Abstract
Biomedical informatics with optimization and machine learning.
Highlights
Fast-growing biomedical and healthcare data have encompassed multiple scales ranging from molecules, individuals, to populations and have connected various entities in healthcare systems with increasing bandwidth, depth, and resolution
It has been a consensus that the sheer volume and complexity of the data we could acquire nowadays in biomedical informatics present major barriers toward their translation into effective clinical actions
Challenging applications are present in many areas of biomedical informatics, such as Computational Biology, which includes the advanced interpretation of critical biological findings, using databases and cutting-edge computational infrastructure; Clinical Informatics, which includes the scenarios of using computation and data for health care, spanning medicine, dentistry, nursing, pharmacy, and allied health; Public Health Informatics, which includes the studies of patients and populations to improve the public health system and to elucidate epidemiology; mHealth Applications, which include the
Summary
Fast-growing biomedical and healthcare data have encompassed multiple scales ranging from molecules, individuals, to populations and have connected various entities in healthcare systems (providers, pharma, payers) with increasing bandwidth, depth, and resolution. There are two types of applications in biomedical informatics where optimization and machine-learning methods are commonly used.
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