New horizons in prediction modelling using machine learning in older people's healthcare research.
Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in predictio...
| Published in: | Age & Ageing Vol. 53; no. 9; pp. 1 - 12 |
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| Format: | Article |
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Oxford University Press / USA
Sep2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=180016554&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 180016554 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Sep2024 vid: 53 iid: 9 pid: 622 pub: Oxford University Press / USA artinfo: ui: 180016554 10.1093/ageing/afae201 ppf: 1 ppct: 11 formats: tig: atl: New horizons in prediction modelling using machine learning in older people's healthcare research. aug: au: Stahl, Daniel affil: Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London , London, UK su: Old age Prediction models Database management Machine learning Individualized medicine Medical care for older people Algorithms sug: subj: Old age Data Processing, Hosting, and Related Services Prediction models Database management Machine learning Individualized medicine Medical care for older people Algorithms keyword: machine learning older adult older people precision medicine prediction modelling machine learning older adult older people precision medicine prediction modelling ab: Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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