De-identified multidimensional medical records for disease population demographics and image processing tools development.

摘要

Recently, The National Institute of Health (NIH) has outlined its scientific priorities in a strategic plan, "NIH Roadmap for Medical Research". In direct alignment with these priorities, many academic and research oriented medical institutions across The United States conduct numerous clinical and translational research studies on an ongoing basis. From a personalized health care and translational research perspective, quite often efforts of such nature will span across multiple departments or even institutions. We consider these activities as a knowledge and information flow which is taking place around multidimensional, heterogeneous clinical and research data that is collected from disparate sources.;The primary objective of the research and development described in this thesis is to provide an integrative platform where multidimensional data from multiple disparate sources can be easily accessed, visualized, and analyzed. We believe that ability to execute such truly integrative queries, visualizations and analyses across multiple data types is critical to the ability to execute highly effective clinical and translational research. Therefore, to address the preceding gap in knowledge, we introduce a model computational framework that is intended to support the integrative query, visualization and analysis of structured data, narrative text, and image data sets in support of translational research activities. The introduced framework also aims to address the challenges posed by regulatory compliance, patient privacy/confidentiality concerns, and the need to facilitate multicenter research paradigms.