Solving complex problems in health informatics: From automatic Machine Learning (aML) to interactive Machine Learning (iML)

Description

Invited lecture to the machine learning lab of the University of Ljubljana
Abstract: Machine learning (ML) is the fastest growing field in computer science, and health informatics is amongst the greatest challenges. The goal of Machine Learning (ML) is to develop algorithms which can learn from data and improve over time. In automatic machine learning (aML) great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in health informatics, we are often confronted with a small number of datasets or rare events, where aML suffer of insufficient training samples. Here interactive Machine Learning (iML) may be of help, where a “human‐in‐the‐loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k‐anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise bean NP‐hardproblem, reduces greatly incomplexity through the input and the assistance of a human agent involved in the learning phase. However, for the successful application of a full Knowledge Discovery pipeline in the health domain a multidisciplinary skill set is required, encompassing the following seven specializations: 1) datascience, 2) ML 3) networkscience, 4) graphs/topology, 5) time/entropy, 6) datavisualization, and 7) privacy, dataprotection, safety and security, which is fostered in the HCI‐KDD approach.

Period	5 Jul 2016
Held at	University of Ljubljana, Slovenia