FeatEHR-LLM uses LLMs with tool-augmented code generation on dataset schemas to extract clinically meaningful features from irregular EHR time series, achieving the highest AUROC on 7 of 8 ICU prediction tasks with gains up to 6 points over baselines.
The eICU Col- laborative Research Database, a freely available multi-center database for critical care research.Scientific Data
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
dataset 3
citation-polarity summary
roles
dataset 3representative citing papers
An LLM-guided framework simulates physiological trajectories to provide interpretable early warnings for sepsis, achieving AUC scores of 0.861-0.903 on MIMIC-IV and eICU data.
Autoregressive transformer modeling with missingness-aware contrastive pre-training outperforms baselines on MIMIC-IV and eICU benchmarks and mitigates divergent behavior from removed modalities in clinical trajectories.
SurvBench supplies a configurable, open-source preprocessing pipeline that standardizes multi-modal EHR data from four critical-care databases for single-risk and competing-risk survival analysis.
Develops and tests an OWL-based NIRS ontology with 145 classes, SWRL rules, and SPARQL evaluation on 6 patient scenarios to enable rule-based clinical reasoning and standardized documentation.
Benchmarking in pediatric ICU antimicrobial stewardship shows performance depends mainly on target prevalence and dataset traits rather than model complexity, with sequence models improving precision-recall at 24-hour resolution but showing poorer calibration than tabular models.
The paper introduces ClinQueryAgent, a conversational agent that converts natural language queries into database queries for population health management while keeping patient data secure, and reports its use by 128 staff across 15 NHS practices covering 148,319 patients.
Machine learning models on a novel Romanian EHR dataset of 12,286 sepsis hospitalizations achieve AUC 0.983 for death versus recovery prediction and identify eosinopenia as a top predictor via SHAP.
citing papers explorer
-
FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records
FeatEHR-LLM uses LLMs with tool-augmented code generation on dataset schemas to extract clinically meaningful features from irregular EHR time series, achieving the highest AUROC on 7 of 8 ICU prediction tasks with gains up to 6 points over baselines.
-
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
An LLM-guided framework simulates physiological trajectories to provide interpretable early warnings for sepsis, achieving AUC scores of 0.861-0.903 on MIMIC-IV and eICU data.
-
Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling
Autoregressive transformer modeling with missingness-aware contrastive pre-training outperforms baselines on MIMIC-IV and eICU benchmarks and mitigates divergent behavior from removed modalities in clinical trajectories.
-
SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis
SurvBench supplies a configurable, open-source preprocessing pipeline that standardizes multi-modal EHR data from four critical-care databases for single-risk and competing-risk survival analysis.
-
Development and Evaluation of an Ontology for Non-Invasive Respiratory Support in Acute Care
Develops and tests an OWL-based NIRS ontology with 145 classes, SWRL rules, and SPARQL evaluation on 6 patient scenarios to enable rule-based clinical reasoning and standardized documentation.
-
Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
Benchmarking in pediatric ICU antimicrobial stewardship shows performance depends mainly on target prevalence and dataset traits rather than model complexity, with sequence models improving precision-recall at 24-hour resolution but showing poorer calibration than tabular models.
-
ClinQueryAgent: A Conversational Agent for Population Health Management
The paper introduces ClinQueryAgent, a conversational agent that converts natural language queries into database queries for population health management while keeping patient data secure, and reports its use by 128 staff across 15 NHS practices covering 148,319 patients.
-
Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset
Machine learning models on a novel Romanian EHR dataset of 12,286 sepsis hospitalizations achieve AUC 0.983 for death versus recovery prediction and identify eosinopenia as a top predictor via SHAP.