Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44732, first published .
Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning–Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders

Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning–Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders

Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning–Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders

Journals

  1. Arutyunov G, Tarlovskaya E, Arutyunov A, Batluk T, Koziolova N, Chesnikova A, Vaskin A, Tokmin D, Bakulin I, Barbarash O, Grigoryeva N, Gubareva I, Izmozherova N, Kamilova U, Kechedzhieva S, Kim Z, Koriagina N, Mironova S, Mitkovskaya N, Nemirova S, Nurieva L, Petrova M, Polyanskaya E, Rebrov A, Svarovskaya A, Smirnova E, Sugraliev A, Khovaeva Y, Shavkuta G, Shaposhnik I, Alieva M, Almukhanova A, Aparkina A, Bashkinov R, Belousova L, Blokhina E, Bochkareva V, Buianova M, Valikulova F, Vende A, Galyavich A, Genkel V, Gorbunova E, Gordeychuk E, Grigorenko E, Grigoryeva E, Davydkin I, Evdokimov D, Ermilova A, Zhangelova S, Zhdankina N, Zheleznyak E, Ilyanok N, Kapsultanova D, Karoli N, Kartashova E, Kuznetsova A, Kumaritova A, Magdeeva N, Makarov S, Melnikov E, Novikova M, Obukhova I, Ponomarenko E, Rubanenko A, Rubanenko O, Rustamova F, Safronenko V, Suchkova E, Sycheva A, Tagaeva D, Trubnikova M, Trunina T, Frolov A, Khatlamadzhiyan V, Khokhlova Y, Chernyavina A, Chizhova O, Shambatov M, Shnyukova T, Shchukin Y. Peculiarities of polyvascular disease and the diagnostic significance of the ankle-brachial index in patients with coronary artery disease: results from the real-world registry KAMMA (Clinical registry on patient population with polyvascular disease in the Russian Federation and Eurasian countries). Russian Journal of Cardiology 2024;29(4):5837 View
  2. Vision Paul V, Masood J. Exploring Predictive Methods for Cardiovascular Disease: A Survey of Methods and Applications. IEEE Access 2024;12:101497 View
  3. Awada I, Florea A, Scafa-Udriște A. An e-learning platform for clinical reasoning in cardiovascular diseases: a study reporting on learner and tutor satisfaction. BMC Medical Education 2024;24(1) View
  4. Grant J, Javaid A, Carrick R, Koester M, Kassamali A, Kim C, Isakadze N, Wu K, Blaha M, Whelton S, Arbab-Zadeh A, Orringer C, Blumenthal R, Martin S, Marvel F. Digital health innovation and artificial intelligence in cardiovascular care: a case-based review. npj Cardiovascular Health 2024;1(1) View