Papers Decompensation
“Decompensation” 태그가 달린 논문 16편 · 필터 해제
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection
In cross-silo Federated Learning (FL), client selection is critical to ensure high model performance, yet it remains challenging due to data quality decompensation, budget constraints, and incentive compatibility. As tra…
DecompensationFederated LearningA Risk Taxonomy for Evaluating AI-Powered Psychotherapy Agents
The proliferation of Large Language Models (LLMs) and Intelligent Virtual Agents acting as psychotherapists presents significant opportunities for expanding mental healthcare access. However, their deployment has also be…
BenchmarkingDecompensationLiver Cirrhosis Stage Estimation from MRI with Deep Learning
We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation from multi-sequence MRI. Cirrhosis is the severe scarring (fibrosis) of the liver and a common endpoint of various chronic l…
DecompensationDeep LearningMultimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency Departm…
DecompensationTemporal Label Smoothing for Early Event Prediction
Models that can predict the occurrence of events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging task is typically treated as…
Binary ClassificationCirculatory FailureDecompensationPrediction+2Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases
Objective: Clinical notes contain information not present elsewhere, including drug response and symptoms, all of which are highly important when predicting key outcomes in acute care patients. We propose the automatic a…
DecompensationLate fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure
Objective: Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization. Early detection of HF symptoms and pro-active management may reduce adverse events. A…
DecompensationManagementA Multi-Modal and Multitask Benchmark in the Clinical Domain
Healthcare represents one of the most promising application areas for machine learning algorithms, including modern methods based on deep learning. Modern deep learning algorithms perform best on large datasets and on…
BIG-bench Machine LearningDecompensationDeep LearningTime Series+1Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data
The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled other sub-fields of machine learning forwar…
BIG-bench Machine LearningDecompensationObject RecognitionPredicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning
Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acu…
DecompensationImproving Emergency Department ESI Acuity Assignment Using Machine Learning and Clinical Natural Language Processing
Effective triage is critical to mitigating the effect of increased volume by accurately determining patient acuity, need for resources, and establishing effective acuity-based patient prioritization. The purpose of this …
BIG-bench Machine LearningDecompensationBenchmarking machine learning models on multi-centre eICU critical care dataset
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established …
BenchmarkingBIG-bench Machine LearningDecompensationMortality Prediction+1Using Clinical Notes with Time Series Data for ICU Management
Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been conti…
DecompensationManagementMortality PredictionTime Series+1Using Clinical Notes for ICU Management
Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been conti…
DecompensationManagementMortality PredictionTime Series+1Patient Subtyping with Disease Progression and Irregular Observation Trajectories
Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of c…
DecompensationRAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at i…
Decompensation