OBJECTIVE The main goal of the study was to predict individual patients' future mental healthcare consumption, and thereby enhancing the design of an efficient demand-oriented mental healthcare system by focusing on a patient population associated with intensive mental healthcare consumption. Factors that affect the mental healthcare consumption of service users with non-affective psychosis were identified, and subsequently used in a prognostic model to predict future healthcare consumption. METHOD This study was a secondary analysis of an existing dataset from the GROUP study. Based on mental healthcare consumption, patients with non-affective psychosis were divided into two groups: low (N = 579) and high (N = 488) intensive mental healthcare consumers. Three different techniques from the field of machine learning were applied on crosssectional data to identify risk factors: logistic regression, classification tree and a random forest. Subsequently, the same techniques were applied longitudinally in order to predict future healthcare consumption. RESULTS Identified variables that affected healthcare consumption were the number of psychotic episodes, paid employment, engagement in social activities, previous healthcare consumption, and met needs. Analyses showed that the random forest method is best suited to model risk factors, and that these relations predict future healthcare consumption (AUC 0.71, PPV 0.65). CONCLUSIONS Machine learning techniques provide valuable information for identifying risk factors in psychosis. They may thus help clinicians optimize allocation of mental healthcare resources by predicting future healthcare consumption.

中文翻译：

---

使用机器学习来预测非情感性精神病的心理保健消费

目的 本研究的主要目标是预测个体患者未来的精神卫生保健消费，从而通过关注与密集精神卫生​​保健消费相关的患者群体来加强有效的以需求为导向的精神卫生保健系统的设计。确定了影响患有非情感性精神病的服务使用者的心理保健消费的因素，随后将其用于预后模型以预测未来的保健消费。方法 本研究是对 GROUP 研究中现有数据集的二次分析。根据心理保健消费，非情感性精神病患者分为两组：低（N = 579）和高（N = 488）密集型心理保健消费者。机器学习领域的三种不同技术应用于横截面数据以识别风险因素：逻辑回归、分类树和随机森林。随后，纵向应用相同的技术以预测未来的医疗保健消费。结果 已确定的影响医疗保健消费的变量是精神病发作次数、有偿工作、参与社会活动、以前的医疗保健消费和满足的需求。分析表明，随机森林方法最适合对风险因素建模，并且这些关系可以预测未来的医疗保健消费（AUC 0.71，PPV 0.65）。结论 机器学习技术为识别精神病的风险因素提供了有价值的信息。