Yoav Rubin

Utilizing assigned treatments as labels for supervised machine learning in clinical decision support

Clinical Decision Support (CDS) tools are typically designed to assist physicians in clinical decision making at Point Of Care (POC). Existing CDS tools commonly rely on relatively simple rules, deduced from relevant clinical guidelines. However, the increasing pace by which Health Care Organizations (HCOs) adopt Electronic Health Record technologies suggest great potential for CDS tools that directly mine the massive clinical data collected at the HCO. A natural goal for such tools is to exploit Machine Learning (ML) algorithms in order to predict patients outcome. However, the technical challenges involved in constructing such a system in practice are quite involved, where in particular treatments outcome are often not available as part of the HCOs data. Here, we propose a different strategy in which we use the assigned treatments as the labels in the learning process of the supervised ML algorithms. We present two different use-cases in which our approach could be used. First, in order to highlight the clinical features most associated with the assigned treatments; and second, in order to predict the customary treatment for a patient at POC in a statistically data-driven manner. Altogether, our approach represents a novel strategy that is complementary to the classical paradigm of rule-based clinical guidelines adherence. Experimental results over hypertension clinical data demonstrate the validity of our approach.

What You See And Do Is What You Get: A Human-Centric Design Approach to Human-Centric Process

Designing human-centric processes is complex. It involves the definition of interactions between humans and machines, interactions between machines and machines, information transfer, and scenarios based on decisions taken by both humans and machines. Traditionally, designing such processes is performed by design experts who define the processes in a way that mimics a bird’s eye view of it, usually expressed by a graph composed of nodes and arrows. In this work, we suggest a design approach based on the way that a process is perceived by the users who participate in it. We present a novel approach termed “What You See And Do Is What You Get” that enables defining an entire human-centric process with a lowered expertise entry bar for process designers. Further, we present a model-driven, web-based tool that realizes the presented design approach and enables fast development of applications that support human-centric processes.