Koochakpour Kaban, Pant Dipendra, Westbye Odd Sverre, Røst Thomas Brox, Leventhal Bennett, Koposov Roman, Clausen Carolyn, Skokauskas Norbert, Nytrø Øystein
Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Child and Adolescent Psychiatry, Clinic of Mental Health Care, St. Olav University Hospital, Trondheim, Norway.
PeerJ Comput Sci. 2024 Oct 18;10:e2367. doi: 10.7717/peerj-cs.2367. eCollection 2024.
This study addresses the challenge of predicting readmissions in Child and Adolescent Mental Health Services (CAMHS) by analyzing the predictability of readmissions over short, medium, and long term periods. Using health records spanning 35 years, which included 22,643 patients and 30,938 episodes of care, we focused on the episode of care as a central unit, defined as a referral-discharge cycle that incorporates assessments and interventions. Data pre-processing involved handling missing values, normalizing, and transforming data, while resolving issues related to overlapping episodes and correcting registration errors where possible. Readmission prediction was inferred from electronic health records (EHR), as this variable was not directly recorded. A binary classifier distinguished between readmitted and non-readmitted patients, followed by a multi-class classifier to categorize readmissions based on timeframes: short (within 6 months), medium (6 months - 2 years), and long (more than 2 years). Several predictive models were evaluated based on metrics like AUC, F1-score, precision, and recall, and the K-prototype algorithm was employed to explore similarities between episodes through clustering. The optimal binary classifier (Oversampled Gradient Boosting) achieved an AUC of 0.7005, while the multi-class classifier (Oversampled Random Forest) reached an AUC of 0.6368. The K-prototype resulted in three clusters as optimal (SI: 0.256, CI: 4473.64). Despite identifying relationships between care intensity, case complexity, and readmission risk, generalizing these findings proved difficult, partly because clinicians often avoid discharging patients likely to be readmitted. Overall, while this dataset offers insights into patient care and service patterns, predicting readmissions remains challenging, suggesting a need for improved analytical models that consider patient development, disease progression, and intervention effects.
本研究通过分析儿童和青少年心理健康服务（CAMHS）中短期、中期和长期再入院的可预测性，应对预测再入院这一挑战。利用跨越35年的健康记录，其中包括22643名患者和30938次护理事件，我们将护理事件作为核心单元，定义为一个包含评估和干预的转诊-出院周期。数据预处理包括处理缺失值、归一化和转换数据，同时解决与重叠事件相关的问题并尽可能纠正登记错误。由于再入院这一变量并非直接记录，因此从电子健康记录（EHR）中推断再入院预测。一个二元分类器区分再入院患者和未再入院患者，随后是一个多类分类器，根据时间框架对再入院进行分类：短期（6个月内）、中期（6个月至2年）和长期（超过2年）。基于AUC、F1分数、精确率和召回率等指标评估了几种预测模型，并采用K原型算法通过聚类探索事件之间的相似性。最优二元分类器（过采样梯度提升）的AUC为0.7005，而多类分类器（过采样随机森林）的AUC为0.6368。K原型算法得出最优的三个聚类（SI：0.256，CI：4473.64）。尽管确定了护理强度、病例复杂性和再入院风险之间的关系，但将这些发现进行推广却很困难，部分原因是临床医生通常会避免让可能再次入院的患者出院。总体而言，虽然该数据集为患者护理和服务模式提供了见解，但预测再入院仍然具有挑战性，这表明需要改进分析模型，以考虑患者的发育情况、疾病进展和干预效果。
