4 research outputs found
ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
Question answering (QA) in the field of healthcare has received much
attention due to significant advancements in natural language processing.
However, existing healthcare QA datasets primarily focus on medical images,
clinical notes, or structured electronic health record tables. This leaves the
vast potential of combining electrocardiogram (ECG) data with these systems
largely untapped. To address this gap, we present ECG-QA, the first QA dataset
specifically designed for ECG analysis. The dataset comprises a total of 70
question templates that cover a wide range of clinically relevant ECG topics,
each validated by an ECG expert to ensure their clinical utility. As a result,
our dataset includes diverse ECG interpretation questions, including those that
require a comparative analysis of two different ECGs. In addition, we have
conducted numerous experiments to provide valuable insights for future research
directions. We believe that ECG-QA will serve as a valuable resource for the
development of intelligent QA systems capable of assisting clinicians in ECG
interpretations.Comment: 39 pages (9 pages for main text, 2 pages for references, 28 pages for
supplementary materials
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
We present a new text-to-SQL dataset for electronic health records (EHRs).
The utterances were collected from 222 hospital staff, including physicians,
nurses, insurance review and health records teams, and more. To construct the
QA dataset on structured EHR data, we conducted a poll at a university hospital
and templatized the responses to create seed questions. Then, we manually
linked them to two open-source EHR databases, MIMIC-III and eICU, and included
them with various time expressions and held-out unanswerable questions in the
dataset, which were all collected from the poll. Our dataset poses a unique set
of challenges: the model needs to 1) generate SQL queries that reflect a wide
range of needs in the hospital, including simple retrieval and complex
operations such as calculating survival rate, 2) understand various time
expressions to answer time-sensitive questions in healthcare, and 3)
distinguish whether a given question is answerable or unanswerable based on the
prediction confidence. We believe our dataset, EHRSQL, could serve as a
practical benchmark to develop and assess QA models on structured EHR data and
take one step further towards bridging the gap between text-to-SQL research and
its real-life deployment in healthcare. EHRSQL is available at
https://github.com/glee4810/EHRSQL.Comment: Published as a conference paper at NeurIPS 2022 (Track on Datasets
and Benchmarks)
Machine Learning Improves the Prediction Rate of Non-Curative Resection of Endoscopic Submucosal Dissection in Patients with Early Gastric Cancer
Non-curative resection (NCR) of early gastric cancer (EGC) after endoscopic submucosal dissection (ESD) can increase the burden of additional treatment and medical expenses. We aimed to develop a machine-learning (ML)-based NCR prediction model for EGC prior to ESD. We obtained data from 4927 patients with EGC who underwent ESD between January 2006 and February 2020. Ten clinicopathological characteristics were selected using extreme gradient boosting (XGBoost) and were used to develop a ML-based model. Dataset was divided into the training and internal validation sets and verified using an external validation set. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were evaluated. The performance of each model was compared by using the Delong test. A total of 1100 (22.1%) patients were identified as being treated non-curatively with ESD. Seven ML-based NCR prediction models were developed. The performance of NCR prediction was highest in the XGBoost model (AUROC, 0.851; 95% confidence interval, 0.837–0.864). When we compared the prediction performance by the Delong test, XGBoost (p = 0.02) and support vector machine (p = 0.02) models showed a significantly higher performance among the NCR prediction models. We developed an ML model capable of accurately predicting the NCR of EGC before ESD. This ML model can provide useful information for decision-making regarding the appropriate treatment of EGC before ESD