Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes. (May 2016)

• Record Type: Journal Article
• Authors: Kurasawa, Hisashi; Hayashi, Katsuyoshi; Fujino, Akinori; Takasugi, Koichi; Haga, Tsuneyuki; Waki, Kayo; Noguchi, Takashi; Ohe, Kazuhiko
• Is Part Of: Journal of diabetes science and technology. Volume 10:Number 3(2016:May)
• Page Start: 730
• Page End: 736
• Publication Date: 2016-05
• Subjects: logistic regression model -- L2-norm regularization -- machine learning -- missed clinic appointment; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/1932296815614866
• Languages: English
• ISSNs: 1932-2968

Abstract

Background: About 10% of patients with diabetes discontinue treatment, resulting in the progression of diabetes-related complications and reduced quality of life. Objective: The objective was to predict a missed clinical appointment (MA), which can lead to discontinued treatment for diabetes patients. Methods: A machine-learning algorithm was used to build a logistic regression model for MA predictions, with L2-norm regularization used to avoid over-fitting and 10-fold cross validation used to evaluate prediction performance. Data associated with patient MAs were extracted from electronic medical records and classified into two groups: one related to patients' clinical condition (X1) and the other related to previous findings (X2). The records used were those of the University of Tokyo Hospital, and they included the history of 16 026 clinical appointments scheduled by 879 patients whose initial clinical visit had been made after January 1, 2004, who had diagnostic codes indicating diabetes, and whose HbA1c had been tested within 3 months after their initial visit. Records between April 1, 2011, and June 30, 2014, were inspected for a history of MAs. Results: The best predictor of MAs proved to be X1 + X2 (AUC = 0.958); precision and recall rates were, respectively, 0.757 and 0.659. Among all the appointment data, the day of the week when an appointment was made was most strongly associated with MA predictions (weight = 2.22). Conclusions: Our findings may provide information to help clinicians make timely interventions to avoid MAs.