Digital health is a rapidly evolving field that lies at the intersection of healthcare and technology. It has emerged as a transformative force in our approach to medical care, patient outcomes, and overall patient well-being. As the world is increasingly becoming interconnected, digital health utilizes technological innovations to revolutionize healthcare practices, improve patient experiences, and enhance healthcare delivery at a global scale. At its core, digital health encompasses a wide range of technologies, including electronic health records (EHRs), mobile health apps, wearable devices, remote patient monitoring, telemedicine platforms, data science, and data management applications.

Digital health, fuelled by the power of data science, is transforming the healthcare landscape by harnessing technology, data, and analytics to develop efficient, patient-centric, and personalized healthcare experiences. With the continuous advancement of data science and the ever-expanding array of digital health technologies, the future holds immense promise for improving global health outcomes and redefining the way we approach healthcare. The goal of this topic collection is to explore data-driven innovation and growth in digital health spanning across personalized diagnosis, treatment, and resource planning for next-generation healthcare systems.

The topics that shall be included in this collection are as follows –

Fusion and Integration of Healthcare Data:
o Development of techniques to integrate and analyze heterogeneous data sources, such as EHRs, wearables, genomic data from gene expression studies, and environmental data from sensors deployed at different geographical locations and satellites monitoring climate changes to gain a comprehensive view of patient's health and provide personalized healthcare in the form of treatment planning.
o Development of robust data privacy and security frameworks for protecting sensitive health information while still allowing for data sharing and analysis.

Health Data Analysis, Patient Wellbeing Assessment and Feedback:
o Application of Natural Language Processing (NLP) techniques to process unstructured clinical text data from physician notes, and patient-reported outcomes, to extract valuable information and facilitate data-driven decision-making. Sentiment analysis of social media data can be used to assess patient well-being and make predictions on patient’s holistic health.
o Analysis of patient feedback and sentiment data from various sources including surveys, and reviews to assess the quality of healthcare services.

Personalised Treatment and Recommendations:
o Application of machine and deep learning to patient data including image analysis, diagnosis, and detection of abnormalities in medical imaging data, for the development of personalized treatment plans, considering factors such as genetics, demographics, lifestyle, and treatment history.
o Investigating the use of Internet of Things (IoT) devices for continuous health monitoring to facilitate data collection and enable early detection of health issues, also allowing the development of personalized treatment plans.
o Use of technologies such as extended reality for empowering personalized treatment.

Medical Decision Support and Healthcare Resource Optimization:
o Exploration of methods for analysis and interpretation of longitudinal patient data, understanding changes and trends over time, and identifying risk factors and treatment responses.
o Development of interpretable and transparent AI models that can provide explanations for their decisions, promoting trust and understanding among healthcare practitioners and patients.
o Investigating the use of data science to optimize healthcare resource allocation, such as smart ambulances, hospital bed management, staff scheduling, and medical equipment utilization, to enhance healthcare system efficiency.

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