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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2021

Open Access 01-12-2021 | Research article

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

Authors: Alan Brnabic, Lisa M. Hess

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

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Abstract

Background

Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making.

Methods

This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist.

Results

A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies.

Conclusions

A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.
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Metadata
Title
Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making
Authors
Alan Brnabic
Lisa M. Hess
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-021-01403-2

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