Answering Clinical Questions with Knowledge-Based and Statistical Techniques

By Dina Demner-Fushman, Jimmy J. Lin. International Conference on Computational Logic, 2007

308 citations recorded by the source corpus.

The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline.

Subfields: Artificial Intelligence, General Health Professions, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9e89e07ad1b5c8b47d6543dbf3795601a48b6fd0

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Citations in the corpus

CliCR: a Dataset of Clinical Case Reports for Machine Reading Comprehension (2018, methodology): “This task can be facilitated by summarization and QA systems (Demner-Fushman and Lin, 2007; Demner-Fushman et al., 2009), and we believe, by fine-grained machine reading.”
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature (2018, background): “Other work attempts to bypass basic extraction tasks and address more complex biomedical QA and (multi-document) summarization problems to support EBM (Demner-Fushman and Lin, 2007; Moll´a and Santiago-Martinez, 2011; Abacha and Zweigenbaum, 2015).”
Information Retrieval: A Biomedical and Health Perspective (2020, background): “Bayesian factors may be particularly important, as a study with a hypothesis that a priori is unlikely (or highly likely) will only have its probability of being correct increased a small (or large) amount when results from an experiment with a p-value indicating low likelihood of chance is obtained”
Automatically Generating Wikipedia Articles: A Structure-Aware Approach (2009, methodology): “These domains have been commonly used in prior work on summarization (Weischedel et al., 2004; Zhou et al., 2004; Filatova and Prager, 2005; Demner-Fushman and Lin, 2007; Biadsy et al., 2008).”
Automatic classification of sentences to support Evidence Based Medicine (2011, methodology): “It is important to note that this is the only previous work in the literature that uses the Other tag as we do. Demner-Fushman and Lin [ 7 ] also applied their final PICO classifiers to a novel weighting formula for medical information retrieval (IR), significantly improving the baseline for the tas”
Frontiers of biomedical text mining: current progress (2007, background): “LitLinker differs from BITOLA ([108], see below) in the statistical processing it performs.”
A survey on question answering technology from an information retrieval perspective (2011, methodology): “[37] with a QA system for structured knowledge sources; Demner-Fushman and Lin [30] with a clinical question answering system; Basili et al.”
Open domain question answering using Wikipedia-based knowledge model (2014, background): “On the other hand, specialized QA systems have relied on well-structured knowledge bases in specific domains (Demner-Fushman & Lin, 2007; Frank et al., 2007).”