Search Results for author:
Found 10 papers, 8 papers with code
Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement
We show that FHLR achieves significantly better performance when learning from noisy labels and achieves state-of-the-art by a large margin, with up to 19% accuracy improvement under symmetric and asymmetric noise.
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images
To develop our dataset, we first construct two uni-modal resources: 1) The MIMIC-CXR-VQA dataset, our newly created medical visual question answering (VQA) benchmark, specifically designed to augment the imaging modality in EHR QA, and 2) EHRSQL (MIMIC-IV), a refashioned version of a previously established table-based EHR QA dataset.
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations.
ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs.
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models.
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning
To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks.
Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram
In this work, we propose an ECG pre-training method that learns both local and global contextual representations for better generalizability and performance on downstream tasks.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
To overcome this problem, we introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR.
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images
We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model.
