title: A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.
abstract: Automated phenotype identification plays a critical role in cohort selection and bioinformatics data mining. Natural Language Processing (NLP)-informed classification techniques can robustly identify phenotypes in unstructured medical notes. In this paper, we systematically assess the effect of naive, lexically normalized, and semantic feature spaces on classifier performance for obesity, atherosclerotic cardiovascular disease (CAD), hyperlipidemia, hypertension, and diabetes. We train support vector machines (SVMs) using individual feature spaces as well as combinations of these feature spaces on two small training corpora (730 and 790 documents) and a combined (1520 documents) training corpus. We assess the importance of feature spaces and training data size on SVM model performance. We show that inclusion of semantically-informed features does not statistically improve performance for these models. The addition of training data has weak effects of mixed statistical significance across disease classes suggesting larger corpora are not necessary to achieve relatively high performance with these models.
authors.affiliation: Informatics Department, University at Albany, State University of New York, Albany, NY, USA. Electronic address: ckotfila@albany.edu.
authors.forename: Christopher
authors.initials: C
authors.lastname: Kotfila
authors.affiliation: Department of Information Studies, University at Albany, State University of New York, NY, USA.
authors.forename: Özlem
authors.initials: Ö
authors.lastname: Uzuner
mesh_terms: D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000453:epidemiology; D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration; D003920:Diabetes Mellitus / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D009323:Natural Language Processing; D009518:New York; D009765:Obesity / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods; D010641:Phenotype; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Classification; Natural language processing; Phenotyping