Data Science Priorities for a University Hospital-Based Institute of Infectious Diseases: A Viewpoint

Area of Science:

• Infectious Diseases
• Medical Informatics
• Computational Biology

Background:

• Modern university hospital-based infectious diseases institutes have long utilized automation, bioinformatic analysis, and data management.
• The 21st century is marked by advancements in computing power, leading to the "big data" revolution in genomics and environmental science, now impacting medical informatics.

Purpose of the Study:

• To highlight the transformative impact of "data-driven" research and machine learning on infectious disease studies.
• To emphasize the evolving skill set required for infectious disease institutes in the era of big data and artificial intelligence.

Main Methods:

• Review of current practices in infectious diseases institutes.
• Analysis of the impact of computational advancements and "big data" on medical research.
• Discussion of the integration of machine learning, knowledge representation, and knowledge discovery.

Main Results:

• Scientific research methods are rapidly evolving due to increased computing power and the "big data" phenomenon.
• Machine learning methods, proven effective in daily life, are poised to revolutionize medical research, including infectious diseases.

Conclusions:

• Infectious disease institutes must adapt to "data-driven" research paradigms.
• Future success requires the integration of biologists and clinicians with computer scientists, statisticians, and mathematical modelers proficient in machine learning and knowledge discovery.