BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSION 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.

中文翻译：

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使用常规数据预测精神病医院的患者结果：机器学习方法。

背景技术机器学习应用中的一个常见问题是决策时数据的可用性。本研究的目的是利用入院时容易获得的常规数据来预测与精神病院护理组织相关的方面。进一步的目标是将机器学习方法的结果与通过传统方法获得的结果以及通过朴素基线分类器获得的结果进行比较。方法该研究纳入了2017年1月1日至2018年12月31日期间德国黑森州9家精神病医院连续出院的患者。我们将随机梯度提升 (GBM) 与多重逻辑回归和朴素基线分类器实现的预测性能进行了比较。我们在另一个日历年和不同医院的未见过的患者身上测试了最终模型的性能。结果 该研究包括 45,388 例住院患者。通过接受者操作特征曲线下面积衡量的模型性能在预测结果之间差异很大，在预测强制治疗（曲线下面积：0.83）和 1:1 观察（0.80）方面具有相对较高的性能在预测短期住院时间（0.69）和治疗无反应（0.65）方面表现相对较差。GBM 的表现略好于逻辑回归。这两种方法都比仅基于基本诊断分组的天真预测要好得多。结论 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。未来的研究应该调查预测性能，这对于在临床实践中提供有效帮助以造福工作人员和患者是必要的。