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Fan P, Guo X, Qi X, Matharu M, Patel R, Sakolsky D, Kirisci L, Silverstein JC, Wang L. Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder. Brain Sci 2020; 10:E784. [PMID: 33121080 PMCID: PMC7692143 DOI: 10.3390/brainsci10110784] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 10/16/2020] [Accepted: 10/21/2020] [Indexed: 02/06/2023] Open
Abstract
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. The use of Trazodone and Citalopram at baseline 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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Affiliation(s)
- Peihao Fan
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, Pittsburgh, PA 15206, USA; (P.F.); (X.G.); (X.Q.)
| | - Xiaojiang Guo
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, Pittsburgh, PA 15206, USA; (P.F.); (X.G.); (X.Q.)
| | - Xiguang Qi
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, Pittsburgh, PA 15206, USA; (P.F.); (X.G.); (X.Q.)
| | - Mallika Matharu
- Department of Statistics and Department of Economics, University of Pittsburgh School of Arts & Sciences, Pittsburgh, PA 15213, USA;
| | - Ravi Patel
- Department of Pharmacy and Therapeutics, University of Pittsburgh School of Pharmacy, Pittsburgh, PA 15213, USA;
| | - Dara Sakolsky
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA;
| | - Levent Kirisci
- Department of Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, PA 15213, USA
| | - Jonathan C. Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Lirong Wang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, Pittsburgh, PA 15206, USA; (P.F.); (X.G.); (X.Q.)
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