Successful Machine Learning for Health Data Sciences …

requires a integrated view on the data science ecosystem and deep understanding of the knowledge discovery pipeline, hence an integrated view and concerted effort of seven topics 1) data science, 2) machine learning algorithms, 3) graph theory/network science, 4) computational topology, 5) entropy, 6) data visualization and visual analytics, and 7) privacy, data protection, safety and security.

integrative interactive machine learning pipeline with the human-in-the-loop

The Knowledge Discovery Pipeline needs an integrated view on seven fields ranging from Visualization (HCI) to Data Sciences (KDD)

Machine learning is the most growing subfield of computer science, driven by the ongoing explosion in the availability of data [Jordan, M. I. & Mitchell, T. M. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349, (6245), 255-260]. Machine learning evolved from artificial intelligence and deals with many different problems and aspects to solve various tasks, including knowledge discovery, data mining, decision support etc.; a grand challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in complex data, which are often hidden and not accessible to the human expert. The classical focus is on two interrelated questions: How can we build algorithms that automatically improve through experience? and What are the fundamental statistical computational and  information-theoretic laws that govern all learning systems, including computers, humans, and organizations? The study of machine learning is important for many application branches. Due to the fact that biomedicine, health and the life sciences are turning into a data intensive science, machine learning can help to more evidence-based decision-making and support to realize the grand goals of personalized medicine. [Holzinger, A. 2014. Trends in Interactive Knowledge Discovery for Personalized Medicine: Cognitive Science meets Machine Learning. IEEE Intelligent Informatics Bulletin, 15, (1), 6-14].

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ACM Digital Library > Computing Classification System (CSS)  > Computing methodologies > machine learning >artificial intelligence