Mortality Prediction
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Benchmarks
MIMIC-III
Most implemented
Multitask learning and benchmarking with clinical time series data
A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care
Efficient nonparametric statistical inference on population feature importance using Shapley values
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration
Papers
MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domain
Time-series data in clinical settings is crucial for capturing dynamic changes in a patient's health over time, enabling timely diagnosis, personalized treatment, and early detection of critical events. However, the deve…
Mortality PredictionQuestion AnsweringMaking Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction
Clinical language models can achieve strong in-hospital accuracy yet fail under deployment shifts because they exploit note-specific artifacts (e.g., templates, separators, boilerplate) that do not reflect patient state.…
Mortality PredictionText ClassificationQuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
Cardiac arrest remains one of the most lethal conditions encountered in intensive care units. Despite the growing availability of electronic health record data, existing mortality prediction studies in this population la…
Mortality PredictionLearning the Pareto Frontier of Predictive Models under Distribution Shift
Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks. These pretrained models can differ not only in performance but also in how they can be used: some o…
Mortality PredictionDomain AdaptationA Personalized Computational Framework for Assessing the Sufficiency of Partially Observed Data in Healthcare AI models
Achieving early and timely diagnosis and treatment for disease is a major challenge. Recent applications of machine learning (ML) algorithms trained on patient data have shown promise in many different settings for predi…
Mortality PredictionFusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling
Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift. We introduce a foundation model-driven framework for cross-modal representation …
Mortality Prediction