%0 Journal Article %1 johnson2018artificial %A Johnson, Kipp W. %A Soto, Jessica Torres %A Glicksberg, Benjamin S. %A Shameer, Khader %A Miotto, Riccardo %A Ali, Mohsin %A Ashley, Euan %A Dudley, Joel T. %D 2018 %J Journal of the American College of Cardiology %K ai cardiology %N 23 %P 2668 - 2679 %R https://doi.org/10.1016/j.jacc.2018.03.521 %T Artificial Intelligence in Cardiology %U http://www.sciencedirect.com/science/article/pii/S0735109718344085 %V 71 %X Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.

@article{johnson2018artificial, abstract = {Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.}, added-at = {2020-03-02T10:23:06.000+0100}, author = {Johnson, Kipp W. and Soto, Jessica Torres and Glicksberg, Benjamin S. and Shameer, Khader and Miotto, Riccardo and Ali, Mohsin and Ashley, Euan and Dudley, Joel T.}, biburl = {https://www.bibsonomy.org/bibtex/200b599fee56919053b56c87bbf311afb/nosebrain}, description = {Artificial Intelligence in Cardiology - ScienceDirect}, doi = {https://doi.org/10.1016/j.jacc.2018.03.521}, interhash = {0937e653e7174b6c21704ad12ef66d05}, intrahash = {00b599fee56919053b56c87bbf311afb}, issn = {0735-1097}, journal = {Journal of the American College of Cardiology}, keywords = {ai cardiology}, number = 23, pages = {2668 - 2679}, timestamp = {2020-03-02T10:23:06.000+0100}, title = {Artificial Intelligence in Cardiology}, url = {http://www.sciencedirect.com/science/article/pii/S0735109718344085}, volume = 71, year = 2018 }