Predicting 28-day all-cause unplanned hospital re-admission of patients with alcohol use disorders: a machine learning approach.

Abstract

Introduction: Patients with alcohol use disorders have a high hospital re-admission rate, adding to the strain on the healthcare system. To address this issue, this study aimed to predict 28-day unplanned hospital re-admission for these patients.

Methods: From linked de-identified datasets, patients with alcohol use disorders who had hospital re-admissions between 2015 and 2018 were identified. Univariate and multiple logistic regression were conducted to select variables for inclusion in five machine learning models-logistic regression (baseline), random forest, support vector machine, long-short term memory and clinical bio bidirectional encoder representation of transformers (Clinical Bio-BERT)-to predict the 28-day re-admission.

Results: Eight hundred and sixty-nine patients with alcohol use disorders incurred 2254 hospital admissions. Patients aged 45-49 or 70-74 or 75-79 were 4-5 times more likely to be re-admitted than those in other age groups; males were 36% more likely than females; patients who use polysubstance were 3.3 times more likely than otherwise. Patients with "respiratory system disorders" or "hepatobiliary system and pancreas disorders" had 60% higher risk than otherwise. Interaction with emergency department or drug and alcohol service after discharge reduced the risk by 71% and 79%, respectively. The 10-variable Clinical Bio-BERT demonstrated the highest sensitivity (.724).

Discussion and conclusions: Patients with alcohol use disorders with the following characteristics were more likely to have unplanned re-admissions within 28 days: male, aged 45-49 or 70-74 or 75-79, with "respiratory system disorders" or "hepatobiliary system and pancreas disorders", or patients who use polysubstance. Interactions with emergency department or drug and alcohol service after discharge had reduced risk of hospital re-admission.