Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Department of Business Development, Evangelical Foundation Neuerkerode, Braunschweig, Germany.
BMC Med Inform Decis Mak. 2020 Feb 6;20(1):21. doi: 10.1186/s12911-020-1042-2.
A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.
The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals.
The study included 45,388 inpatient episodes. The models' performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping.
The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.
机器学习应用中的一个常见问题是在决策点可用的数据。本研究的目的是使用入院时现成的常规数据来预测与精神病院护理组织相关的方面。另一个目的是将机器学习方法的结果与传统方法和朴素基线分类器的结果进行比较。
该研究纳入了 2017 年 1 月 1 日至 2018 年 12 月 31 日期间德国黑森州 9 家精神病院连续出院的患者。我们比较了随机梯度增强（GBM）与多变量逻辑回归和朴素基线分类器的预测性能。我们在另一年和来自不同医院的未见过的患者上测试了我们最终模型的性能。
该研究共纳入 45388 例住院患者。模型的性能，通过接收器操作特征曲线下的面积来衡量，在预测结果之间差异很大，在预测强制性治疗（曲线下面积：0.83）和 1:1 观察结果（0.80）方面表现出较高的性能，而在预测短住院时间（0.69）和治疗无反应（0.65）方面表现较差。GBM 的表现略优于逻辑回归。两种方法都明显优于仅基于基本诊断分组的朴素预测。
本研究表明，行政常规数据可用于预测与精神病院护理组织相关的方面。未来的研究应调查在为员工和患者的利益提供有效的临床实践辅助方面所需的预测性能。
