Reducing healthcare costs and improving quality of outcomes is a challenge even in developed economies. Much information remains in paper form, lack common standards, sharing is uncommon and frequently hampered by the lack of foolproof de-identification for patient privacy. All of these issues impede opportunities for data mining and analysis that would enable better predictive and preventive medicine. These issues are compounded in emerging economies due to geopolitical constraints, transportation and geographic barriers, a much more limited clinical workforce, and infrastructural challenges to delivery. Thus, simple, high-impact deliverable interventions such as universal childhood immunization and maternal childcare are hampered by poor monitoring and reporting systems. This workshop is focused on identifying challenges to be overcome for effectively delivering efficient healthcare and to the masses. Specifically, we will provide a forum to discuss research directions, share experience and insights from both academia and industry. The anticipated outcome of the workshop is an assessment of the state of the art in the area, as well as identification of critical next steps to pursue in this topic.

Detecting impending failure of hard disks is an important prediction task which might help computer systems to prevent loss of data and performance degradation. Currently most of the hard drive vendors support self-monitoring, analysis and reporting technology (SMART) which are often considered unreliable for such tasks. The problem of finding alternatives to SMART for predicting disk failure is an area of active research. In this paper, we consider events recorded from live disks and show that it is possible to construct decision support systems which can detect such failures. It is desired that any such prediction methodology should have high accuracy and ease of interpretability. Black box models can deliver highly accurate solutions but do not provide an understanding of events which explains the decision given by it. To this end we explore rule based classifiers for predicting hard disk failures from various disk events. We show that it is possible to learn easy to understand rules, from disk events, which have extremely low false alarm rates on real world data.