Hospital Readmission

Hospital readmission, the re-hospitalization of patients shortly after discharge, is a significant healthcare problem impacting patient outcomes and costs. Current research focuses on developing accurate predictive models using diverse data sources (electronic health records, medical claims, imaging) and sophisticated algorithms, including deep learning (e.g., multilayer perceptrons, convolutional LSTMs, graph neural networks), to identify at-risk patients. These models aim to improve prediction accuracy while enhancing interpretability for clinical use, addressing biases and incorporating temporal information for better risk stratification. Ultimately, improved readmission prediction can lead to more effective interventions, resource allocation, and ultimately better patient care.

Papers

October 23, 2024

Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model
Xintao Li, Sibei Liu, Dezhi Yu, Yang Zhang, Xiaoyu Liu
Human Prediction DCU Insight AQ LSTM Network Patient Friendly Language Patient Data LSTM Model Readmission Prediction Hospital Readmission

March 12, 2024

Enhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts from Clinical Texts
Rasoul Samani, Mohammad Dehghani, Fahime Shahrokh
Deep Learning Clinical Text Biomedical Concept Readmission Prediction Hospital Readmission Unplanned Readmission Patient Readmission

October 16, 2023

An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records
Fabio Azzalini, Tommaso Dolci, Marco Vagaggini
Deep Learning Electronic Health Record Interpretable Deep Learning Hospital Readmission Unplanned Readmission Hospital Readmission Prediction

May 14, 2023

Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation
Eitam Sheetrit, Menachem Brief, Oren Elisha
Intensive Care Unit Multimodal Evaluation Readmission Prediction Hospital Readmission Unplanned Readmission

April 19, 2023

Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Hongjing Xia, Joshua C. Chang, Sarah Nowak, Sonya Mahajan, Rohit Mahajan, Ted L. Chang, Carson C. Chow
Interpretable Way Post COVID 19 Biased Causal Effect Heterogeneous Population Hospital Readmission Interpretable Survival

August 28, 2022

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
Joshua C. Chang, Ted L. Chang, Carson C. Chow, Rohit Mahajan, Sonya Mahajan, Joe Maisog, Shashaank Vattikuti, Hongjing Xia
Interpretable Way Bayesian Deep Learning Survival Model Medical Claim Hospital Readmission Discharge Instruction

June 30, 2022

Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data
Shuja Khalid, Francisco Matos, Ayman Abunimer, Joel Bartlett, Richard Duszak, Michal Horny, Judy Gichoya, Imon Banerjee, Hari Trivedi
Human Prediction Long Short Term Memory Healthcare Data Hospital Readmission Unplanned Readmission Patient Readmission

June 13, 2022

A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission
Shaina Raza
Machine Learning Model Human Prediction Medical Diagnosis Health Disparity Clinical Data Healthcare Datasets Fairness Measure Fair Prediction Hospital Readmission

April 14, 2022

Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel Rubin, Bhavik N. Patel, Imon Banerjee
Neural Network Graph Learning Deep Learning Based Better Prediction Medical Multimodal Readmission Prediction Hospital Readmission

Hospital Readmission

Papers

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

Enhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts from Clinical Texts

An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records

Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation

Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death

Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data

A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission

Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission