Authors: André Skupin*, Center for Information Convergence and Strategy, San Diego State University
Topics: Geographic Information Science and Systems, Medical and Health Geography, Cartography
Keywords: spatial intelligence, health, biomedicine, big data, natural language processing, machine learning, visualization, knowledge management
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded
The relevance of spatial thinking and geographic techniques for the medical domain extends far beyond the traditional bounds of using GIS and spatial analysis to investigate public health phenomena in geographic space. With some imagination, medical concepts and practices can be seen as simultaneously existing in a multitude of different spaces. Once an overarching spatial viewpoint is adopted, specific data sources can be transformed into engaging artifacts that support research, education, and medical practice. For example, medical records generated by intensive care units could be turned into nuanced representations of patients' health status. The written notes of medical professionals – from surgeons to nurses and radiologists – can be a rich source of insight, not only with respect to individual patients, but in surveying and monitoring the medical ecosystem. Meanwhile, natural language processing and machine learning can be used to organize biomedical research results into coherent knowledge structures. The presentation will highlight three projects that exemplify a spatial intelligence approach to health. These involve diverse data sources, namely: (1) a corpus of two million biomedical research articles, (2) detailed clinical records for more than 10,000 ICU patients, and (3) a multi-year registry data base of several thousand heart attack patients.