A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.
Journal:
Journal of biomedical informatics
Published Date:
Aug 1, 2015
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
Keywords
Cardiovascular Diseases
Data Mining
Decision Support Systems, Clinical
Diabetes Mellitus
Diagnosis, Computer-Assisted
Electronic Health Records
Humans
Natural Language Processing
New York
Obesity
Pattern Recognition, Automated
Phenotype
Reproducibility of Results
Sensitivity and Specificity
Support Vector Machine