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Predictive models f...
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Eloranta, SandraKarolinska Institutet
(author)
Predictive models for clinical decision making : Deep dives in practical machine learning
- Article/chapterEnglish2022
Publisher, publication year, extent ...
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2022-04-25
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Wiley,2022
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printrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:kth-323269
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-323269URI
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https://doi.org/10.1111/joim.13483DOI
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http://kipublications.ki.se/Default.aspx?queryparsed=id:149350074URI
Supplementary language notes
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Language:English
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Summary in:English
Part of subdatabase
Classification
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
Notes
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QC 20230124
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The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted a review of the recent literature that explains standard taxonomies, terminology and central concepts to a broad clinical readership. Articles aimed at readers with little or no prior experience of commonly used methods or typical workflows were summarised and key references are highlighted. Continual interdisciplinary developments in data science, biostatistics and epidemiology also motivated us to further discuss emerging topics in predictive and data-driven (hypothesis-less) analytics with machine learning. Through two methodological deep dives using examples from precision psychiatry and outcome prediction after lymphoma, we highlight how the use of, for example, natural language processing can outperform established clinical risk scores and aid dynamic prediction and adaptive care strategies. Such realistic and detailed examples allow for critical analysis of the importance of new technological advances in artificial intelligence for clinical decision-making. New clinical decision support systems can assist in prevention and care by leveraging precision medicine.
Subject headings and genre
Added entries (persons, corporate bodies, meetings, titles ...)
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Boman, MagnusKarolinska Institutet,KTH,Programvaruteknik och datorsystem, SCS,Department of Learning, Informatics, Management, and Ethics, Karolinska Institutet, Stockholm, Sweden(Swepub:kth)u1tnstr8
(author)
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Karolinska InstitutetProgramvaruteknik och datorsystem, SCS
(creator_code:org_t)
Related titles
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In:Journal of Internal Medicine: Wiley292:2, s. 278-2950954-68201365-2796
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