| Title | Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines. |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Feeny, AK, Rickard J, Patel D, Toro S, Trulock KM, Park CJ, LaBarbera MA, Varma N, Niebauer MJ, Sinha S, Gorodeski EZ, Grimm RA, Ji X, Barnard J, Madabhushi A, Spragg DD, Chung MK |
| Journal | Circulation. Arrhythmia and electrophysiology |
| Volume | 12 |
| Issue | 7 |
| Pagination | e007316 |
| Date Published | 2019 07 |
| ISSN | 1941-3084 |
| Keywords | Aged, Baltimore, Cardiac Resynchronization Therapy, Clinical Decision-Making, Decision Support Techniques, Disease Progression, Echocardiography, Female, Heart Failure, Heart Transplantation, Heart-Assist Devices, Humans, Machine Learning, Male, Middle Aged, Ohio, Patient Selection, Practice Guidelines as Topic, Predictive Value of Tests, Progression-Free Survival, Recovery of Function, Retrospective Studies, Risk Assessment, Risk Factors, Stroke Volume, Time Factors, Ventricular Function, Left |
| Abstract | Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines. |
| DOI | 10.1161/CIRCEP.119.007316 |
| PDF Link | |
| Alternate Journal | Circ Arrhythm Electrophysiol |
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