What can Natural Language Processing do for Clinical Decision Support?

By Dina Demner-Fushman, Wendy W. Chapman, Clement J. McDonald. Journal of Biomedical Informatics, 2009

680 citations recorded by the source corpus.

Computerized Clinical Decision Support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. Natural Language Processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5b73be2f2373f9f6bf3d3137bcdc7b8065901746

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Citations in the corpus

How essential are unstructured clinical narratives and information fusion to clinical trial recruitment? (2014, methodology): “These match functions are computed for two levels of analysis - (1) medical concept-level, where we compare all the medical concepts in the trials dataset against the structured and unstructured data, and (2) eligibility criteria level, where we compare all the medical concepts in each criterion aga”
Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management (2013, background): “Automated text classification techniques extract structured information from narrative clinical text, empowering novel secondary uses of unstructured data [1,2].”
Mining electronic health records: towards better research applications and clinical care (2012, background): “For complex rules or when the necessary information is not available in structured form, NLP-based processing of text may supplement structured data points 34 .”
NAREOR: The Narrative Reordering Problem (2021, neutral): “Systems that can generate event timelines for clinical narratives, e.g. admission notes and physical reports, is important for applications like medical document summarization (Bram-sen et al. 2006; Reichert et al. 2010) and clinical decision making (Demner-Fushman, Chapman, and McDonald 2009).”
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.”
Two-stage Federated Phenotyping and Patient Representation Learning (2019, background): “Converting the unstructured data into useful structured representations will not only help data analysis but also improve efﬁciency in clinical practice (Jagan-nathan et al., 2009; Kreimeyer et al., 2017; Ford et al., 2016; Demner-Fushman et al., 2009; Murff et al., 2011; Friedman et al., 2004).”
emrQA: A Large Corpus for Question Answering on Electronic Medical Records (2018, background): “…in facts, abundant use of domain-specific terminology, temporal narratives with multiple related diseases, symptoms, medications that go back and forth in time, and misspellings, make it complex and difficult to apply existing NLP tools (Demner-Fushman et al., 2009; Raghavan and Patwardhan, 2016).”
A clinical text classification paradigm using weak supervision and deep representation (2019, background): “It has been shown effective in the clinical domain due to the clinical sublanguage characteristics [23].”
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances (2018, neutral): “Thus NLP approaches both enable the improvement of case identification from health records [46,47] and can provide a much richer set of data than could be achieved by the use of structured data alone.”
Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review (2018, neutral): “Future protocol development will bene ﬁ t from the use of arti ﬁ cial intelligence tools, such as natural language processing [2,17,32,53], which will be able to extract meaningful information across published documents and present systematically organized data to the study designer for…”