title: A rule-based approach for identifying obesity and its comorbidities in medical discharge summaries.
abstract: OBJECTIVE Evaluate the effectiveness of a simple rule-based approach in classifying medical discharge summaries according to indicators for obesity and 15 associated co-morbidities as part of the 2008 i2b2 Obesity Challenge. METHODS The authors applied a rule-based approach that looked for occurrences of morbidity-related keywords and identified the types of assertions in which those keywords occurred. The documents were then classified using a simple scoring algorithm based on a mapping of the assertion types to possible judgment categories. MEASUREMENTS RESULTS for the challenge were evaluated based on macro F-measure. We report micro and macro F-measure results for all morbidities combined and for each morbidity separately. Results Our rule-based approach achieved micro and macro F-measures of 0.97 and 0.77, respectively, ranking fifth out of the entries submitted by 28 teams participating in the classification task based on textual judgments and substantially outperforming the average for the challenge. CONCLUSIONS As shown by its ranking in the challenge results, this approach performed relatively well under conditions in which limited training data existed for some judgment categories. Further, the approach held up well in relation to more complex approaches applied to this classification task. The approach could be enhanced by the addition of expert rules to model more complex medical reasoning.
authors.affiliation: Centers for Disease Control and Prevention, 1600 Clifton Rd, Mail Stop E76, Atlanta, GA, USA. nmishra@cdc.gov
authors.forename: Ninad K
authors.initials: NK
authors.lastname: Mishra
authors.forename: David M
authors.initials: DM
authors.lastname: Cummo
authors.forename: James J
authors.initials: JJ
authors.lastname: Arnzen
authors.forename: Jason
authors.initials: J
authors.lastname: Bonander
mesh_terms: D015897:Comorbidity; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D009765:Obesity; D010351:Patient Discharge