title: Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes.
abstract: About 10% of patients with diabetes discontinue treatment, resulting in the progression of diabetes-related complications and reduced quality of life.
  
  
  
  The objective was to predict a missed clinical appointment (MA), which can lead to discontinued treatment for diabetes patients.
  
  
  
  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.
  
  
  
  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).
  
  
  
  Our findings may provide information to help clinicians make timely interventions to avoid MAs.
authors.affiliation: University of Tokyo, Tokyo, Japan Nippon Telegraph and Telephone Corporation, Tokyo, Japan.
authors.forename: Hisashi
authors.initials: H
authors.lastname: Kurasawa
authors.affiliation: University of Tokyo, Tokyo, Japan Nippon Telegraph and Telephone Corporation, Tokyo, Japan.
authors.forename: Katsuyoshi
authors.initials: K
authors.lastname: Hayashi
authors.affiliation: Nippon Telegraph and Telephone Corporation, Tokyo, Japan.
authors.forename: Akinori
authors.initials: A
authors.lastname: Fujino
authors.affiliation: University of Tokyo, Tokyo, Japan Nippon Telegraph and Telephone Corporation, Tokyo, Japan.
authors.forename: Koichi
authors.initials: K
authors.lastname: Takasugi
authors.affiliation: Nippon Telegraph and Telephone Corporation, Tokyo, Japan.
authors.forename: Tsuneyuki
authors.initials: T
authors.lastname: Haga
authors.affiliation: University of Tokyo, Tokyo, Japan kwaki-tky@umin.ac.jp.
authors.forename: Kayo
authors.initials: K
authors.lastname: Waki
authors.affiliation: University of Tokyo, Tokyo, Japan.
authors.forename: Takashi
authors.initials: T
authors.lastname: Noguchi
authors.affiliation: University of Tokyo, Tokyo, Japan.
authors.forename: Kazuhiko
authors.initials: K
authors.lastname: Ohe
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D003920:Diabetes Mellitus; D005260:Female; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D000067455:No-Show Patients; D012372:ROC Curve
keywords: L2-norm regularization; logistic regression model; machine learning; missed clinic appointment