emrKBQA: A Clinical Knowledge-Base Question Answering Dataset
Preethi Raghavan, Jennifer J. Liang, Diwakar Mahajan, Rachita Chandra, Peter Szolovits
Abstract
We present emrKBQA, a dataset for answering physician questions from a structured patient record. It consists of questions, logical forms and answers. The questions and logical forms are generated based on real-world physician questions and are slot-filled and answered from patients in the MIMIC-III KB This community-shared release consists of over 940000 question, logical form and answer triplets with 389 types of questions and 7.5 paraphrases per question type. We perform experiments to validate the quality of the dataset and set benchmarks for question to logical form learning that helps answer questions on this dataset.
Topics & Concepts
Question answeringComputer scienceKnowledge baseSet (abstract data type)Quality (philosophy)Natural language processingProcess (computing)Information retrievalArtificial intelligenceBase (topology)Logical consequenceEpistemologyProgramming languageMathematicsPhilosophyMathematical analysisTopic ModelingNatural Language Processing TechniquesMultimodal Machine Learning Applications