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Found 15 papers, 11 papers with code
Cost-effective Interactive Attention Learning with Neural Attention Process
Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features.
Medical Hallucinations in Foundation Models and Their Impact on Healthcare
Our contributions include (1) a taxonomy for understanding and addressing medical hallucinations, (2) benchmarking models using medical hallucination dataset and physician-annotated LLM responses to real medical cases, providing direct insight into the clinical impact of hallucinations, and (3) a multi-national clinician survey on their experiences with medical hallucinations.
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
We present RelCon, a novel self-supervised Relative Contrastive learning approach for training a motion foundation model from wearable accelerometry sensors.
Identifying Differential Patient Care Through Inverse Intent Inference
Sepsis is a life-threatening condition defined by end-organ dysfunction due to a dysregulated host response to infection.
Finding "Good Views" of Electrocardiogram Signals for Inferring Abnormalities in Cardiac Condition
In this project, we investigate several ways to define positive samples, and assess which approach yields the best performance in a downstream task of classifying arrhythmia.
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively.
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
MDAgents achieved the best performance in seven out of ten benchmarks on tasks requiring an understanding of medical knowledge and multi-modal reasoning, showing a significant improvement of up to 4. 2% (p < 0. 05) compared to previous methods' best performances.
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Event-Based Contrastive Learning for Medical Time Series
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event.
Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals
Additionally, the supervised DML model that uses ECGs with access to 8, 172 mPCWP labels demonstrated significantly better performance on the mPCWP regression task compared to the supervised baseline.
End-to-End Learnable Multi-Scale Feature Compression for VCM
In the feature compression track of MPEG-VCM, multi-scale features extracted from images are subject to compression.
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals.
Ranked #1 on
Seizure Detection
on TUH EEG Seizure Corpus
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning
Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss.
Cost-effective Interactive Attention Learning with Neural Attention Processes
Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features.
Temporal Probabilistic Asymmetric Multi-task Learning
To capture such dynamically changing asymmetric relationships between tasks and long-range temporal dependencies in time-series data, we propose a novel temporal asymmetric multi-task learning model, which learns to combine features from other tasks at diverse timesteps for the prediction of each task.
