title: A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.
abstract: In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies. We then utilize these clusters as features to predict patient severity of condition. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 patients. We compare our cluster-based feature set with another that incorporates the Charlson comorbidity score as a feature and demonstrate an accuracy improvement of up to 14% in the predictability of the severity of condition.
authors.forename: Costas
authors.initials: C
authors.lastname: Sideris
authors.forename: Nabil
authors.initials: N
authors.lastname: Alshurafa
authors.forename: Mohammad
authors.initials: M
authors.lastname: Pourhomayoun
authors.forename: Farhad
authors.initials: F
authors.lastname: Shahmohammadi
authors.forename: Lauren
authors.initials: L
authors.lastname: Samy
authors.forename: Majid
authors.initials: M
authors.lastname: Sarrafzadeh
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057286:Electronic Health Records; D006333:Heart Failure / Q000175:diagnosis* / Q000453:epidemiology*; D006801:Humans; D007297:Inpatients; D038801:International Classification of Diseases; D008962:Models, Theoretical; D010351:Patient Discharge; D015203:Reproducibility of Results