Promise and Frustration: Machine Learning in Cardiology

Author:

Fornwalt Brandon K.12ORCID,Pfeifer John M.13

Affiliation:

1. Department of Translational Data Science and Informatics, Geisinger, Danville, PA (B.K.F., J.M.P.).

2. Department of Radiology and the Heart Institute, Geisinger, Danville, PA (B.K.F.).

3. Heart and Vascular Center, Evangelical Hospital, Lewisburg, PA (J.M.P.).

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine,Radiology Nuclear Medicine and imaging

Reference8 articles.

1. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

2. Deep Learning–Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution

3. Centers for Medicare and Medicaid Services. Medicare Coverage of Innovative Technology (MCIT) and Definition of “Reasonable and Necessary”. Federal Register National Archives. URL https://www.federalregister.gov/documents/2020/09/01/2020-19289/medicare-program-medicare-coverage-of-innovative-technology-mcit-and-definition-of-reasonable-and. Published 9/1/2020. Updated 9/1/2020. Accessed 5/1/2021.

4. Centers for Medicare and Medicaid Services. Medicare Program; Medicare Coverage of Innovative Technology (MCIT) and Definition of “Reasonable and Necessary”; Delay of Effective Date; Public Comment Period. Federal Register National Archives. URL https://www.federalregister.gov/documents/2021/03/17/2021-05490/medicare-program-medicare-coverage-of-innovative-technology-mcit-and-definition-of-reasonable-and. Published 3/17/2021. Updated 3/17/2021. Accessed 5/1/2021.

5. Big data and black-box medical algorithms.;Nicholson Price W;Sci Transl Med,2018

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