Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development
Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development
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DOI：
10.2196/37951
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发表时间：
2022-09
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影响因子：
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通讯作者：
Hisashi Kurasawa;K. Waki;Akihiro Chiba;Tomohisa Seki;Katsuyoshi Hayashi;Akinori Fujino;T. Haga;T. Noguchi;K. Ohe
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文献类型：
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作者：
Hisashi Kurasawa;K. Waki;Akihiro Chiba;Tomohisa Seki;Katsuyoshi Hayashi;Akinori Fujino;T. Haga;T. Noguchi;K. Ohe
背景治疗中止（TD）是糖尿病护理中的主要预后问题之一，已经提出了几种模型来预测可能导致TD的糖尿病患者错过的预约，通过使用二元分类模型来早期检测TD并为患者提供干预支持。然而，由于二元分类模型输出在预定时间段内发生的错过预约的概率，因此它们在估计预约之间间隔不一致的患者的TD风险的大小方面的能力有限，使得难以优先考虑应该为其提供干预支持的患者。目的本研究旨在开发一种机器学习的预测模型，该模型可以输出TD风险评分，该评分由到TD的时间长度定义，并根据TD风险对患者进行干预。该模型包括2012年9月3日至2014年5月17日期间在东京大学医院诊断代码指示糖尿病的患者。该模型在2014年5月18日至2016年1月29日期间对同一医院的患者进行了内部验证。本研究中使用的数据包括2004年1月1日以后到医院就诊的7551名患者，这些患者的诊断代码指示糖尿病。特别是，使用了2012年9月3日至2016年1月29日期间记录在电子病历中的数据。主要结局是患者的TD，其定义为错过计划的临床预约，并且在患者访视之间的平均天数的3倍内和60天内没有医院访视。TD风险评分通过使用从机器学习的排名模型导出的参数计算。通过使用测试数据和C指数对患者进行排名，接受者操作特征曲线下面积和区分的精确度-召回率曲线下面积以及校准图来评估预测能力。结果TD风险评分的C指数、受试者工作特征曲线下面积和精确-召回曲线下面积的均值（95%置信限）分别为0.749（0.655，0.823）、0.758（0.649，0.857）和0.713（0.554，0.841）。观察到的和预测的概率与校准图。结论通过将机器学习方法与电子病历相结合，为糖尿病患者开发了TD风险评分。评分计算可以集成到医疗记录中，以识别TD高风险患者，这将有助于支持糖尿病护理和预防TD。
Background Treatment discontinuation (TD) is one of the major prognostic issues in diabetes care, and several models have been proposed to predict a missed appointment that may lead to TD in patients with diabetes by using binary classification models for the early detection of TD and for providing intervention support for patients. However, as binary classification models output the probability of a missed appointment occurring within a predetermined period, they are limited in their ability to estimate the magnitude of TD risk in patients with inconsistent intervals between appointments, making it difficult to prioritize patients for whom intervention support should be provided. Objective This study aimed to develop a machine-learned prediction model that can output a TD risk score defined by the length of time until TD and prioritize patients for intervention according to their TD risk. Methods This model included patients with diagnostic codes indicative of diabetes at the University of Tokyo Hospital between September 3, 2012, and May 17, 2014. The model was internally validated with patients from the same hospital from May 18, 2014, to January 29, 2016. The data used in this study included 7551 patients who visited the hospital after January 1, 2004, and had diagnostic codes indicative of diabetes. In particular, data that were recorded in the electronic medical records between September 3, 2012, and January 29, 2016, were used. The main outcome was the TD of a patient, which was defined as missing a scheduled clinical appointment and having no hospital visits within 3 times the average number of days between the visits of the patient and within 60 days. The TD risk score was calculated by using the parameters derived from the machine-learned ranking model. The prediction capacity was evaluated by using test data with the C-index for the performance of ranking patients, area under the receiver operating characteristic curve, and area under the precision-recall curve for discrimination, in addition to a calibration plot. Results The means (95% confidence limits) of the C-index, area under the receiver operating characteristic curve, and area under the precision-recall curve for the TD risk score were 0.749 (0.655, 0.823), 0.758 (0.649, 0.857), and 0.713 (0.554, 0.841), respectively. The observed and predicted probabilities were correlated with the calibration plots. Conclusions A TD risk score was developed for patients with diabetes by combining a machine-learned method with electronic medical records. The score calculation can be integrated into medical records to identify patients at high risk of TD, which would be useful in supporting diabetes care and preventing TD.