Health Analytics Tooling: Retrieval, Assessment Decision and Trending
Project runs from 06/01/2018 to 11/30/2019
With a focus on Diabetes in Phase 1, we propose the development of a comprehensive tool which will systematically and seamlessly navigate across the various hybrid data accessible through UNC Medical Records, with a health assessment enabling capability as well as various possible trends. More specifically, we plan on fully exploiting the tools of machine learning and bring them to bear on each step of the data analysis and exploitation. In close consultation with the health specialists, we will develop a mapping mechanism of qualitative data to the quantitative space, thus homogenizing the data. In addition, we plan to design a “Decision Tree” (DT) adapted to our homogenized data. We will exploit the characteristic computational efficiency of tree structures to comb through each patient’s data to yield a quantifiable assessment of interest (e.g. patient is cured as a result of treatment, follow up visits, prescription follow up, and cross validated with State Vital Records). Note that the proposed DT-based analyses not only play a key role in the analysis of trends across populations of patients, but also can also be used to conduct other global statistical analyses (e.g. probabilistically determine a permanent cure conditioned on a life style).
Our plan is to have a Personal Computer-based menu-driven tool with a comprehensive set of options, including visualizations, to carry through a thorough exploration of data; the planned interactive operation will necessitate some attention to identifying and solving all computational bottlenecks in this process.