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Found 8 papers, 6 papers with code
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.
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain.
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.
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
We then manually linked these questions to two open-source EHR databases, MIMIC-III and eICU, and included various time expressions and held-out unanswerable questions in the dataset, which were also collected from the poll.
Graph-Text Multi-Modal Pre-training for Medical Representation Learning
MedGTX uses a novel graph encoder to exploit the graphical nature of structured EHR data, and a text encoder to handle unstructured text, and a cross-modal encoder to learn a joint representation space.
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records
In addition, for a reliable EHR-QA model, we apply the uncertainty decomposition method to measure the ambiguity in the input question.
Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture
An intelligent machine that can answer human questions based on electronic health records (EHR-QA) has a great practical value, such as supporting clinical decisions, managing hospital administration, and medical chatbots.
