Reply: Deep Learning With Unsupervised Feature in Echocardiographic Imaging

Journal of the American College of Cardiology

Volumn 69, Issue 16, 2017, Pages 2101-2102

  Narula, Sukrit ^a   Shameer, Khader ^a   Salem Omar, Alaa Mabrouk ^a   Dudley, Joel T ^a   Sengupta, Partho P ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CLINICAL DECISION SUPPORT SYSTEM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PREDICTION; DEEP LEARNING; ECHOCARDIOGRAPHY; HEART FAILURE; HOSPITAL READMISSION; HUMAN; LETTER; MACHINE LEARNING; POINT OF CARE TESTING;

ALGORITHMS; DEEP LEARNING; ECHOCARDIOGRAPHY;

EID: 85018417228     PISSN: 07351097     EISSN: 15583597     Source Type: Journal    
DOI: 10.1016/j.jacc.2017.01.062     Document Type: Letter

References (4)

1. 1 Narula, S., Shameer, K., Salem Omar, A.M., Dudley, J.T., Sengupta, P.P., Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol 68 (2016), 2287–2295.
2. 2 Sengupta, P.P., Huang, Y.M., Bansal, M., et al. Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging, 9, 2016, e004330.
3. 3 Miotto, R., Li, L., Kidd, B.A., Dudley, J.T., Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep, 6, 2016, 26094.
4. 4 Shameer, K., Johnson, K.W., Yahi, A., et al. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai Heart Failure Cohort. Pac Symp Biocomput 22 (2016), 276–287.