TY - GEN
T1 - Application of machine learning for detection of hypertrophic cardiomyopathy patients from echocardiogram measurements
AU - Farahani, Nasibeh Zanjirani
AU - Enayati, Moein
AU - Sundaram, Divaakar Siva Baala
AU - Damani, Devanshi
AU - Kaggal, Vinod C.
AU - Zacher, April L.
AU - Geske, Jeffrey B.
AU - Kane, Garvan
AU - Arunachalam, Shivaram Poigai
AU - Pasupathy, Kalyan
AU - Arruda-Olson, Adelaide M.
N1 - Publisher Copyright:
© 2021 by ASME.
PY - 2021
Y1 - 2021
N2 - Hypertrophic cardiomyopathy (HCM) is a heritable, phenotypically diverse, and often asymptomatic heart muscle disease which is a major cause of sudden cardiac death (SCD) in young adults. The gold-standard for the diagnosis of HCM is echocardiography (echo), which is an ultrasound-based cardiac imaging modality. Across all sites of the Mayo Clinic enterprise, echo images and measurement data are reviewed, interpreted, and reported via the Echo Information Management System (EIMS). The objective of this paper is to develop a machine learning model for the identification of HCM from cardiac measurements obtained by the echo. We developed a novel machine learning model on patient demographic information and echo measurements that were retrieved from the EIMS digital data registry and selected by cardiologists. Random forest (RF) was utilized to investigate the predictive performance of these features on the identification of HCM patients. The HCM cohort consists of 3,548 patients with at least one HCM diagnostic billing code (ICD-9 or ICD-10), from 2014 to 2019. The class labels HCM yes and HCM no were assigned by manual review of medical records as well as the outcomes of the gold standard imaging tests for HCM diagnosis. The developed model performed well in finding HCM patients with an accuracy of 95%, recall of 99%, and precision of 97%. The F1 score was 98 %, while 4% of patients were misclassified. This model will be translated into clinical practice for a clinical decision support system in EIMS to assist providers in the accurate diagnosis of HCM from echo data automatically while ensuring high-quality echo interpretation.
AB - Hypertrophic cardiomyopathy (HCM) is a heritable, phenotypically diverse, and often asymptomatic heart muscle disease which is a major cause of sudden cardiac death (SCD) in young adults. The gold-standard for the diagnosis of HCM is echocardiography (echo), which is an ultrasound-based cardiac imaging modality. Across all sites of the Mayo Clinic enterprise, echo images and measurement data are reviewed, interpreted, and reported via the Echo Information Management System (EIMS). The objective of this paper is to develop a machine learning model for the identification of HCM from cardiac measurements obtained by the echo. We developed a novel machine learning model on patient demographic information and echo measurements that were retrieved from the EIMS digital data registry and selected by cardiologists. Random forest (RF) was utilized to investigate the predictive performance of these features on the identification of HCM patients. The HCM cohort consists of 3,548 patients with at least one HCM diagnostic billing code (ICD-9 or ICD-10), from 2014 to 2019. The class labels HCM yes and HCM no were assigned by manual review of medical records as well as the outcomes of the gold standard imaging tests for HCM diagnosis. The developed model performed well in finding HCM patients with an accuracy of 95%, recall of 99%, and precision of 97%. The F1 score was 98 %, while 4% of patients were misclassified. This model will be translated into clinical practice for a clinical decision support system in EIMS to assist providers in the accurate diagnosis of HCM from echo data automatically while ensuring high-quality echo interpretation.
KW - Decision support system
KW - Echocardiography
KW - Hypertrophic cardiomyopathy
KW - Machine learning
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85107232143&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107232143&partnerID=8YFLogxK
U2 - 10.1115/DMD2021-1078
DO - 10.1115/DMD2021-1078
M3 - Conference contribution
AN - SCOPUS:85107232143
T3 - Proceedings of the 2021 Design of Medical Devices Conference, DMD 2021
BT - Proceedings of the 2021 Design of Medical Devices Conference, DMD 2021
PB - American Society of Mechanical Engineers
T2 - 2021 Design of Medical Devices Conference, DMD 2021
Y2 - 12 April 2021 through 15 April 2021
ER -