Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

本研究构建了基于语言学驱动的预测模型，用于评估自杀风险。这些模型源自美国退伍军人事务部（U.S. Veterans Administration, VA）医疗记录全国样本中的非结构化临床病历。本研究构建了三组匹配队列：自杀身亡的退伍军人、使用过心理健康服务且未自杀的退伍军人，以及观察期内未使用心理健康服务且未自杀的退伍军人（每组样本量n=70）。研究人员从临床病历中提取单关键词与多词短语以构建数据集，并基于遗传编程框架的机器学习算法构建预测模型。最终得到的推理准确率始终可达65%及以上。因此本研究数据表明，计算机文本分析技术可应用于非结构化医疗记录以评估自杀风险。该系统可帮助临床医生在基层医疗层面对看似健康的患者进行自杀风险筛查，并持续评估精神疾病患者的自杀风险。