西班牙Koa Health公司Aleksandar Matic、庞培法布拉大学Roger Garriga等研究人员开发出从电子健康记录中预测心理健康危机的机器学习模型。这一研究成果于2022年5月16日在线发表在国际学术期刊《自然—医学》上。

研究人员开发了一个机器学习模型，利用电子健康记录，在28天内持续监测病人的心理健康危机风险。该模型的接收者操作特征曲线下的面积为0.797，精确召回曲线下的面积为0.159，预测危机的灵敏度为58%，特异性为85%。一项为期6个月的后续前瞻性研究评估了这个算法在临床实践中的应用，并观察到预测在管理病例或减轻64%病例的危机风险方面具有临床价值。这项研究是第一个连续预测各种心理健康危机的风险，并探索这种预测在临床实践中的附加价值。

据悉，及时发现有精神健康危机风险的病人，可以改善疗效，减轻负担和成本。然而，精神健康问题的高发率意味着人工审查复杂的病人记录以做出积极的护理决定在实践中是不可行的。

附：英文原文

Title: Machine learning model to predict mental health crises from electronic health records

Author: Garriga, Roger, Mas, Javier, Abraha, Semhar, Nolan, Jon, Harrison, Oliver, Tadros, George, Matic, Aleksandar

Issue&Volume: 2022-05-16

Abstract: The timely identification of patients who are at risk of a mental health crisis can lead to improved outcomes and to the mitigation of burdens and costs. However, the high prevalence of mental health problems means that the manual review of complex patient records to make proactive care decisions is not feasible in practice. Therefore, we developed a machine learning model that uses electronic health records to continuously monitor patients for risk of a mental health crisis over a period of 28days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. A follow-up 6-month prospective study evaluated our algorithm’s use in clinical practice and observed predictions to be clinically valuable in terms of either managing caseloads or mitigating the risk of crisis in 64% of cases. To our knowledge, this study is the first to continuously predict the risk of a wide range of mental health crises and to explore the added value of such predictions in clinical practice.

DOI: 10.1038/s41591-022-01811-5

Source: https://www.nature.com/articles/s41591-022-01811-5