Integration of electronic health records and public biological repositories illuminates human pathophysiology and underlying molecular relationships [electronic resource]
- David Pei-Ann Chen.
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|3781 2011 C||In-library use|
- Chen, David Pei-Ann.
- Butte, Atul J. primary advisor.
- Altman, Russ advisor.
- Walker, Michael advisor.
- Stanford University Department of Biomedical Informatics.
- Secondary use of electronic health record (EHR) data has the potential to unlock novel insight into human pathophysiology. While EHR data has often been used in retrospective studies, management of public health, and to improve patient safety, its use in discovering underlying molecular mechanisms of human disease and pathophysiology has been limited. Much of this can be attributed to the differing priorities between healthcare providers and basic biological researchers. The advent of biobanks that collect physiological measurements as well as tissue samples and molecular measurements promises to address this issue. However, the sheer number of different biological and clinical measurement modalities hinders the generation of a truly complete view of the human organism. The increased adoption of EHRs as well as growing biological data repositories enables researchers to answer biological questions applicable to the human population. The goal is not to treat humans as experimental organisms, but rather to gain as much knowledge as possible from every patient seen. By viewing EHRs as a repository of perturbations and their associated physiological consequences we can begin to design experiments that leverage EHR data to generate hypotheses that can be further evaluated. This thesis aims to describe methods to summarize EHR biomarker data in a systematic way to enable downstream analysis as well as methods for integrating EHR data and disparate biological data. I will describe the creation of the "clinarray" and its application to specific disease populations to differentiate patients by severity and to discover latent physiological factors associated with disease. I will also describe how to aggregate and analyze clinarrays from across the EHR to build models of aging. Finally I will discuss the use of diseases to integrate EHR data with gene expression data from a disparate biological data source to discover genes related to aging and to generate hypotheses for relationships between biomarkers and genes. The integration of readily available clinical and biological data promises to improve our understanding of phenomics without impacting patient care and adding an unnecessary burden to the healthcare system. It is important for biological research to leverage the increased amount of molecular and environmental data stored in EHRs to build a more complete view of the human organism.
- Publication date
- Submitted to the Department of Biomedical Informatics.
- Ph.D. Stanford University 2011
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