Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model

• Main Authors: Li, Xintao, Liu, Sibei, Yu, Dezhi, Zhang, Yang, Liu, Xiaoyu
• Format: Journal Article
• Language: English
• Published: 22-10-2024
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2410.17545

Readmissions among Medicare beneficiaries are a major problem for the US
healthcare system from a perspective of both healthcare operations and patient
caregiving outcomes. Our study analyzes Medicare hospital readmissions using
LSTM networks with feature engineering to assess feature contributions. We
selected variables from admission-level data, inpatient medical history and
patient demography. The LSTM model is designed to capture temporal dynamics
from admission-level and patient-level data. On a case study on the MIMIC
dataset, the LSTM model outperformed the logistic regression baseline,
accurately leveraging temporal features to predict readmission. The major
features were the Charlson Comorbidity Index, hospital length of stay, the
hospital admissions over the past 6 months, while demographic variables were
less impactful. This work suggests that LSTM networks offers a more promising
approach to improve Medicare patient readmission prediction. It captures
temporal interactions in patient databases, enhancing current prediction models
for healthcare providers. Adoption of predictive models into clinical practice
may be more effective in identifying Medicare patients to provide early and
targeted interventions to improve patient outcomes.