Natural language processing framework to assess clinical conditions.

By Henry Ware, C. Mullett, V. Jagannathan. 2009

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Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “[80] Self-Dev NA Narrative Medication, treatment, word bigrams, numerical features, synonym list Wei et al.”
Automatic lymphoma classification with sentence subgraph mining from pathology reports. (2014, background): “…recognizing obesity and its 15 comorbidities [19], the top four systems employed heavier feature engineering on hand-crafted rules that integrated “disease-specific, non-preventive medications and their brand names” [23], disease-related procedures [24], and disease-specific symptoms [25][26].”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, background): “domain of endocrine, nutritional, and metabolic diseases include negation detection and mention of family history in free-text notes [84] and assigning temporal tags to medical concepts [85]; obesity [25,86] and diabetes identification [77,87, 88, 89]; and diabetes complications such as foot examina”