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

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 le...

Full description

Saved in:
Bibliographic Details
Published in:Journal of internal medicine Vol. 292; no. 2; pp. 278 - 295
Main Authors: Eloranta, Sandra, Boman, Magnus
Format: Journal Article
Language:English
Published: England Blackwell Publishing Ltd 01.08.2022
Subjects:
Artificial intelligence
bioinformatics
biostatistics
Clinical
Clinical decision making
clinical decision support system
clinical outcome
clinical research
Data science
Decision making
Decision Support Systems
Epidemiology
human
Humans
learning algorithm
Learning algorithms
Literature reviews
Lymphoma
Machine learning
medical genetics
Natural language processing
personalized medicine
physician
Precision medicine
prediction
Prediction models
predictive model
procedures
Psychiatry
psychology
Review
risk assessment
statistical reasoning
survival analysis
Taxonomy
Terminology
workflow
precision medicine
physician
clinical decision-making
machine learning
artificial intelligence
ISSN:0954-6820, 1365-2796, 1365-2796
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.   
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:0954-6820
1365-2796
1365-2796
DOI:10.1111/joim.13483