Mortality Prediction
60 papers with code • 2 benchmarks • 4 datasets
( Image credit: Early hospital mortality prediction using vital signals )
Benchmarks
These leaderboards are used to track progress in Mortality Prediction
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Libraries
Use these libraries to find Mortality Prediction models and implementations
MIMIC-III
MeDAL
eICU-CRD
Clinical Admission Notes from MIMIC-III
Latest papers with no code
Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data
Using medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), collected from 139 patients with acute myocardial infarction, we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years.
Voice-Driven Mortality Prediction in Hospitalized Heart Failure Patients: A Machine Learning Approach Enhanced with Diagnostic Biomarkers
However, there is a lack of voice biomarkers for predicting mortality rates among heart failure patients with standardized speech protocols.
Analysis and Mortality Prediction using Multiclass Classification for Older Adults with Type 2 Diabetes
A new target variable is invented by combining the two original target variables.
A Kalman Filter Based Framework for Monitoring the Performance of In-Hospital Mortality Prediction Models Over Time
Therefore, in this study, for binary classifiers running in a long time period, we proposed to adjust these performance metrics for sample size and class distribution, so that a fair comparison can be made between two time periods.
TA-RNN: an Attention-based Time-aware Recurrent Neural Network Architecture for Electronic Health Records
For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit.
Inadequacy of common stochastic neural networks for reliable clinical decision support
In both methods, unsubstantiated model confidence is not prevented due to strongly biased functional posteriors, rendering them inappropriate for reliable clinical decision support.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond
This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond.
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model
The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable.
Preserving the knowledge of long clinical texts using aggregated ensembles of large language models
The results and analysis of this study is supportive of our method assisting in clinical healthcare systems by enabling clinical decision-making with robust performance overcoming the challenges of long text inputs and varied datasets.
