Papers Mortality Prediction
“Mortality Prediction” 태그가 달린 논문 237편 · 필터 해제
Beyond Demographic Balance: Multi-Metric and Intersectional Evaluation of Fairness in MIMIC-IV Mortality Prediction
Fairness conclusions in clinical prediction can depend strongly on both the metrics reported and the demographic resolution at which performance is evaluated. We revisit these evaluation choices for ICU mortality predict…
Mortality PredictionICU MortalityMMTClinic: 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 PredictionHoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care
Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain. However, existing MKG-based…
Representation LearningMortality PredictionClinical KnowledgeKnowledge GraphsMILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
Multimodal irregular time series (MITS) consist of asynchronous and irregularly sampled observations from heterogeneous numerical and textual channels. In healthcare, for example, patients' electronic health records (EHR…
Mortality PredictionFrom Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction
By processing electronic health records (EHRs) as natural language sequences, large language models (LLMs) have shown potential in clinical prediction tasks such as mortality prediction and phenotyping. However, longitud…
Self-Supervised LearningMortality PredictionBiosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model
Cardiovascular disease remains the leading cause of global mortality, yet scalable cardiac monitoring is hindered by the gap between diagnostic-rich ECG and ubiquitous wearable PPG. Bridging this gap requires representat…
Mortality PredictionEnhance the after-discharge mortality rate prediction via learning from the medical notes
With the increase of the Electronic Health Records (EHR) data, more and more researchers are developing machine learning models to learn from the medical notes. These unstructured text data pose significant challenges on…
Mortality PredictionNEURON: A Neuro-symbolic System for Grounded Clinical Explainability
Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability. We present NEUR…
Mortality PredictionLearning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness
Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolution of patient health. Yet these observations are sparse, and whether …
Representation LearningMortality PredictionSparse Autoencoder Decomposition of Clinical Sequence Model Representations: Feature Complexity, Task Specialisation, and Mortality Prediction
Sparse autoencoders (SAEs) have been applied to large language models and protein language models, but not systematically to electronic health record (EHR) foundation models. We train TopK SAEs on FlatASCEND, a 14.5-mill…
Mortality PredictionA Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs
Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk prediction. However, due to diverse clinical workflows and privacy constraints,…
Computational EfficiencyMortality PredictionIs Clinical Text Enough? A Multimodal Study on Mortality Prediction in Heart Failure Patients
Accurate short-term mortality prediction in heart failure (HF) remains challenging, particularly when relying on structured electronic health record (EHR) data alone. We evaluate transformer-based models on a French HF c…
Mortality PredictionTemporal Validation Changes the Apparent Public-Health Utility of Under-Five Mortality Prediction in Bangladesh: A Four-Round DHS Machine-Learning Study
Background: Under-five mortality in Bangladesh remains uneven despite national progress. DHS-based prediction models may guide targeted follow-up, but only if validation reflects future use. We examined how validation de…
Neural Architecture SearchMortality PredictionEmbracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities
Medical multimodal learning faces significant challenges with missing modalities prevalent in clinical practice. Existing approaches assume equal contribution of modality and random missing patterns, neglecting inherent …
Mortality PredictionTemporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care
We introduce Temporal Fusion Nexus (TFN), a multi-modal and task-agnostic embedding model to integrate irregular time series and unstructured clinical narratives. We analysed TFN in post-kidney transplant (KTx) care, wit…
Mortality Prediction