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Cited by in F6Publishing
For: Miura K, Goto S, Katsumata Y, Ikura H, Shiraishi Y, Sato K, Fukuda K. Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data. NPJ Digit Med 2020;3:141. [PMID: 33145437 DOI: 10.1038/s41746-020-00348-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Medical Informatics. [DOI: 10.2196/38454] [Reference Citation Analysis]
2 Amelard R, Hedge ET, Hughson RL. Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities. NPJ Digit Med 2021;4:156. [PMID: 34764446 DOI: 10.1038/s41746-021-00531-3] [Reference Citation Analysis]
3 Kainz B, Heinrich MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Deane C, Mischkewitz S, Al-Noor F, Rawdin AC, Ruttloff A, Stevenson MD, Klein-Weigel P, Curry N. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med 2021;4:137. [PMID: 34526639 DOI: 10.1038/s41746-021-00503-7] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
4 Chalitsios C, Nikodelis T, Konstantakos V, Kollias I. Sensitivity of movement features to fatigue during an exhaustive treadmill run. Eur J Sport Sci 2021;:1-9. [PMID: 34256682 DOI: 10.1080/17461391.2021.1955015] [Reference Citation Analysis]
5 Bulaj G, Clark J, Ebrahimi M, Bald E. From Precision Metapharmacology to Patient Empowerment: Delivery of Self-Care Practices for Epilepsy, Pain, Depression and Cancer Using Digital Health Technologies. Front Pharmacol 2021;12:612602. [PMID: 33972825 DOI: 10.3389/fphar.2021.612602] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
6 Seki Y, Nakashima D, Shiraishi Y, Ryuzaki T, Ikura H, Miura K, Suzuki M, Watanabe T, Nagura T, Matsumato M, Nakamura M, Sato K, Fukuda K, Katsumata Y. A novel device for detecting anaerobic threshold using sweat lactate during exercise. Sci Rep 2021;11:4929. [PMID: 33654133 DOI: 10.1038/s41598-021-84381-9] [Cited by in F6Publishing: 8] [Reference Citation Analysis]