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

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 meth

SUBMITTER: Nickson D 

PROVIDER: S-EPMC10680172 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature