Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

By M. Motwani, Damini Dey, D. Berman, G. Germano, S. Achenbach, M. Al-Mallah, D. Andreini, M. Budoff, Filippo Cademartiri, T. Callister and 26 more. European Heart Journal, 2016

663 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4973d4fc2b20a9b8c83d897c7de2e79cb980daa9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. T. Marwick
2. Jeroen J. Bax
3. J. Halperin
4. James K. Min
5. Andrew Y. Ng

In Radiology, Nuclear Medicine and Imaging

1. T. Marwick
2. M. Ahsan
3. Zahed Siddique
4. Andrew Y. Ng

Citations in the corpus

• Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, background): “(2017) [27] earned the most citations (257), with 51.”
• Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “79) for the prediction of death compared with established risk indexes and visual CTA assessment (Figure 4) (23).”
• Improving palliative care with deep learning (2017, background): “ning techniques on high dimensional data provides a unique opportunity to make contributions, especially in disease prognosis [30] [31]. All the tools described above, and those we reviewed [32] [33] [34] [35] [36], have at least one of the following limitations. They were either derived from small ”
• Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, background): “4,5 Altogether, the potential for ML to fundamentally change the way we practice medicine is now well-appreciated.”
• Deep learning for cardiovascular medicine: a practical primer. (2019, methodology): “Motwani et al. 57 used DL on CT angiography in 10 300 patients with suspected coronary disease to improve prediction of 5year all-cause mortality over existing CT or 58 In a novel study applying DL to retinal fundus photographs trained on 284 335 patients, DL predicted MACE with c-statistic 0.70 in…”