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Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using...

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Autores principales: Fan, Peihao, Guo, Xiaojiang, Qi, Xiguang, Matharu, Mallika, Patel, Ravi, Sakolsky, Dara, Kirisci, Levent, Silverstein, Jonathan C., Wang, Lirong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692143/
https://www.ncbi.nlm.nih.gov/pubmed/33121080
http://dx.doi.org/10.3390/brainsci10110784
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author Fan, Peihao
Guo, Xiaojiang
Qi, Xiguang
Matharu, Mallika
Patel, Ravi
Sakolsky, Dara
Kirisci, Levent
Silverstein, Jonathan C.
Wang, Lirong
author_facet Fan, Peihao
Guo, Xiaojiang
Qi, Xiguang
Matharu, Mallika
Patel, Ravi
Sakolsky, Dara
Kirisci, Levent
Silverstein, Jonathan C.
Wang, Lirong
author_sort Fan, Peihao
collection PubMed
description Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Sertraline, Fentanyl, Aripiprazole, Lamotrigine, and Tramadol were strong indicators for no SREs within one year. The use of Haloperidol, Trazodone and Citalopram, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.
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spelling pubmed-76921432020-11-28 Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder Fan, Peihao Guo, Xiaojiang Qi, Xiguang Matharu, Mallika Patel, Ravi Sakolsky, Dara Kirisci, Levent Silverstein, Jonathan C. Wang, Lirong Brain Sci Article Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Sertraline, Fentanyl, Aripiprazole, Lamotrigine, and Tramadol were strong indicators for no SREs within one year. The use of Haloperidol, Trazodone and Citalopram, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making. MDPI 2020-10-27 /pmc/articles/PMC7692143/ /pubmed/33121080 http://dx.doi.org/10.3390/brainsci10110784 Text en © 2020 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Fan, Peihao
Guo, Xiaojiang
Qi, Xiguang
Matharu, Mallika
Patel, Ravi
Sakolsky, Dara
Kirisci, Levent
Silverstein, Jonathan C.
Wang, Lirong
Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder
title Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder
title_full Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder
title_fullStr Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder
title_full_unstemmed Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder
title_short Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder
title_sort prediction of suicide-related events by analyzing electronic medical records from ptsd patients with bipolar disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692143/
https://www.ncbi.nlm.nih.gov/pubmed/33121080
http://dx.doi.org/10.3390/brainsci10110784
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