Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, USA.
Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, New Hampshire, USA.
Epilepsia. 2022 Sep;63(9):2269-2278. doi: 10.1111/epi.17324. Epub 2022 Jun 20.
The prevalence of suicide in the United States has seen an increasing trend and is responsible for 1.6% of all mortality nationwide. Although suicide has the potential to broadly impact the entire population, it has a substantially increased prevalence in persons with epilepsy (PWE), despite many of these individuals consistently seeing a health care provider. The goal of this work is to predict the development of suicidal ideation (SI) in PWE using machine learning methodology such that providers can be better prepared to address suicidality at visits where it is likely to be prominent.
The current study leverages data collected at an epilepsy clinic during patient visits to predict whether an individual will exhibit SI at their next visit. The data used for prediction consisted of patient responses to questions about the severity of their epilepsy, issues with memory/concentration, somatic problems, markers for mental health, and demographic information. A machine learning approach was then applied to predict whether an individual would display SI at their following visit using only data collected at the prior visit.
The modeling approach allowed for the successful prediction of an individual's passive and active SI severity at the following visit (r = .42, r = .39) as well as the presence of SI regardless of severity (area under the curve [AUC] = .82, AUC = .8). This shows that the model was successfully able to synthesize the unique combination of an individual's responses to important questions during a clinical visit and utilize that information to indicate whether that individual will exhibit SI at their next visit.
The results of this modeling approach allow the health care team to be prepared, in advance of a clinical visit, for the potential reporting of SI. By allowing the necessary support to be prepared ahead of time, it can be better integrated at the point of care, where patients are most likely to follow up on potential referrals or treatment.
美国的自杀率呈上升趋势，占全国总死亡率的 1.6%。尽管自杀有可能广泛影响整个人群，但在癫痫患者（PWE）中，自杀的发病率显著增加，尽管这些人中的许多人一直在接受医疗保健提供者的治疗。这项工作的目的是使用机器学习方法预测 PWE 中自杀意念（SI）的发展，以便提供者能够更好地准备在可能出现自杀倾向的就诊时解决自杀问题。
本研究利用癫痫诊所就诊期间收集的数据来预测个体在下一次就诊时是否会出现 SI。用于预测的数据包括患者对其癫痫严重程度、记忆力/注意力问题、躯体问题、心理健康标志物和人口统计学信息的回答。然后，应用机器学习方法仅使用上次就诊时收集的数据来预测个体是否会在下一次就诊时出现 SI。
该建模方法能够成功预测个体在下一次就诊时的被动和主动 SI 严重程度（r=.42，r=.39）以及 SI 的存在与否（曲线下面积 [AUC] =.82，AUC =.8）。这表明该模型能够成功地综合个体在临床就诊期间对重要问题的回答的独特组合，并利用这些信息来指示个体在下一次就诊时是否会出现 SI。
这种建模方法的结果使医疗团队能够在就诊前为可能报告的 SI 做好准备。通过提前准备必要的支持，可以在患者最有可能跟进潜在转介或治疗的护理点更好地整合。
