Mortality Prediction Model

Mortality prediction models aim to accurately forecast the likelihood of death, primarily focusing on improving prediction accuracy and interpretability across diverse populations and healthcare settings. Current research emphasizes hybrid approaches combining statistical methods (like exponential smoothing) with deep learning architectures (e.g., recurrent neural networks, convolutional networks, and transformers), often incorporating sparse attention mechanisms to enhance model transparency and efficiency. These advancements hold significant promise for improving clinical decision-making, resource allocation, and personalized treatment strategies in critical care and other areas of medicine, particularly by enabling more reliable risk stratification and fairer algorithmic applications.

Papers

November 21, 2023

Fast and Interpretable Mortality Risk Scores for Critical Care Patients
Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin
Intensive Care Black Box Machine Learning Model Credit Scoring Model Mortality Prediction Model

August 30, 2023

Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction
Yola Jones, Fani Deligianni, Jeff Dalton, Pierpaolo Pellicori, John G F Cleland
Internal State Consensus Group Decision Year Specific Mortality Surface Global Interpretability Mortality Prediction Model

August 24, 2023

Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction
Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D. Salim, Antonio Jimeno Yepes, Jun Shen, Jiang Bian
Fine Grained Risk Prediction Hypergraph Convolutional Network Patient Outcome Prediction Mortality Prediction Model

July 28, 2023

Is this model reliable for everyone? Testing for strong calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner
Full Model Calibration Performance Calibration Error Mortality Prediction Model

May 2, 2023

Expressive Mortality Models through Gaussian Process Kernels
Mike Ludkovski, Jimmy Risk
Gaussian Process Covariance Conditioning Year Specific Mortality Surface Covariance Structure Mortality Prediction Model

March 30, 2023

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Sirui Ding, Qiaoyu Tan, Chia-yuan Chang, Na Zou, Kai Zhang, Nathan R. Hoot, Xiaoqian Jiang, Xia Hu
Case Study Multi Task Learning Year Specific Mortality Surface xApp Distillation Mortality Prediction Model Liver Transplant

December 12, 2022

Mortality Prediction Models with Clinical Notes Using Sparse Attention at the Word and Sentence Levels
Miguel Rios, Ameen Abu-Hanna
Self Attention Attention Mechanism Sentence Level Clinical Note Sparse Attention Real Text Word Clinical Prediction Model Hospital Mortality Prediction Mortality Prediction Model

December 16, 2021

A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
Thabang Mathonsi, Terence L. van Zyl
Multivariate Time Series Forecasting Hybrid Deep Learning Statistical Query Exponential Smoothing Forecast Combination Mortality Prediction Model E RNN

Mortality Prediction Model

Papers

Fast and Interpretable Mortality Risk Scores for Critical Care Patients

Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction

Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

Is this model reliable for everyone? Testing for strong calibration

Expressive Mortality Models through Gaussian Process Kernels

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

Mortality Prediction Models with Clinical Notes Using Sparse Attention at the Word and Sentence Levels

A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling