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首页> 外文期刊>BMC Medical Informatics and Decision Making >Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach
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Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

机译：预测常规数据的精神病院患者结果：机器学习方法

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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.
机译：机器学习应用中的常见问题是在决策点的数据的可用性。本研究的目的是使用常规数据在准入时进行常规数据，以预测与精神科医院护理组织相关的方面。进一步的目的是将机器学习方法的结果与通过传统方法获得的那些和通过天真基线分类而获得的结果进行比较。该研究包括2017年1月1日至2018年1月1日至2018年12月31日的患者，从德国黑森州的九家精神病院。我们比较了随机梯度升压（GBM）实现的预测性能，具有多个逻辑回归和天真基线分类器。我们测试了我们在另一家日历年和不同医院的看不见患者上的最终模型的表现。该研究包括45,388个入住性发作。由接收器操作特性曲线下的面积测量的模型的性能，在预测的结果之间具有强烈变化，在矫顽矫顽约（曲线下面积下的区域）和1：1观察中具有相对高的性能（0.83）在预测的时间内的预测（0.69）和对治疗的不反应（0.65）的情况下相对较差的性能相对较差（0.69）。 GBM比Logistic回归略好。两种方法基本上比仅基于基本诊断分组的天真预测。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临床实践中提供有效援助所需的预测性能，以获得员工和患者的益处。

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