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jbi.2025.104934","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Advancing healthcare analytics: a thematic review of machine learning, health informatics, and real-world data applications.
Objective: To map the conceptual and methodological landscape of healthcare analytics by identifying dominant thematic clusters, synthesizing key trends, and outlining translational challenges and research opportunities in the field.
Methods: A total of 2,281 Scopus-indexed publications were analyzed using unsupervised text mining and clustering techniques. The analysis focused on identifying recurring themes, methodological innovations, and gaps within healthcare analytics literature across clinical, administrative, and public health contexts.
Results: Eight dominant themes were identified: intelligent systems for predictive healthcare, patient-centered health analytics, adaptive AI for clinical insights, demographic health analytics, digital mental health surveillance, ethical analytics for health surveillance, personalized care through data analytics, and AI-driven insights for outbreak response. These reflect a transition toward real-time, multimodal, and ethically grounded analytics ecosystems. Persistent challenges include data interoperability, algorithmic opacity, standardization of evaluation, and demographic bias.
Conclusions: The review highlights emerging priorities, including explainable AI, federated learning, and context-aware modeling, as well as ethical considerations related to data privacy and digital equity. Practical recommendations include co-designing with healthcare professionals, investing in infrastructure, and deploying real-time clinical decision support. Healthcare analytics is positioned as a foundational pillar of learning health systems with broad implications for translational research and precision health.
期刊介绍：
The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
