KEYNOTE LECTURE: Machine Learning for Decision Making in Healthcare
09:00 - 09:50
Building 9 - Lecture hall 2
An overview of our ongoing projects aimed to facilitate predictive analytics in healthcare will be presented in this talk. Challenges and the proposed solutions will be discussed related to structured regression on multilayer networks, recovering network connectivity, modeling positive and negative influences, uncertainty propagation and effective integration of domain knowledge and big data. The algorithms will be evaluated in the context of applications related to exploiting information extracted from electronic health records for identifying resources a patient would need for triage systems in emergency departments, estimating hospitalization cost, predicting admission and mortality rate for high impact diseases, identifying disease relationships, discovering gene-disease interactions and assessing tolerance to viral infections.