Machine Learning in Epidemiology and Health Outcomes Research.
Annu Rev Public Health
; 41: 21-36, 2020 04 02.
Article
in En
| MEDLINE
| ID: mdl-31577910
ABSTRACT
Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-to-end approach to machine learning can be a daunting task. We take the reader through each step in the process and discuss novel concepts in the area of machine learning, including identifying treatment effects and explaining the output from machine learning models.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Epidemiologic Methods
/
Outcome Assessment, Health Care
/
Machine Learning
Type of study:
Prognostic_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
Annu Rev Public Health
Year:
2020
Document type:
Article