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Predicting depression using electronic health records : a systematic review
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Nickson, David, Meyer, Caroline, Walasek, Lukasz and Toro, Carla T. (2023) Predicting depression using electronic health records : a systematic review. BMC Medical Informatics and Decision Making . doi:10.21203/rs.3.rs-2510168/v1 (Submitted)
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Official URL: https://doi.org/10.21203/rs.3.rs-2510168/v1
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 (ML) methods have been used in combination with Electronic Health Records (EHRs) 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 EHRs 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 over the generalizability and interpretability.
Item Type: | Submitted Journal Article | ||||||
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Subjects: | Q Science > Q Science (General) R Medicine > R Medicine (General) R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Mental Health and Wellbeing Faculty of Science, Engineering and Medicine > Science > Psychology Administration > University Executive Office Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Artificial Intelligence, Depression, Mental , Depression, Mental -- Diagnosis , Depression, Mental -- Patients, Medical records -- Data processing, Information storage and retrieval systems -- Medical care, Machine learning | ||||||
Journal or Publication Title: | BMC Medical Informatics and Decision Making | ||||||
Publisher: | BioMed Central Ltd. | ||||||
ISSN: | 1472-6947 | ||||||
Official Date: | 24 January 2023 | ||||||
Dates: |
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DOI: | 10.21203/rs.3.rs-2510168/v1 | ||||||
Status: | Not Peer Reviewed | ||||||
Publication Status: | Submitted | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
RIOXX Funder/Project Grant: |
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