%0 Journal Article %1 attia2019artificial %A Attia, Zachi I %A Noseworthy, Peter A %A Lopez-Jimenez, Francisco %A Asirvatham, Samuel J %A Deshmukh, Abhishek J %A Gersh, Bernard J %A Carter, Rickey E %A Yao, Xiaoxi %A Rabinstein, Alejandro A %A Erickson, Brad J %A Kapa, Suraj %A Friedman, Paul A %B The Lancet %D 2019 %I Elsevier %J The Lancet %K atrial clinic convnet ecg fibrillation paper:dzhi-ecg %N 10201 %P 861--867 %R 10.1016/S0140-6736(19)31721-0 %T An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction %V 394 %X BackgroundAtrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.

@article{attia2019artificial, abstract = {BackgroundAtrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.}, added-at = {2020-01-21T20:52:12.000+0100}, author = {Attia, Zachi I and Noseworthy, Peter A and Lopez-Jimenez, Francisco and Asirvatham, Samuel J and Deshmukh, Abhishek J and Gersh, Bernard J and Carter, Rickey E and Yao, Xiaoxi and Rabinstein, Alejandro A and Erickson, Brad J and Kapa, Suraj and Friedman, Paul A}, biburl = {https://www.bibsonomy.org/bibtex/2d56b9a9ed0ba012d2a3c0e172d65448e/nosebrain}, booktitle = {The Lancet}, comment = {doi: 10.1016/S0140-6736(19)31721-0}, doi = {10.1016/S0140-6736(19)31721-0}, interhash = {16404358dc5078ff15badb32aeadece8}, intrahash = {d56b9a9ed0ba012d2a3c0e172d65448e}, issn = {01406736}, journal = {The Lancet}, keywords = {atrial clinic convnet ecg fibrillation paper:dzhi-ecg}, month = sep, number = 10201, pages = {861--867}, publisher = {Elsevier}, timestamp = {2020-08-12T20:03:41.000+0200}, title = {An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction}, volume = 394, year = 2019 }