TY - GEN
T1 - Using EHRs for Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics
AU - Panahiazar, Maryam
AU - Taslimitehrani, Vahid
AU - Pereira, Naveen L.
AU - Pathak, Jyotishman
N1 - Publisher Copyright:
© 2015 European Federation for Medical Informatics (EFMI).
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - electronic health records
KW - heart failure
KW - patient similarity
UR - http://www.scopus.com/inward/record.url?scp=84937422357&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937422357&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-512-8-369
DO - 10.3233/978-1-61499-512-8-369
M3 - Conference contribution
C2 - 25991168
AN - SCOPUS:84937422357
T3 - Studies in Health Technology and Informatics
SP - 369
EP - 373
BT - Digital Healthcare Empowering Europeans - Proceedings of MIE 2015
A2 - Cornet, Ronald
A2 - Stoicu-Tivadar, Lacramioara
A2 - Cornet, Ronald
A2 - Parra Calderon, Carlos Luis
A2 - Andersen, Stig Kjaer
A2 - Horbst, Alexander
A2 - Hercigonja-Szekeres, Mira
PB - IOS Press
T2 - 26th Medical Informatics in Europe Conference, MIE 2015
Y2 - 27 May 2015 through 29 May 2015
ER -