Abstract
Abstract Objective A large amount of clinical data are stored in clinical notes that frequently contain spelling variations, typos, local practice-generated acronyms, synonyms, and informal words. Instead of relying on established but infrequently updated ontologies with keywords limited to formal language, we developed an artificial intelligence (AI) assistant (named “DeepSuggest”) that interactively offers suggestions to expand or pivot queries to help overcome these challenges. Methods We applied an unsupervised neural network (Word2Vec) to the clinical notes to build keyword contextual similarity matrix. With a user's input query, DeepSuggest generates a list of relevant keywords, including word variations (e.g., formal or informal forms, synonyms, abbreviations, and misspellings) and other relevant words (e.g., related diagnosis, medications, and procedures). Human intelligence is then used to further refine or pivot their query. Results DeepSuggest learns the semantic and linguistic relationships between the words from a large collection of local notes. Although DeepSuggest is only able to recall 0.54 of Systematized Nomenclature of Medicine (SNOMED) synonyms on average among the top 60 suggested terms, it covers the semantic relationship in our corpus for a larger number of raw concepts (6.3 million) than SNOMED ontology (24,921) and is able to retrieve terms that are not stored in existing ontologies. The precision for the top 60 suggested words averages at 0.72. Usability test resulted that DeepSuggest is able to achieve almost twice the recall on clinical notes compared with Epic (average of 5.6 notes retrieved by DeepSuggest compared with 2.6 by Epic). Conclusion DeepSuggest showed the ability to improve retrieval of relevant clinical notes when implemented on a local corpus by suggesting spelling variations, acronyms, and semantically related words. It is a promising tool in helping users to achieve a higher recall rate for clinical note searches and thus boosting productivity in clinical practice and research. DeepSuggest can supplement established ontologies for query expansion.
Highlights
Keyword-driven search of clinical notes greatly expedites retrieval of medical information beyond manual chart review to serve the needs of patient care, quality improvement, and clinical research.[1,2,3] While searching notes may appear to be simple and quick, determining the optimal set of query keywords is not straightforward
Instead of relying on established but infrequently updated ontologies with keywords limited to formal language, we developed an artificial intelligence (AI) assistant that interactively offers suggestions to expand or pivot queries to help overcome these challenges
DeepSuggest is only able to recall 0.54 of Systematized Nomenclature of Medicine (SNOMED) synonyms on average among the top 60 suggested terms, it covers the semantic relationship in our corpus for a larger number of raw concepts (6.3 million) than SNOMED ontology (24,921) and is able to retrieve terms that are not stored in existing ontologies
Summary
Keyword-driven search of clinical notes greatly expedites retrieval of medical information beyond manual chart review to serve the needs of patient care, quality improvement, and clinical research.[1,2,3] While searching notes may appear to be simple and quick, determining the optimal set of query keywords is not straightforward. When searching notes for tonsillectomy patients, using “tonsillectomy” can miss notes containing “tonsilectomy” (common misspelling), “T/A” and “T&A” (nonstandard but commonly used abbreviations), or “adenotonsillectomy” (semantically related concept) This vocabulary mismatch between the query words and the actual words used in target documents might best be resolved by expanding the query with relevant options.[4,5] Since human recall of synonyms is usually poor, medical ontologies have often been used to assist the expansion of the original query, either interactively[6] or algorithmically.[7] Ontology-driven query expansion strategies have long been adopted by both academic and commercial implementations, such as EMERSE (Michigan University, United States),[8,9] CISearch (Columbia University, United States),[1] SemEHR (King’s College London, United Kingdom),[6] EpicCare (Epic, United States), and Cerner PowerChart (Cerner, United States). Ontologydriven approaches lack timely updates due to the high curation costs for such efforts.[10]
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