Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation

Caravaca, Juan Manuel; Soria-Olivas, Emilio; Bataller, María Querol; Serrano, Adela; Such-Miquel, Luis; Vila‐Francés, Joan; Guerrero, Juan

doi:10.1016/j.compbiomed.2013.11.008

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…There is no doubt about that exercise can benefit health including aging population. Impropriate physical training could induce large spectrum of ventricular fibrillation [134] and the modest exercise could not promote protection on stress-induced tachycardia [135]. Therefore, what exercise program composed of appropriate combination of intensity, modality, frequency, and duration of exercise should be chosen as the intrinsic part of aging cohort still remain unknown largely.…”

Section: Challenges Of Translating Animal Exercise Models To Clinicalmentioning

confidence: 99%

A systematic comparison of exercise training protocols on animal models of cardiovascular capacity

Feng

Wang

et al. 2019

Life Sciences

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Cardiovascular disease (CVD) is a major global cause of mortality, which has prompted numerous studies seeking to reduce the risk of heart failure and sudden cardiac death. While regular physical activity is known to improve CVD associated morbidity and mortality, the optimal duration, frequency, and intensity of exercise remains unclear. To address this uncertainty, various animal models have been used to study the cardioprotective effects of exercise and related molecular mechanism such as the mice training models significantly decrease size of myocardial infarct by affecting Kir6.1, VSMC sarc-KATP channels, and pulmonary eNOS. Although these findings cement the importance of animal models in studying exercise induced cardioprotection, the vast assortment of exercise protocols makes comparison across studies difficult. To address this issue, we review and break down the existent exercise models into categories based on exercise modality, intensity, frequency, and duration. The timing of sample collection is also compared and sorted into four distinct phases: pre-exercise (Phase I), mid-exercise (Phase II), exercise recovery (Phase III), and post-exercise (Phase IV). Finally, because the life-span of animals so are limited, small changes in animal exercise duration can corresponded to untenable amounts of human exercise. To address this limitation, we introduce the Life-Span Relative Exercise Time (RETlife span) as a method of accurately defining short-term, medium-term and long-term exercise relative to the animal’s life expectancy. Systematic organization of existent protocols and this new system of defining exercise duration will allow for a more solid framework from which researchers can extrapolate animal model data to clinical application.

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Section: Challenges Of Translating Animal Exercise Models To Clinicalmentioning

confidence: 99%

A systematic comparison of exercise training protocols on animal models of cardiovascular capacity

Feng

Wang

et al. 2019

Life Sciences

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“…This topic selected 60 college students from different departments of Chongqing Education College to carry out the questionnaire survey [2][3][4][5], the problem is that in the face of injustice and frustration will choose what way to adjust the mentality. The answer is the following five, respectively: (1) Sleeping; (2) Watching TV or watching the film for relaxation; (3) Reading or Reading magazines; (4) Sports; (5) The other way.…”

Section: Function Of Regulating Mental Healthmentioning

confidence: 99%

“…Promote the Development of Youth'S Physiology and Psychology For college students, physical exercise can not only improve their physical quality, but also to a certain extent, to ensure the development of psychological quality, to provide the essential material basis for its physical and psychological development of college students have some help [4][5] . The brain is not only the body's organs; it is the material carrier carrying the soul.…”

Section: Function Of Regulating Mental Healthmentioning

confidence: 99%

Simulation analysis on the Influence of Physical Exercises on Youthsr Physical and Mental Health

Yang¹

2018

Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)

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The importance of physical exercise is investigated by analyzing the effects of exercise on the youth's mental and physical health. We also discuss the effect of physical exercise on youth's body shape, body composition, physical function index, and physical quality index by data analysis. Our result shows that we should pay attention to physical education teaching, improve exercise interest level, and promote more students to participate in physical exercise, so that the youths have healthier body.

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“…Los sistemas machine learning podrían permitir optimizar los ingresos hospitalarios evitables por insuficiencia cardiaca identificando de forma más precisa qué pacientes son susceptibles de descompensación cardiaca tras el alta hospitalaria que las escalas de riesgo clásico 90,106 ; este es un ejemplo de resultados contradictorios de la experiencia inicial en este campo tecnológico 107 que apunta a la importancia en el ajuste en la metodología de los sistemas 108 . Asimismo, hay estudios iniciales que abordan la utilidad de la inteligencia artificial como sistema de gestión en la telemonitorización remota de pacientes con insuficiencia cardiaca.…”

Section: Insuficiencia Cardiacaunclassified

Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario

Díaz¹

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Background: There is currently no other hot topic comparable to the capacity of the current technology to develop similar capabilities as human beings, even in medicine.This ability to simulate the processes of human intelligence with computer systems is known as artificial intelligence. One of the fields of artificial intelligence with greater application today in medicine is that of prediction, recommendation or diagnosis, where machine learning techniques are applied. Furthermore, there is growing interest in precision medicine techniques as machine learning, which can deliver individually adapted medical care.Percutaneous coronary interventions (PCI) with stent implantation has become routine practice for the revascularization of coronary vessels with significant obstructive atherosclerotic disease. Furthermore, PCI is the gold-standard of treatment in patients with acute myocardial infarction; reducing the rates of death and recurrent ischemia as compared to medical treatment. The long-term success of this procedure may be limited by stent restenosis, a pathological process leading to recurrent arterial narrowing at the site of PCI. Identifying which patients will do restenosis is an important clinical challenge; since it can manifest itself as a new acute myocardial infarction or force a new revascularization of the affected vessel, and that in cases of recurrent restenosis represents an important therapeutic dilemma.Objectives: Starting with a review of artificial intelligence techniques applied to medicine and in greater depth, of machine learning techniques applied to cardiology, the main objective of this doctoral thesis has been to develop a machine learning model to predict restenosis in patients with acute myocardial infarction undergoing PCI with stent implantation. In addition, secondary objectives have been to compare the developed machine learning model with classical predictive clinical scores, and to develop software able to transfer this machine learning contribution to daily clinical practice. In order to develop an easily-applicable model, we performed our predictions without any additional variables than those obtained in daily practice. Material: The dataset, obtained from the GRACIA-3 trial, consisted of 263 patients with demographic, clinical and angiographic characteristics; 23 of them presented restenosis at 12-months after stent implantation. All software development has been made in Python and cloud computing has been used, specifically AWS (Amazon Web Services).Results: Our best performing model was developed with an extremely randomized trees classifier; which significantly outperformed (0.77; area under the ROC curve) three clinical scores; PRESTO-1 (0.58), PRESTO-2 (0.58), and TLR (0.62). Precision-Prefacio recall curves gave a more accurate picture of the extremely randomized trees model´s performance showing an efficient algorithm (0.96) for non-restenosis, returning high precision as well as high recall. For a threshold considered as optimal, out of 1,000 patients undergoing s...

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Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation

Cited by 7 publications

References 17 publications

A systematic comparison of exercise training protocols on animal models of cardiovascular capacity

A systematic comparison of exercise training protocols on animal models of cardiovascular capacity

Simulation analysis on the Influence of Physical Exercises on Youthsr Physical and Mental Health

Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario

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