Horsens Regional Hospital, Horsens, Denmark.
Enversion A/S, Aarhus, Denmark.
Med Care. 2023 Apr 1;61(4):226-236. doi: 10.1097/MLR.0000000000001830. Epub 2023 Feb 3.
The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been directed at potentially preventable hospitalizations.
We aimed to develop an artificial intelligence (AI) prediction model for potentially preventable hospitalizations in the coming year, and to apply explainable AI to identify predictors of hospitalization and their interaction.
We used the Danish CROSS-TRACKS cohort and included citizens in 2016-2017. We predicted potentially preventable hospitalizations within the following year using the citizens' sociodemographic characteristics, clinical characteristics, and health care utilization as predictors. Extreme gradient boosting was used to predict potentially preventable hospitalizations with Shapley additive explanations values serving to explain the impact of each predictor. We reported the area under the receiver operating characteristic curve, the area under the precision-recall curve, and 95% confidence intervals (CI) based on five-fold cross-validation.
The best performing prediction model showed an area under the receiver operating characteristic curve of 0.789 (CI: 0.782-0.795) and an area under the precision-recall curve of 0.232 (CI: 0.219-0.246). The predictors with the highest impact on the prediction model were age, prescription drugs for obstructive airway diseases, antibiotics, and use of municipality services. We found an interaction between age and use of municipality services, suggesting that citizens aged 75+ years receiving municipality services had a lower risk of potentially preventable hospitalization.
AI is suitable for predicting potentially preventable hospitalizations. The municipality-based health services seem to have a preventive effect on potentially preventable hospitalizations.
人口老龄化和医疗资源有限给医疗保健部门带来了新的挑战。减少住院人数已成为许多国家的政治重点，尤其是针对那些可以预防的住院情况。
我们旨在开发一种人工智能（AI）预测模型，以预测未来一年内可能需要住院治疗的情况，并应用可解释的 AI 来识别住院的预测因素及其相互作用。
我们使用丹麦的 CROSS-TRACKS 队列，纳入了 2016-2017 年的公民。我们使用公民的社会人口统计学特征、临床特征和医疗保健利用情况作为预测指标，预测未来一年内可能需要住院治疗的情况。使用极端梯度提升算法进行预测，并使用 Shapley 加性解释值来解释每个预测指标的影响。我们报告了基于五折交叉验证的接收者操作特征曲线下面积、精度-召回曲线下面积及其 95%置信区间（CI）。
表现最佳的预测模型的接收者操作特征曲线下面积为 0.789（CI：0.782-0.795），精度-召回曲线下面积为 0.232（CI：0.219-0.246）。对预测模型影响最大的预测因素是年龄、阻塞性气道疾病的处方药物、抗生素和使用市立服务。我们发现年龄和使用市立服务之间存在交互作用，这表明 75 岁以上的公民在使用市立服务时，潜在可预防住院的风险较低。
AI 适合预测潜在可预防的住院情况。基于市立的医疗服务似乎对潜在可预防的住院有预防作用。
