Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival

Developing priorities to achieve health equity through diabetes translation research: A concept mapping study

Introduction: The goal of diabetes translation research is to advance research into practice and ensure equitable benefit from scientific evidence. This study uses concept mapping to inform and refine future directions of diabetes translation research with the goal of achieving health equity in diabetes prevention and control. Research design and methods: This study used concept mapping and input from a national network of diabetes researchers and public health practitioners. Concept mapping is a mixed-method, participant-based process. First, participants generated statements by responding to a focus prompt ( Results: Ten clusters were identified containing between 6 and 12 statements from 95 total generated statements. The ranges of average importance and feasibility ratings for clusters were fairly high and narrow (3.62-4.09; 3.10-3.93, respectively). Clusters with the most statements in the go-zone quadrant (above average importance/feasibility) were Conclusions: This study created a framework of 10 priority areas to guide current and future efforts in diabetes translation research to achieve health equity. Themes rated as highly important and feasible provide the basis to evaluate current research support. Future efforts should explore how to best support innovative-targets, those rated highly important but less feasible

Blending the physical and the digital through conceptual spaces

The rise of the Internet facilitates an ever increasing growth of virtual, i.e. digital spaces which co-exist with the physical environment, i.e. the physical space. In that, the question arises, how physical and digital space can interact synchronously. While sensors provide a means to continuously observe the physical space, several issues arise with respect to mapping sensor data streams to digital spaces, for instance, structured linked data, formally represented through symbolic Semantic Web (SW) standards such as OWL or RDF. The challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the vast variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an approach which allows to refine symbolic concepts as CS and to ground ontology instances to so-called prototypical members which are vectors in the CS. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of CS members, the most similar instance can be identified. In that, we provide a means to bridge between the physical space, as observed by sensors, and the digital space made up of symbolic representations