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V Vijay Vignesh

V Vijay Vignesh supervised by Dr. Deva Priyakumar U  received his Master of Science – Dual Degree  in Computational Natural Science (CNS). Here’s a summary of his research work on Strategic Applications of Artificial Intelligence in Healthcare: Focus on Public Health and Cardiology:

The strategic application of artificial intelligence (AI) in healthcare has the potential to revolutionize patient care, particularly in the domains of public health and cardiology. This thesis presents innovative AI-based solutions to address critical challenges in these fields, focusing on enhancing decision-making, disease surveillance, and diagnostic accuracy. In the second chapter, we introduce the AI-Based Home-Based Care Assistive System (AI-HBCAS), developed to aid healthcare professionals in identifying severe patient conditions that require referrals. By analyzing patient demographics, symptoms, and vital signs, AI-HBCAS enhances clinical decisionmaking, ensuring timely and appropriate care for patients with non-communicable diseases (NCDs). This system aligns with the Ministry of Health and Family Welfare’s goals of improving public health delivery through digital health solutions. The third chapter explores the development of a Pharmacosurveillance model for outbreak detection and awareness in Primary Health Centers (PHCs). Leveraging AI, we try to analyse prescriptions and pharmacy records to map drug utilization patterns and detect local outbreaks. The insights generated could potentially support timely public health interventions and contribute to the Ministry of Health and Family Welfare’s objectives of enhancing public health infrastructure and outcomes. We then delve into the development of a unique deep learning architecture for heart murmur detection using phonocardiogram (PCG) recordings in chapter four. Our modified variable kernel length Residual Networks (ResNets) demonstrate significant improvements in detecting heart murmurs, achieving a weighted accuracy score of 0.708 in the George B. Moody PhysioNet Challenge 2022. This innovative approach highlights the potential of deep learning models in improving diagnostic accuracy in cardiology, specifically using audio modalities. Chapter five focuses on the creation of a machine learning approach for predicting outcomes in post-anoxic coma patients using longitudinal EEG and ECG recordings. Our model extracts frequency domain features from these recordings, combining them with demographic data to predict neurological recovery following cardiac arrest. This solution, part of the George B Moody PhysioNet Challenge 2023, underscores the viability of machine learning models in critical care settings, offering valuable prognostic insights. 

In summary, this thesis presents a comprehensive exploration of AI applications in healthcare, with a particular emphasis on public health and cardiology. By developing AI-based assistive systems and advanced diagnostic tools, this research contributes to the advancement of digital health and improves healthcare delivery and patient outcomes. These innovations pave the way for more effective, data driven healthcare solutions, addressing both current challenges and future needs in the medical. 

October 2024