Machine Learning for Health Informatics
Machine learning is a large and rapidly developing subfield of computer science that evolved from artificial intelligence (AI) and is tightly connected with data mining and knowledge discovery. The ultimate goal of machine learning is to design and develop algorithms which can learn from data. Consequently, machine learning systems learn and improve with experience over time and their trained models can be used to predict outcomes of questions based on previously seen knowledge. In fact, the process of learning intelligent behaviour from noisy examples is one of the major questions in the field. The ability to learn from noisy, high dimensional data is highly relevant for many applications in the health informatics domain. This is due to the inherent nature of biomedical data, and health will increasingly be the focus of machine learning research in the near future.