Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder.
Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder.
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DOI：
10.3390/brainsci10110784
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发表时间：
2020-10-27
期刊：
影响因子：
3.3
通讯作者：
Wang L
中科院分区：
文献类型：
--
作者：
Fan P;Guo X;Qi X;Matharu M;Patel R;Sakolsky D;Kirisci L;Silverstein JC;Wang L
全世界每年约有80万人死于自杀，这是美国第十大死亡原因。建立一个能够准确预测自杀的数学模型，特别是在高危人群中，具有重要的价值。使用从电子病历（EMR）中获得的特征对几种不同的基于ML的模型进行了训练和评估。计算每个特征的贡献，以确定它如何影响模型预测。选择性能最佳的模型进行分析和分解。随机森林表现出最好的性能，真阳性率（TPR）和阳性预测值（PPV）大于80%。舍曲林、芬太尼、阿立哌唑、拉莫三嗪和曲马多的使用是一年内无SRE的有力指标。使用氟哌啶醇、曲唑酮和西酞普兰，在诊断为PTSD和双相情感障碍时诊断为自闭症、精神分裂症或物质使用障碍，可预测一年内SRE的发生。该模型识别了对SRE具有潜在保护或危险影响的其他特征。我们构建了一个基于ML的模型，该模型成功地识别了PTSD和双相情感障碍诊断后一年内SRE高风险亚群中的患者。该模型还提供了特征分解，以指导机制研究。该模型与其他EMR数据集的验证将在资源分配和临床决策方面具有重要价值。
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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