SOTAVerified

Mortality Prediction

Papers

Showing 2650 of 189 papers

TitleStatusHype
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
Interpretable Patient Mortality Prediction with Multi-value Rule SetsCode0
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICUCode0
Characterizing the Value of Information in Medical NotesCode0
Interpolation-Prediction Networks for Irregularly Sampled Time SeriesCode0
IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing ModalitiesCode0
Improving COVID-19 Forecasting using eXogenous VariablesCode0
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive CareCode0
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular DataCode0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksCode0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
COPER: Continuous Patient State PerceiverCode0
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural NetworksCode0
Fast and Interpretable Mortality Risk Scores for Critical Care PatientsCode0
Evaluation of Embeddings of Laboratory Test Codes for Patients at a Cancer CenterCode0
Countdown Regression: Sharp and Calibrated Survival PredictionsCode0
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital DataCode0
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge InjectionCode0
Deep Kernel Learning for Mortality Prediction in the Face of Temporal ShiftCode0
Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series dataCode0
Feature importance to explain multimodal prediction models. A clinical use caseCode0
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-SeriesCode0
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestF1 score0.97Unverified
2Gaussian SVMF1 score0.96Unverified
3Decision TreeF1 score0.91Unverified
4Boosted TreesF1 score0.87Unverified
5ELECTRA (pretrained)Accuracy0.84Unverified
6ELECTRA (from scratch)Accuracy0.83Unverified
7LSTM+SA (pretrained)Accuracy0.83Unverified
8LSTM (pretrained)Accuracy0.83Unverified
9K-NNF1 score0.82Unverified
10LSTM+SA (from scratch)Accuracy0.8Unverified