The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers

BMC Medical Informatics and Decision Making

Yun Liu¹  Lei Wang²  Jiancheng Dong²  Huiqun Wu²  Siwei Zhou²  Kui Jiang²  Zheqing Zhang²  Yujuan Shang³ 

[1] Department of Information, the First Affiliated Hospital, Nanjing Medical University, No. 300 Guang Zhou Road, 210029, Nanjing, Jiangsu, People’s Republic of China;Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 211166, Nanjing, Jiangsu, People’s Republic of China;Department of Medical Informatics, Medical School of Nantong University, 19 Qixiu Road, 226001, Nantong, Jiangsu, People’s Republic of China;Department of Medical Informatics, Medical School of Nantong University, 19 Qixiu Road, 226001, Nantong, Jiangsu, People’s Republic of China;Department of Statistics and Data Management, Children’s Hospital of Fudan University, 201102, Shanghai, People’s Republic of China;

关键词: Prediction model;    Readmission;    Diabetes;    Machine learning;   

DOI  :  10.1186/s12911-021-01423-y

来源: Springer

【 摘 要 】

Background and objectivesDiabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients.MethodsThe dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such asrandom forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared.ResultsA total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions.ConclusionThe factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and  deserves further validation in clinical trials.

【 授权许可】

CC BY