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
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability.
XAI for In-hospital Mortality Prediction via Multimodal ICU Data
To address this issue, this paper proposes an eXplainable Multimodal Mortality Predictor (X-MMP) approaching an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data.
MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records
This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality.
Multimodal Pretraining of Medical Time Series and Notes
kingrc15/multimodal-clinical-pretraining
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In downstream tasks, including in-hospital mortality prediction and phenotyping, our pretrained model outperforms baselines in settings where only a fraction of the data is labeled, emphasizing its ability to enhance ICU data analysis.
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns.
Dynamic Multimodal Information Bottleneck for Multimodality Classification
ayanglab/dmib
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Specifically, our information bottleneck module serves to filter out the task-irrelevant information and noises in the fused feature, and we further introduce a sufficiency loss to prevent dropping of task-relevant information, thus explicitly preserving the sufficiency of prediction information in the distilled feature.
General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History
starmpcc/remed
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This approach effectively eliminates the need for manual feature selection and enables an unrestricted observation window.
Privacy-preserving patient clustering for personalized federated learning
g2lab/pcfbl
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In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy.
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
information-fusion-lab-umass/multiwave
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To address these issues, we introduce MultiWave, a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals.
MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression
Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment.
