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Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in dete...

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Autores principales: Richter, Thalia, Fishbain, Barak, Richter-Levin, Gal, Okon-Singer, Hadas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537335/
https://www.ncbi.nlm.nih.gov/pubmed/34683098
http://dx.doi.org/10.3390/jpm11100957
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author Richter, Thalia
Fishbain, Barak
Richter-Levin, Gal
Okon-Singer, Hadas
author_facet Richter, Thalia
Fishbain, Barak
Richter-Levin, Gal
Okon-Singer, Hadas
author_sort Richter, Thalia
collection PubMed
description The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.
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spelling pubmed-85373352021-10-24 Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions Richter, Thalia Fishbain, Barak Richter-Levin, Gal Okon-Singer, Hadas J Pers Med Review The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future. MDPI 2021-09-26 /pmc/articles/PMC8537335/ /pubmed/34683098 http://dx.doi.org/10.3390/jpm11100957 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Richter, Thalia
Fishbain, Barak
Richter-Levin, Gal
Okon-Singer, Hadas
Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
title Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
title_full Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
title_fullStr Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
title_full_unstemmed Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
title_short Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
title_sort machine learning-based behavioral diagnostic tools for depression: advances, challenges, and future directions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537335/
https://www.ncbi.nlm.nih.gov/pubmed/34683098
http://dx.doi.org/10.3390/jpm11100957
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