Digital Health Using Data Science

Document Type

Conference Proceeding

Publication Date

2025

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC)

Source ISSN

2836-3795

Original Item ID

DOI: 10.1109/COMPSAC65507.2025.00330

Abstract

The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to apply computational tools in real-world healthcare environments responsibly.

Comments

Published as part of the proceedings of the IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), 2025: 2344-2351. DOI.

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