Using EHRs for Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics

TitleUsing EHRs for Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics
Publication TypeBook Chapter
Year of Publication2015
AuthorsMaryam Panahiazar, Vahid Taslimitehrani, Naveen Pereira, Jyotishman Pathak
EditorRonald Cornet, Lăcrămioara Stoicu-Tivadar, Alexander Hörbst, Carlos Parra-Calderón, Stig Andersen, Mira Hercigonja-Szekeres
Book TitleDigital Healthcare Empowering Europeans: Proceedings of MIE 2015
Volume210
Series VolumeStudies in Health Technology and Informatics
Pagination369-373
PublisherIOS Press
ISSN Number978-1-61499-511-1
Keywordselectronic health records, heart failure, patient similarity
Abstract

Electronic Health Records (EHRs) contain a wealth of information about an individual patient's diagnosis, treatment and health outcomes. This information can be leveraged effectively to identify patients who are similar to each for disease diagnosis and prognosis. In recent years, several machine learning methods 1 have been proposed to assessing patient similarity, although the techniques have primarily focused on the use of patient diagnoses data from EHRs for the learning task. In this study, we develop a multidimensional patient similarity assessment technique that leverages multiple types of information from the EHR and predicts a medication plan for each new patient based on prior knowledge and data from similar patients. In our algorithm, patients have been clustered into different groups using a hierarchical clustering approach and subsequently have been assigned a medication plan based on the similarity index to the overall patient population. We evaluated the performance of our approach on a cohort of heart failure patients (N=1386) identified from EHR data at Mayo Clinic and achieved an AUC of 0.74. Our results suggest that it is feasible to harness population-based information from EHRs for an individual patient-specific assessment.

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