2019
DOI: 10.5195/jmla.2019.758
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Text mining for clinical support

Abstract: Background: In 2013, the Dahlgren Memorial Library (DML) at the Georgetown University Medical Center began using text mining software to enable its clinical informationists to quickly retrieve specific, relevant information from MEDLINE abstracts while on patient rounds.Description: In 2013, DML licensed the use of the Linguamatics I2E text-mining program, and DML’s clinical informationist began using it to text mine MEDLINE abstracts on patient rounds. In 2015, DML installed I2E on a server at Georgetown and … Show more

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Cited by 10 publications
(4 citation statements)
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“…We focused on four clinical note types (“History and Physical,” “ED Provider,” “ED Progress,” and “Consult History and Physical”) and extracted unstructured text following five note header types (“History of Present Illness,” “Patient Presents With,” “HPI,” “HPI Comments,” and Summary”). We used existing signs and symptoms ontologies generated by Systematized Nomenclature of Medicine and National Cancer Institute to identify relevant terms, based on their implementation within the I2E natural language processing software (Linguamatics I2E 5.4.1R13, Cambridge, UK) ( 31 36 ). We iteratively developed documentation queries to account for symptom negation and common text patterns like comma-separated lists.…”
Section: Methodsmentioning
confidence: 99%
“…We focused on four clinical note types (“History and Physical,” “ED Provider,” “ED Progress,” and “Consult History and Physical”) and extracted unstructured text following five note header types (“History of Present Illness,” “Patient Presents With,” “HPI,” “HPI Comments,” and Summary”). We used existing signs and symptoms ontologies generated by Systematized Nomenclature of Medicine and National Cancer Institute to identify relevant terms, based on their implementation within the I2E natural language processing software (Linguamatics I2E 5.4.1R13, Cambridge, UK) ( 31 36 ). We iteratively developed documentation queries to account for symptom negation and common text patterns like comma-separated lists.…”
Section: Methodsmentioning
confidence: 99%
“…"Text mining" refers to the use of natural language processing (NLP) techniques to extract facts, relationships, and opinions from a text [32]. With the rise of social media, massive amounts of text data have emerged on the web, and the study of social media content based on text mining has received attention from scholars.…”
Section: Text Mining In Online Consultation Platformmentioning
confidence: 99%
“…According to Hartmann et al, 2019, text mining is divided into two classes of machine learning (ML) and lexicon-based methods [29]. In order to make the best at selecting either of these approaches, one should be equipped with the subjective knowledge over research objectives as well as computational and data analysis skills.…”
Section: Background Of Text Miningmentioning
confidence: 99%