| 1 |
Aljizeeri A, Al‐mallah MH. The Role of Noninvasive Cardiac Imaging in the Management of Diseases of the Cardiovascular System. Radiology‐Nuclear Medicine Diagnostic Imaging 2023. [DOI: 10.1002/9781119603627.ch8] [Reference Citation Analysis]
|
| 2 |
Kapoor A, Kapoor S, Upreti K, Singh P, Kapoor S, Alam MS, Nasir MS. Cardiovascular Disease Prognosis and Analysis Using Machine Learning Techniques. Communications in Computer and Information Science 2023. [DOI: 10.1007/978-3-031-25088-0_15] [Reference Citation Analysis]
|
| 3 |
Jing B, Boscardin WJ, Deardorff WJ, Jeon SY, Lee AK, Donovan AL, Lee SJ. Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data. Med Care 2022. [PMID: 35352701 DOI: 10.1097/MLR.0000000000001720] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 4 |
Xiang L, Deng K, Mei Q, Gao Z, Yang T, Wang A, Fernandez J, Gu Y. Population and Age-Based Cardiorespiratory Fitness Level Investigation and Automatic Prediction. Front Cardiovasc Med 2021;8:758589. [PMID: 35071342 DOI: 10.3389/fcvm.2021.758589] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 5 |
Guo R, Zhang R, Liu R, Liu Y, Li H, Ma L, He M, You C, Tian R. Machine Learning-Based Approaches for Prediction of Patients’ Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage. JPM 2022;12:112. [DOI: 10.3390/jpm12010112] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
|
| 6 |
Herrgårdh T, Madai VI, Kelleher JD, Magnusson R, Gustafsson M, Milani L, Gennemark P, Cedersund G. Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios. Neuroimage Clin 2021;31:102694. [PMID: 34000646 DOI: 10.1016/j.nicl.2021.102694] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
|
| 7 |
Yang H, Li Z, Wang Z. Prediction of atherosclerosis diseases using biosensor-assisted deep learning artificial neuron model. Neural Comput & Applic 2021;33:5257-66. [DOI: 10.1007/s00521-020-05317-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 8 |
Catania LJ. AI applications in prevalent diseases and disorders. Foundations of Artificial Intelligence in Healthcare and Bioscience 2021. [DOI: 10.1016/b978-0-12-824477-7.00007-9] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 9 |
Al'Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, Pandey M, Maliakal G, van Rosendael AR, Beecy AN, Berman DS, Leipsic J, Nieman K, Andreini D, Pontone G, Schoepf UJ, Shaw LJ, Chang HJ, Narula J, Bax JJ, Guan Y, Min JK. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019;40:1975-1986. [PMID: 30060039 DOI: 10.1093/eurheartj/ehy404] [Cited by in Crossref: 206] [Cited by in F6Publishing: 222] [Article Influence: 68.7] [Reference Citation Analysis]
|
| 10 |
Alshammari R, Atiyah N, Daghistani T, Alshammari A. Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet. Online J Public Health Inform 2020;12:e11. [PMID: 32908645 DOI: 10.5210/ojphi.v12i1.10611] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
|
| 11 |
Cui M, Gang X, Gao F, Wang G, Xiao X, Li Z, Li X, Ning G, Wang G. Risk Assessment of Sarcopenia in Patients With Type 2 Diabetes Mellitus Using Data Mining Methods. Front Endocrinol (Lausanne) 2020;11:123. [PMID: 32210921 DOI: 10.3389/fendo.2020.00123] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
|
| 12 |
Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, Wang R. Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine 2020;67:412-22. [PMID: 31673954 DOI: 10.1007/s12020-019-02121-6] [Cited by in Crossref: 23] [Cited by in F6Publishing: 23] [Article Influence: 5.8] [Reference Citation Analysis]
|
| 13 |
Al-mallah MH, Sakr S. Artificial intelligence for plaque characterization: A scientific exercise looking for a clinical application. Atherosclerosis 2019;288:158-9. [DOI: 10.1016/j.atherosclerosis.2019.06.914] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.8] [Reference Citation Analysis]
|
| 14 |
Borror A, Mazzoleni M, Coppock J, Jensen BC, Wood WA, Mann B, Battaglini CL. Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network. Biomedical Human Kinetics 2019;11:60-8. [DOI: 10.2478/bhk-2019-0008] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 15 |
Ozemek C, Laddu DR, Lavie CJ, Claeys H, Kaminsky LA, Ross R, Wisloff U, Arena R, Blair SN. An Update on the Role of Cardiorespiratory Fitness, Structured Exercise and Lifestyle Physical Activity in Preventing Cardiovascular Disease and Health Risk. Prog Cardiovasc Dis 2018;61:484-90. [PMID: 30445160 DOI: 10.1016/j.pcad.2018.11.005] [Cited by in Crossref: 115] [Cited by in F6Publishing: 99] [Article Influence: 23.0] [Reference Citation Analysis]
|
| 16 |
Al-Mallah MH, Sakr S, Al-Qunaibet A. Cardiorespiratory Fitness and Cardiovascular Disease Prevention: an Update. Curr Atheroscler Rep 2018;20:1. [PMID: 29340805 DOI: 10.1007/s11883-018-0711-4] [Cited by in Crossref: 78] [Cited by in F6Publishing: 82] [Article Influence: 15.6] [Reference Citation Analysis]
|
