Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "heterogeneity"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Causal Pattern Mining in Highly Heterogeneous and Temporal EHRs Data
    (2017-03) Yadav, Pranjul
    The World Health Organization (WHO) estimates that the total healthcare spending in the U.S. is around 18\% of its GDP for the year 2011. Even with such a high per-capita expenditure, the quality of healthcare in U.S. lags behind as compared to the healthcare in other industrialized countries. This inefficient state of the U.S. healthcare system is attributed to the current Fee-for-service (FFS) model. Under the FFS model, healthcare providers (doctors, hospitals) receive payments for every hospital visit or service rendered. The lack of coordination between the service providers and patient outcomes, leads to an increase in the costs associated with the healthcare management, as healthcare providers often recommend expensive treatments. Several legislations have been approved in the recent past to improve the overall U.S. healthcare management while simultaneously reducing the associated costs. The HITECH Act, proposes to spend close to \$30 billion dollars on creating a nationwide repository of electronic Health Records (EHRs). Such a repository would consist of patient attributes such as demographics, laboratories test results, vital information and diagnosis codes. It is hoped that this EHR repository will be a platform to improve care coordination between service providers and patients healthcare outcomes, reduce health disparities thereby improving the overall healthcare management system. Data collected and stored in the EHR (HITECH) and the need to improve care efficiency and outcome (ACT) would help to improve the current state of U.S. healthcare system. Data mining techniques in conjunction with EHRs can be used to develop novel clinical decision making tools, to analyze the prevalence and incidence of diseases and to evaluate the efficacy of existing clinical and surgical interventions. In this thesis we focus on two key aspects of EHR data, i.e. temporality and causation. This becomes more important considering that the temporal nature of EHRs data has not been fully exploited. Further, increasing amounts of clinical evidence suggest that temporal nature is important for the development of clinical decision making tools and techniques. Secondly, several research articles hint at the the presence of antiquated clinical guidelines which are still in practice. In this dissertation, we first describe EHR along with the following terminologies : temporality, causation and heterogeneity. Building on this, we then describe methodologies for extracting non-causal patterns in the absence of longitudinal data. Further, we describe methods to extract non-causal patterns in the presence of longitudinal data. We describe such methodologies in the context of Type-2 Diabetes Mellitus (T2DM). Furthermore, we describe techniques to extract simple and complex causal patterns from longitudinal data in the context of sepsis and T2DM. Finally, we conclude this dissertation, by providing a summary of our work along with future directions.
  • Thumbnail Image
    Item
    The effects of heterogeneity in individual infectiousness on disease modeling predictions
    (2018-06) White , Lauren A.
    Individual variation in infectiousness is generated by heterogeneities in the host, the pathogen, and the environment. However, many models of disease transmission, especially those designed for wildlife and livestock populations, do not typically allow for such variation in individual infectiousness. The objective of my research is to explore the effects of heterogeneity in individual infectiousness on disease modeling predictions within and across populations. My dissertation research explores three different types of heterogeneity that can alter individual infectiousness: (i) host heterogeneity resulting from individual differences in susceptibility, infectiousness, and behavioral contact rates, (ii) contact heterogeneity that arises within a population from underlying social systems and interactions; and (iii) spatial heterogeneity that arises from variation in host density as a function of resource quality and variable individual movement rates across a landscape. An improved understanding of the factors that lead to variability in individual infectiousness and the conditions that necessitate the inclusion of such variability in future disease models will be critical to address the growing global threats of zoonoses and emerging infectious diseases.
  • Thumbnail Image
    Item
    Evaluation of Wild and Cultivated Soybean Genomic Resources for Genotype Variation and Identity
    (2018-08) Mihelich, Nicole
    Cultivated soybean has low genetic diversity due to a strong domestication bottleneck and selection in modern breeding. This lack of diversity limits the identification of genetic loci responsible for traits of interest to breeders. However, recent advancements in genotyping have resulted in an influx of genomic resources for soybean germplasm around the globe. In this thesis, the high-throughput SoySNP50K Chip genotyping of USDA Soybean Germplasm Collection was used to evaluate wild and cultivated soybean accessions for within-accession variation. Intervals of heterogeneity were found in 4% of the collection, representing 870 accessions (Supplemental Table S1.1). The SoySNP50K dataset was also used to compare genotype identity with a dataset of 106 resequenced soybean genomes. Although 78% unambiguous matches were found, some discrepancies in identity were detected. These analyses can be used to harness unutilized standing variation and maintain consistency across datasets to innovate and streamline international efforts for soybean improvement.