Predictive models for clinical decision making: Deep dives in practical machine learning

Predictive models for clinical decision making : Deep dives in practical machine learning

QC 20230124
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.

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
artificial intelligence
clinical decision-making
machine learning
physician
precision medicine
bioinformatics
biostatistics
clinical decision making
clinical decision support system
clinical outcome
clinical research
data science
human
learning algorithm
lymphoma
medical genetics
natural language processing
personalized medicine
prediction
predictive model
psychiatry
psychology
Review
risk assessment
statistical reasoning
survival analysis
workflow
procedures
Decision Support Systems
Clinical
Humans

Boman, MagnusKarolinska Institutet,KTH,Programvaruteknik och datorsystem, SCS,Department of Learning, Informatics, Management, and Ethics, Karolinska Institutet, Stockholm, Sweden(Swepub:kth)u1tnstr8 (författare)
Karolinska InstitutetProgramvaruteknik och datorsystem, SCS (creator_code:org_t)

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