Aceso: PICO-Guided Evidence Summarization on Medical Literature
Xiang Zhang, Ping Geng, Tengteng Zhang, Qian Lu, Peng Gao, Jing Mei
Abstract
Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> system, which automatically generates PICO-based evidence summaries from medical literature. In Aceso, we adopt an active learning paradigm, which helps to minimize the cost of manual labeling and to optimize the quality of summarization with limited labeled data. An UMLS2Vec model is proposed to learn a vector representation of medical concepts in UMLS, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and we fuse the embedding of medical knowledge with textual features in summarization. The evaluation shows that our approach is better on identifying PICO sentences against state-of-the-art studies and outperforms baseline methods on producing high-quality evidence summaries.