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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | Mood Disorders | Research

Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review

Authors: David Nickson, Caroline Meyer, Lukasz Walasek, Carla Toro

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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Abstract

Background

Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used in combination with Electronic Health Records for prediction of depression.

Methods

Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022).

Results

Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques.

Limitations

The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography.

Conclusion

This review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns with generalizability and interpretability.
Appendix
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Metadata
Title
Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review
Authors
David Nickson
Caroline Meyer
Lukasz Walasek
Carla Toro
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02341-x

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