PREDICTION OF DIASTOLIC DYSFUNCTION IN PATIENTS WITH CARDIOVASCULAR DISEASES AND TYPE 2 DIABETES WITH RESPECT TO COVID-19 IN ANAMNESIS USING ARTIFICIAL INTELLIGENCE-Reference-Cited by-同舟云学术

PREDICTION OF DIASTOLIC DYSFUNCTION IN PATIENTS WITH CARDIOVASCULAR DISEASES AND TYPE 2 DIABETES WITH RESPECT TO COVID-19 IN ANAMNESIS USING ARTIFICIAL INTELLIGENCE

Published:2023-05-12 Issue: Volume: Page:
ISSN:
Container-title:2023 the 7th International Conference on Medical and Health Informatics (ICMHI)
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Author:

Alimova Dilnoza¹^ORCID,Ikramov Alisher²^ORCID,Trigulova Raisa¹^ORCID,Abdullaeva Saodat¹^ORCID,Mukhtarova Shakhnoza¹^ORCID

Affiliation:

1. Uzbekistan Republican scientific cardiology center, Uzbekistan

2. Biomedinformatics, Institute of Mathematics of Republic of Uzbekistan, Uzbekistan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3608298.3608310

Reference12 articles.

1. Evolution of diastolic function algorithms: Implications for clinical practice

2. Precision Phenotyping in Heart Failure and Pattern Clustering of Ultrasound Data for the Assessment of Diastolic Dysfunction

3. A. Omar , K. Shameer , S. Narula , 2018 . Artificial intelligence-based assessment of left ventricular filling pressures from 2-dimensional cardiac ultrasound images . JACC Cardiovasc Imaging. (2018) 11, pp.509– 10 . doi: 10.1016/j.jcmg.2017.05.003 10.1016/j.jcmg.2017.05.003 A. Omar, K. Shameer, S. Narula, 2018. Artificial intelligence-based assessment of left ventricular filling pressures from 2-dimensional cardiac ultrasound images. JACC Cardiovasc Imaging. (2018) 11, pp.509–10. doi: 10.1016/j.jcmg.2017.05.003

4. Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features

5. Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction