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Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach
BACKGROUND: A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD). OBJECTIVE: Identifying those at elevated risk shortly after trauma exposure is a cli...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6681635/ https://www.ncbi.nlm.nih.gov/pubmed/31333201 http://dx.doi.org/10.2196/13946 |
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author | Wshah, Safwan Skalka, Christian Price, Matthew |
author_facet | Wshah, Safwan Skalka, Christian Price, Matthew |
author_sort | Wshah, Safwan |
collection | PubMed |
description | BACKGROUND: A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD). OBJECTIVE: Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions. METHODS: We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones. RESULTS: We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma. CONCLUSIONS: These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention. |
format | Online Article Text |
id | pubmed-6681635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-66816352019-08-20 Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach Wshah, Safwan Skalka, Christian Price, Matthew JMIR Ment Health Original Paper BACKGROUND: A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD). OBJECTIVE: Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions. METHODS: We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones. RESULTS: We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma. CONCLUSIONS: These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention. JMIR Publications 2019-07-22 /pmc/articles/PMC6681635/ /pubmed/31333201 http://dx.doi.org/10.2196/13946 Text en ©Safwan Wshah, Christian Skalka, Matthew Price. Originally published in JMIR Mental Health (http://mental.jmir.org), 22.07.2019. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Wshah, Safwan Skalka, Christian Price, Matthew Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach |
title | Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach |
title_full | Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach |
title_fullStr | Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach |
title_full_unstemmed | Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach |
title_short | Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach |
title_sort | predicting posttraumatic stress disorder risk: a machine learning approach |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6681635/ https://www.ncbi.nlm.nih.gov/pubmed/31333201 http://dx.doi.org/10.2196/13946 |
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