Health Informatics

Health informatics leverages computational methods to analyze and interpret healthcare data, aiming to improve patient care, research, and public health. Current research emphasizes the development and application of machine learning models, including deep learning architectures and large language models, for tasks such as disease prediction, personalized medicine, and clinical decision support. A significant focus is on addressing biases and ensuring fairness in algorithms, as well as enhancing model interpretability and transparency to build trust and accountability. These advancements hold considerable promise for transforming healthcare delivery and biomedical research through more efficient and equitable practices.