International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Medical Records and Health Information, Health Polytechnic of the Ministry of Health Tasikmalaya, Tasikmalaya, West Java, Indonesia.
Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Int J Med Inform. 2021 Oct;154:104569. doi: 10.1016/j.ijmedinf.2021.104569. Epub 2021 Sep 4.
Type 2 diabetes mellitus (T2DM) and hypertension (HTN), both non-communicable diseases, are leading causes of death globally, with more imbalances in lower middle-income countries. Furthermore, poor treatment and management are known to lead to intensified healthcare utilization and increased medical care costs and impose a significant societal burden, in these countries, including Indonesia. Predicting future clinical outcomes can determine the line of treatment and value of healthcare costs, while ensuring effective patient care. In this paper, we present the prediction of length of stay (LoS) and mortality among hospitalized patients at a tertiary referral hospital in Tasikmalaya, Indonesia, between 2016 and 2019. We also aimed to determine how socio-demographic characteristics, and T2DM- or HTN-related comorbidities affect inpatient LoS and mortality.
We analyzed insurance claims data of 4376 patients with T2DM or HTN hospitalized in the referral hospital. We used four prediction models based on machine-learning algorithms for LoS prediction, in relation to disease severity, physician-in-charge, room type, co-morbidities, and types of procedures performed. We used five classifiers based on multilayer perceptron (MLP) to predict inpatient mortality and compared them according to training time, testing time, and Area under Receiver Operative Curve (AUROC). Classifier accuracy measures, which included positive predictive value (PPV), negative predictive value (NPV), F-Measure, and recall, were used as performance evaluation methods.
A Random forest best predicted inpatient LoS (R2, 0.70; root mean square error [RMSE], 1.96; mean absolute error [MAE], 0.935), and the gradient boosting regression model also performed similarly (R2, 0.69; RMSE, 1.96; MAE, 0.935). For inpatient mortality, best results were observed using MLP with back propagation (AUROC 0.899; 69.33 and 98.61 for PPV and NPV, respectively). The other classifiers, stochastic gradient descent with regression loss function, Huber, and random forest models all showed an average performance.
Linear regression model best predicted LoS and mortality was best predicted using MLP. Patients with primary diseases such as T2DM or HTN may have comorbidities that can prolong inpatient LoS. Physicians play an important role in disseminating health related information. These predictions could assist in the development of health policies and strategies that reduce disease burden in resource-limited settings.
2 型糖尿病（T2DM）和高血压（HTN）均为非传染性疾病，是全球主要的死亡原因，在中低收入国家更为失衡。此外，据了解，较差的治疗和管理会导致医疗利用率增加，医疗费用增加，并给包括印度尼西亚在内的这些国家带来重大的社会负担。预测未来的临床结果可以确定治疗方案和医疗保健成本的价值，同时确保有效的患者护理。在本文中，我们介绍了在印度尼西亚塔斯马尼亚的一家三级转诊医院 2016 年至 2019 年住院患者的住院时间（LoS）和死亡率预测。我们还旨在确定社会人口统计学特征以及与 T2DM 或 HTN 相关的合并症如何影响住院患者的 LoS 和死亡率。
我们分析了在转诊医院住院的 4376 名 T2DM 或 HTN 患者的保险索赔数据。我们使用了基于机器学习算法的四个预测模型来预测与疾病严重程度、主治医生、病房类型、合并症和所进行的手术类型有关的 LoS 预测。我们使用基于多层感知器（MLP）的五个分类器来预测住院患者的死亡率，并根据训练时间、测试时间和接收器工作特性曲线下的面积（AUROC）进行比较。分类器准确性度量包括阳性预测值（PPV）、阴性预测值（NPV）、F 度量和召回率，作为性能评估方法。
随机森林最能预测住院患者的 LoS（R2，0.70；均方根误差 [RMSE]，1.96；平均绝对误差 [MAE]，0.935），梯度提升回归模型也表现相似（R2，0.69；RMSE，1.96；MAE，0.935）。对于住院患者的死亡率，使用带回归损失函数的反向传播 MLP 获得了最佳结果（AUROC 0.899；PPV 和 NPV 分别为 69.33 和 98.61）。其他分类器，包括随机梯度下降回归损失函数、Huber 和随机森林模型，表现平均。
线性回归模型最能预测 LoS，而 MLP 最能预测死亡率。患有 T2DM 或 HTN 等原发性疾病的患者可能患有会延长住院 LoS 的合并症。医生在传播健康相关信息方面发挥着重要作用。这些预测可以帮助制定减轻资源有限环境下疾病负担的卫生政策和战略。
