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Volume 12
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Article

DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients

by
Oshin Miranda
1,†,
Peihao Fan
1,†,
Xiguang Qi
1,†,
Zeshui Yu
2,
Jian Ying
3,
Haohan Wang
4,
David A. Brent
5,
Jonathan C. Silverstein
6,*,
Yu Chen
7,* and
Lirong Wang
1,*
1
Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15206, USA
2
Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15206, USA
3
Department of Internal Medicine, University of Utah, Salt Lake City, UT 84132, USA
4
Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
5
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
6
Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
7
Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46225, USA
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Pers. Med. 2022, 12(4), 524; https://doi.org/10.3390/jpm12040524
Submission received: 15 February 2022 / Revised: 21 March 2022 / Accepted: 22 March 2022 / Published: 24 March 2022
(This article belongs to the Topic Big Data in Healthcare, Bioinformatics and Precision Medicine)
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Abstract

Identifying patients with high risk of suicide is critical for suicide prevention. We examined lab tests together with medication use and diagnosis from electronic medical records (EMR) data for prediction of suicide-related events (SREs; suicidal ideations, attempts and deaths) in post-traumatic stress disorder (PTSD) patients, a population with a high risk of suicide. We developed DeepBiomarker, a deep-learning model through augmenting the data, including lab tests, and integrating contribution analysis for key factor identification. We applied DeepBiomarker to analyze EMR data of 38,807 PTSD patients from the University of Pittsburgh Medical Center. Our model predicted whether a patient would have an SRE within the following 3 months with an area under curve score of 0.930. Through contribution analysis, we identified important lab tests for suicide prediction. These identified factors imply that the regulation of the immune system, respiratory system, cardiovascular system, and gut microbiome were involved in shaping the pathophysiological pathways promoting depression and suicidal risks in PTSD patients. Our results showed that abnormal lab tests combined with medication use and diagnosis could facilitate predicting SRE risk. Moreover, this may imply beneficial effects for suicide prevention by treating comorbidities associated with these biomarkers.
Keywords: PTSD; real-world evidence; deep learning; biomarker identification PTSD; real-world evidence; deep learning; biomarker identification

Share and Cite

MDPI and ACS Style

Miranda, O.; Fan, P.; Qi, X.; Yu, Z.; Ying, J.; Wang, H.; Brent, D.A.; Silverstein, J.C.; Chen, Y.; Wang, L. DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients. J. Pers. Med. 2022, 12, 524. https://doi.org/10.3390/jpm12040524

AMA Style

Miranda O, Fan P, Qi X, Yu Z, Ying J, Wang H, Brent DA, Silverstein JC, Chen Y, Wang L. DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients. Journal of Personalized Medicine. 2022; 12(4):524. https://doi.org/10.3390/jpm12040524

Chicago/Turabian Style

Miranda, Oshin, Peihao Fan, Xiguang Qi, Zeshui Yu, Jian Ying, Haohan Wang, David A. Brent, Jonathan C. Silverstein, Yu Chen, and Lirong Wang. 2022. "DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients" Journal of Personalized Medicine 12, no. 4: 524. https://doi.org/10.3390/jpm12040524

APA Style

Miranda, O., Fan, P., Qi, X., Yu, Z., Ying, J., Wang, H., Brent, D. A., Silverstein, J. C., Chen, Y., & Wang, L. (2022). DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients. Journal of Personalized Medicine, 12(4), 524. https://doi.org/10.3390/jpm12040524

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