Mortality Prediction
Mortality prediction is the task of predicting the mortality of patients. It is important in medical decision support systems in order to prioritise the patients who have a high risk of mortality.
State-of-the-art leaderboards
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Greatest papers with code
Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data
Nature Scientific Data 2019 • yerevann/mimic3-benchmarks
Health care is one of the most exciting frontiers in data mining and machine learning.
COMPUTATIONAL PHENOTYPING LENGTH-OF-STAY PREDICTION MORTALITY PREDICTION
Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning.
COMPUTATIONAL PHENOTYPING LENGTH-OF-STAY PREDICTION MORTALITY PREDICTION TIME SERIES
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
• USC-Melady/Benchmarking_DL_MIMICIII
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications.
LENGTH-OF-STAY PREDICTION MORTALITY PREDICTION SPEECH RECOGNITION TIME SERIES
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
However, flexible tools such as artificial neural networks (ANNs) suffer from a lack of interpretability limiting their acceptability to clinicians.
Early hospital mortality prediction using vital signals
• RezaSadeghiWSU/Early-Hospital-Mortality-Prediction-using-Vital-Signals
In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients.
SOTA for Mortality Prediction on MIMIC-III
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight on the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations.
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed measurements.
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
• MLforHealth/MIMIC_Generalisation
When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.
Evaluation of Embeddings of Laboratory Test Codes for Patients at a Cancer Center
• elleros/DSHealth2019_loinc_embeddings
Laboratory test results are an important and generally high dimensional component of a patient's Electronic Health Record (EHR).