Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study

Abstract

Due to multiple comorbid illnesses, polypharmacy, and age-related changes in pharmacokinetics and pharmacodynamics in older adults, the prevalence of potentially inappropriate medications (PIMs) is high, which affects the quality of life of older adults. Building an effective warning model is necessary for the early identification of PIMs to prevent harm caused by medication in geriatric patients. The purpose of this study was to develop a machine learning-based model for the warning of PIMs in older Chinese outpatients. This retrospective study was conducted among geriatric outpatients in nine tertiary hospitals in Chengdu from January 2018 to December 2018. The Beers criteria 2019 were used to assess PIMs in geriatric outpatients. Three problem transformation methods were used to tackle the multilabel classification problem in prescriptions. After the division of patient prescriptions into the training and test sets (8:2), we adopted six widely used classification algorithms to conduct the classification task and assessed the discriminative performance by the accuracy, precision, recall, F1 scores, subset accuracy (ss Acc), and Hamming loss (hm) of each model. The results showed that among 11,741 older patient prescriptions, 5816 PIMs were identified in 4038 (34.39%) patient prescriptions. A total of 41 types of PIMs were identified in these prescriptions. The three-problem transformation methods included label power set (LP), classifier chains (CC), and binary relevance (BR). Six classification algorithms were used to establish the warning models, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), CatBoost, Deep Forest (DF), and TabNet. The CC + CatBoost model had the highest accuracy value (97.83%), recall value (89.34%), F1 value (90.69%), and ss Acc value (97.79%) with a good precision value (92.18%) and the lowest hm value (0.0006). Therefore, the CC + CatBoost model was selected to predict the occurrence of PIM in geriatric Chinese patients. This study’s novelty establishes a warning model for PIMs in geriatric patients by using machine learning. With the popularity of electronic patient record systems, sophisticated computer algorithms can be implemented at the bedside to improve medication use safety in geriatric patients in the future.

Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital

“…Foremost, the guardians of outpatients were required to register and wait for their turn with a doctor. As a result, we computed waiting time between the time required to register and the time beginning of the consultation ( 11 , 12 ). The dependent variable was the waiting time.…”

“…Many machine learning algorithms ( 8 ), including deep learning ( 9 ) and random forest (RF) ( 10 ), have demonstrated excellent performance in time prediction. Studies have used AI to predict the onset time of illnesses and the time spent in the emergency department and operating room ( 11 , 12 ). However, compared with studies conducted elsewhere, those concerning the outpatient care situation in China entail greater difficulty.…”

“…China currently has few models for predicting waiting times and even fewer models that are based on AI. Although two studies of prediction in medication for older adults ( 11 ) and chronic respiratory diseases ( 12 ) have been conducted in China, there is no specific research on patient waiting time prediction models for outpatients in Chinese children’s hospitals. To better respond to hospital development and patient demands, this study developed a set of AI algorithm models capable of accurately predicting patient wait times using data from the hospital information system (HIS) of Shanghai Children’s Medical Center (SCMC) and the characteristics of each department.…”