Medical Question Answering for Clinical Decision Support
By Travis R. Goodwin, Sanda M. Harabagiu. 2016
69 citations recorded by the source corpus.
The goal of modern Clinical Decision Support (CDS) systems is to provide physicians with information relevant to their management of patient care. When faced with a medical case, a physician asks questions about the diagnosis, the tests, or treatments that should be administered. Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found. Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet sufficiently promising. In this paper, we present a novel framework for answering medical questions in the spirit of TREC-CDS by first discovering the answer and then selecting and ranking scientific articles that contain the answer. Answer discovery is the result of probabilistic inference which operates on a probabilistic knowledge graph, automatically generated by processing the medical language of large collections of electronic medical records (EMRs). The probabilistic inference of answers combines knowledge from medical practice (EMRs) with knowledge from medical research (scientific articles). It also takes into account the medical knowledge automatically discerned from the medical case description. We show that this novel form of medical question answering (Q/A) produces very promising results in (a) identifying accurately the answers and (b) it improves medical article ranking by 40\%.
Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology
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Citations in the corpus
- Reinforcement Learning in Healthcare: A Survey (2019, background): “series of questions about their symptoms, and then attempts to diagnose some potential diseases [246].”
- Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning (2017, background): “Other works have explored graph-based reasoning methods to incorporate relevant medical concepts and their associations (Shi et al., 2017; Geng and Zhang, 2014; Goodwin and Harabagiu, 2016; Zheng and Wan, 2016; Ling et al., 2017a).”
- Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study (2017, background): “…(CDS) tracks1, clinical diagnosis inferencing from free text clinical narratives has been showcased as a significant milestone in clinical question answering and a path to improving the accuracy of relevant biomedical article retrieval (Roberts et al., 2015, 2016; Goodwin and Harabagiu, 2016).”