Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied—An Exploratory Study DOI Open Access
Maciej Rosoł, Jakub S. Gąsior,

Kacper Korzeniewski

et al.

Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(23), P. 7353 - 7353

Published: Dec. 2, 2024

This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals-cardiological patients, healthy participants, and athletes-based on cardiorespiratory features from short-term static measurements. It also examined impact coupling (CRC)-related (from causal information domains) modeling identify a preferred feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions.

Language: Английский

The role of explainability and transparency in fostering trust in AI healthcare systems: a systematic literature review, open issues and potential solutions DOI
Christopher Ifeanyi Eke, Liyana Shuib

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Language: Английский

Citations

2
Artificial Intelligence in Cardiovascular Medicine: From Clinical Care, Education, and Research Applications to Foundational Models—A Perspective DOI
Robert Avram, Girish Dwivedi, Padma Kaul

et al.

Canadian Journal of Cardiology, Journal Year: 2024, Volume and Issue: 40(10), P. 1769 - 1773

Published: Aug. 19, 2024

Language: Английский

Citations

0
Pediatric Cardiology Machine Learning: Clinical Integration and Ethics DOI

Shenghao Xu,

Xinrui He

Canadian Journal of Cardiology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Citations

0
The application of artificial intelligence in tissue repair and regenerative medicine related to pediatric and congenital heart surgery: a narrative review DOI
Jeevan Francis, Joseph George,

Edward W.K. Peng

et al.

Regenerative medicine reports ., Journal Year: 2024, Volume and Issue: 1(2), P. 131 - 136

Published: Dec. 1, 2024

Artificial intelligence and machine learning have the potential to revolutionize tissue repair regenerative medicine in field of pediatric congenital heart surgery. is increasingly being recognized as a transformative force healthcare with its ability analyse large complex datasets, predict surgical outcomes, improve education training use virtual reality simulators. This review explores current applications artificial predicting improving peri-operative decision-making, facilitating for surgeons, particularly low-income countries. By leveraging advanced algorithms simulations, can intricate patient data anatomical variations, enabling early detection defects optimising approaches. Ultimately, while barriers such inconsistent quality limited resources remain, advancement technologies offers promising avenue enhance related care

Language: Английский

Citations

0
Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied—An Exploratory Study DOI Open Access
Maciej Rosoł, Jakub S. Gąsior,

Kacper Korzeniewski

et al.

Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(23), P. 7353 - 7353

Published: Dec. 2, 2024

This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals-cardiological patients, healthy participants, and athletes-based on cardiorespiratory features from short-term static measurements. It also examined impact coupling (CRC)-related (from causal information domains) modeling identify a preferred feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions.

Language: Английский

Citations

0