Identifying factors associated with locomotive syndrome using machine learning methods: The third survey of the research on osteoarthritis/osteoporosis against disability study.

AIM: To identify factors associated with locomotive syndrome (LS) using medical questionnaire data and machine learning. METHODS: A total of 1575 participants underwent the LS risk tests from the third survey of the research on osteoarthritis/osteoporosis against disability study (ROAD) study. LS was defined as stage 1 or higher based on clinical decision limits of the Japanese Orthopaedic Association. A total of 1335 items of medical questionnaire data came from this study. The number of medical questionnaire items was reduced from 1335 to 331 in data cleaning. From the 331 items, identify factors associated with LS use by light gradient boosting machine-based recursive feature elimination with cross-validation. The performance of each set was evaluated using an average of seven performance metrics, including 95% confidence intervals, using a bootstrapping method. The smallest set of items is determined with the highest average of receiver operating characteristic area under the curve (ROC-AUC) under 20 items as association factors of LS. Additionally, the performance of the selected items was compared with the LS risk tests and Loco-check. RESULTS: The nine items have the best average ROC-AUC under 20 items. The nine items show an average ROC-AUC of 0.858 (95% confidence interval 0.816-0.898). Age and back pain during walking were strongly associated with the prevalence of LS. The ROC-AUC of nine items is higher than that of existing questionnaire-based LS assessments, including the 25-question Geriatric Locomotor Scale and Loco-check. CONCLUSIONS: The identified nine items could aid early LS detection, enhancing understanding and prevention. Geriatr Gerontol Int 2024; ••: ••-••.

Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development.

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.

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

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.