Patient Readmission

Hospital readmission, the re-hospitalization of patients shortly after discharge, is a significant concern impacting patient outcomes and healthcare costs. Current research focuses on developing predictive models using diverse data sources (electronic health records, medical images, clinical notes, insurance claims) and advanced machine learning techniques, including deep learning architectures like LSTMs, graph transformers, and multilayer perceptrons, to identify at-risk patients. These models aim to improve prediction accuracy and interpretability, enabling timely interventions to reduce readmission rates and optimize resource allocation. The ultimate goal is to improve patient care and reduce the substantial financial burden associated with unnecessary hospital readmissions.

Papers

June 28, 2024

Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission
Abolfazl Zarghani
Machine Learning Model Comparative Study LSTM Network LSTM Model Predictive Healthcare Patient 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

November 11, 2023

MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction
Yan Miao, Lequan Yu
Graph Transformer Multimodal Feature Spatiotemporal Feature Patient Readmission Hospital Readmission Prediction

September 25, 2023

Explainable Machine Learning for ICU Readmission Prediction
Alex G. C. de Sá, Daniel Gould, Anna Fedyukova, Mitchell Nicholas, Lucy Dockrell, Calvin Fletcher, David Pilcher, Daniel Capurro, David B. Ascher, Khaled El-Khawas, Douglas E. V. Pires
Intensive Care Unit Explainable Machine Learning Intensive Care Readmission Prediction Unplanned Readmission Patient Readmission

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

Patient Readmission

Papers

Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission

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

MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction

Explainable Machine Learning for ICU Readmission Prediction

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