Towards the Next Generation of Clinical Decision Support: Overcoming the Integration Challenges of Genomic Data and Electronic Health Records

The wide adoption of electronic health records (EHRs), the unprecedented abundance of genomic data, and the rapid advancements in computational methods have paved the way for next generation clinical decision support (NGCDS) systems. NGCDS provides significant opportunities for the prevention, early detection, and the personalized treatment of complex diseases. The integration of genomic and EHR data into the NGCDS workflow is faced with significant challenges due to the high complexity and sheer magnitude of the associated data. This dissertation performs an in depth investigation to address the computational and algorithmic challenges of integrating genomic and EHR data within the NGCDS workflow. In particular, the dissertation (i) defines the major genomic challenges NGCDS faces and discusses possible resolution directions, (ii) proposes an accelerated method for processing raw genomic data, (iii) introduces a data representation and compression method to store the processed genomic outcomes in a database schema, and finally, (iv) investigates the feasibility of using EHR data to produce accurate disease risk assessments. We hope that the proposed solutions will expedite the adoption of NGCDS and help advance the state of healthcare

Towards the Next Generation of Clinical Decision Support: Overcoming the Integration Challenges of Genomic Data and Electronic Health Records

The wide adoption of electronic health records (EHRs), the unprecedented abundance of genomic data, and the rapid advancements in computational methods have paved the way for next generation clinical decision support (NGCDS) systems. NGCDS provides significant opportunities for the prevention, early detection, and the personalized treatment of complex diseases. The integration of genomic and EHR data into the NGCDS workflow is faced with significant challenges due to the high complexity and sheer magnitude of the associated data. This dissertation performs an in depth investigation to address the computational and algorithmic challenges of integrating genomic and EHR data within the NGCDS workflow. In particular, the dissertation (i) defines the major genomic challenges NGCDS faces and discusses possible resolution directions, (ii) proposes an accelerated method for processing raw genomic data, (iii) introduces a data representation and compression method to store the processed genomic outcomes in a database schema, and finally, (iv) investigates the feasibility of using EHR data to produce accurate disease risk assessments. We hope that the proposed solutions will expedite the adoption of NGCDS and help advance the state of healthcare

Towards the Next Generation of Clinical Decision Support: Overcoming the Integration Challenges of Genomic Data and Electronic Health Records

The wide adoption of electronic health records (EHRs), the unprecedented abundance of genomic data, and the rapid advancements in computational methods have paved the way for next generation clinical decision support (NGCDS) systems. NGCDS provides significant opportunities for the prevention, early detection, and the personalized treatment of complex diseases. The integration of genomic and EHR data into the NGCDS workflow is faced with significant challenges due to the high complexity and sheer magnitude of the associated data. This dissertation performs an in depth investigation to address the computational and algorithmic challenges of integrating genomic and EHR data within the NGCDS workflow. In particular, the dissertation (i) defines the major genomic challenges NGCDS faces and discusses possible resolution directions, (ii) proposes an accelerated method for processing raw genomic data, (iii) introduces a data representation and compression method to store the processed genomic outcomes in a database schema, and finally, (iv) investigates the feasibility of using EHR data to produce accurate disease risk assessments. We hope that the proposed solutions will expedite the adoption of NGCDS and help advance the state of healthcare

Towards the Next Generation of Clinical Decision Support: Overcoming the Integration Challenges of Genomic Data and Electronic Health Records

The wide adoption of electronic health records (EHRs), the unprecedented abundance of genomic data, and the rapid advancements in computational methods have paved the way for next generation clinical decision support (NGCDS) systems. NGCDS provides significant opportunities for the prevention, early detection, and the personalized treatment of complex diseases. The integration of genomic and EHR data into the NGCDS workflow is faced with significant challenges due to the high complexity and sheer magnitude of the associated data. This dissertation performs an in depth investigation to address the computational and algorithmic challenges of integrating genomic and EHR data within the NGCDS workflow. In particular, the dissertation (i) defines the major genomic challenges NGCDS faces and discusses possible resolution directions, (ii) proposes an accelerated method for processing raw genomic data, (iii) introduces a data representation and compression method to store the processed genomic outcomes in a database schema, and finally, (iv) investigates the feasibility of using EHR data to produce accurate disease risk assessments. We hope that the proposed solutions will expedite the adoption of NGCDS and help advance the state of healthcare

Simulating variance heterogeneity in quantitative genome wide association studies

Abstract Background Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. Results A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. Conclusions We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci