Doctor of Philosophy

dissertationWith the growing national dissemination of the electronic health record (EHR), there are expectations that the public will benefit from biomedical research and discovery enabled by electronic health data. Clinical data are needed for many diseases and conditions to meet the demands of rapidly advancing genomic and proteomic research. Many biomedical research advancements require rapid access to clinical data as well as broad population coverage. A fundamental issue in the secondary use of clinical data for scientific research is the identification of study cohorts of individuals with a disease or medical condition of interest. The problem addressed in this work is the need for generalized, efficient methods to identify cohorts in the EHR for use in biomedical research. To approach this problem, an associative classification framework was designed with the goal of accurate and rapid identification of cases for biomedical research: (1) a set of exemplars for a given medical condition are presented to the framework, (2) a predictive rule set comprised of EHR attributes is generated by the framework, and (3) the rule set is applied to the EHR to identify additional patients that may have the specified condition. iv Based on this functionality, the approach was termed the ‘cohort amplification' framework. The development and evaluation of the cohort amplification framework are the subject of this dissertation. An overview of the framework design is presented. Improvements to some standard associative classification methods are described and validated. A qualitative evaluation of predictive rules to identify diabetes cases and a study of the accuracy of identification of asthma cases in the EHR using frameworkgenerated prediction rules are reported. The framework demonstrated accurate and reliable rules to identify diabetes and asthma cases in the EHR and contributed to methods for identification of biomedical research cohorts

How Much is E-Commerce Worth to Rural Businesses?

The probability of a business paying for an e-commerce presence ultimately depends on demographic features, experiences with e-commerce, technological expertise, and knowledge of e-commerce opportunities and limitations. Results allow for the assignment of probabilities associated with various business profiles to determine the willingness to pay for an e-commerce presence.Research and Development/Tech Change/Emerging Technologies,

Website Usage Information for Rural-Based E-Commerce Start-Ups

Usage patterns for start-up agricultural and non-agricultural websites, as well as product and service oriented websites, were studied to determine differences in the number of unique visitors, usage based on the day and time of the week, total time spent on the website, and how the visitor found the website.Research and Development/Tech Change/Emerging Technologies,

Prediction of Critical Illness During Out-of-Hospital Emergency Care

CONTEXT: Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care. OBJECTIVES: To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization. DESIGN AND SETTING: Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities. PATIENTS: Nontrauma, non-cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144,913) were linked to hospital discharge data and randomly split into development (n = 87,266 [60%]) and validation (n = 57,647 [40%]) cohorts. MAIN OUTCOME MEASURE: Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization. RESULTS: Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17). CONCLUSIONS: In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent populationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85143/1/Seymour - JAMA-2010-747-54.pdf11

HWN* Mobility Management Considering QoS, Optimisation and Cross Layer Issues

In this paper, we address mobility management for 4th generation heterogeneous networks from a quality of service (QoS), optimisation and cross layer design perspective. Users are classified as high profile, normal profile and low profile according to their differentiated service requirements. Congestion avoidance control and adaptive handover mechanisms are implemented for efficient cooperation within the mobile heterogeneous network environment consisting of a TDMA network, ad hoc network and relay nodes. A previous proposed routing algorithm is also revised to include mobility management

Resource Sharing via Planed Relay for HWN

We present an improved version of adaptive distributed cross-layer routing algorithm (ADCR) for hybrid wireless network with dedicated relay stations () in this paper. A mobile terminal (MT) may borrow radio resources that are available thousands mile away via secure multihop RNs, where RNs are placed at pre-engineered locations in the network. In rural places such as mountain areas, an MT may also communicate with the core network, when intermediate MTs act as relay node with mobility. To address cross-layer network layers routing issues, the cascaded ADCR establishes routing paths across MTs, RNs, and cellular base stations (BSs) and provides appropriate quality of service (QoS). We verify the routing performance benefits of over other networks by intensive simulation

Reliable Delay Constrained Multihop Broadcasting in VANETs

Vehicular communication is regarded as a major innovative feature for in-car technology. While improving road safety is unanimously considered the major driving factor for the deployment of Intelligent Vehicle Safety Systems, the challenges relating to reliable multi-hop broadcasting are exigent in vehicular networking. In fact, safety applications must rely on very accurate and up-to-date information about the surrounding environment, which in turn requires the use of accurate positioning systems and smart communication protocols for exchanging information. Communications protocols for VANETs must guarantee fast and reliable delivery of information to all vehicles in the neighbourhood, where the wireless communication medium is shared and highly unreliable with limited bandwidth. In this paper, we focus on mechanisms that improve the reliability of broadcasting protocols, where the emphasis is on satisfying the delay requirements for safety applications. We present the Pseudoacknowledgments (PACKs) scheme and compare this with existing methods over varying vehicle densities in an urban scenario using the network simulator OPNET

The Delta E-Commerce Connection: Preliminary Findings

A USDA Fund for Rural America project is creating economic opportunities for small agricultural and rural businesses in the Lower Mississippi Delta by assisting in e-commerce business development. Select rural businesses are provided technical support in web site development, marketing strategy formulation, electronic retailing services, and space on a secure server for one year. Businesses retaining web sites after this time assume responsibility for maintaining and funding the site. Characteristics of rural businesses adopting e-commerce are compared with those not adopting. Preliminary results suggest the amount of time invested in initial web site design, satisfaction with design, economic benefits from owning a web site, and number of levels traveled in the e-commerce roadmap (a measure of technological progress) as determinants of success. These findings will assist in selecting and guiding participant involvement to maximize the likelihood of success

Quantifying the impact of community quarantine on SARS transmission in Ontario: estimation of secondary case count difference and number needed to quarantine

<p>Abstract</p> <p>Background</p> <p>Community quarantine is controversial, and the decision to use and prepare for it should be informed by specific quantitative evidence of benefit. Case-study reports on 2002-2004 SARS outbreaks have discussed the role of quarantine in the community in transmission. However, this literature has not yielded quantitative estimates of the reduction in secondary cases attributable to quarantine as would be seen in other areas of health policy and cost-effectiveness analysis.</p> <p>Methods</p> <p>Using data from the 2003 Ontario, Canada, SARS outbreak, two novel expressions for the impact of quarantine are presented. Secondary Case Count Difference (SCCD) reflects reduction in the average number of transmissions arising from a SARS case in quarantine, relative to not in quarantine, at onset of symptoms. SCCD was estimated using Poisson and negative binomial regression models (with identity link function) comparing the number of secondary cases to each index case for quarantine relative to non-quarantined index cases. The inverse of this statistic is proposed as the number needed to quarantine (NNQ) to prevent one additional secondary transmission.</p> <p>Results</p> <p>Our estimated SCCD was 0.133 fewer secondary cases per quarantined versus non-quarantined index case; and a NNQ of 7.5 exposed individuals to be placed in community quarantine to prevent one additional case of transmission in the community. This analysis suggests quarantine can be an effective preventive measure, although these estimates lack statistical precision.</p> <p>Conclusions</p> <p>Relative to other health policy areas, literature on quarantine tends to lack in quantitative expressions of effectiveness, or agreement on how best to report differences in outcomes attributable to control measure. We hope to further this discussion through presentation of means to calculate and express the impact of population control measures. The study of quarantine effectiveness presents several methodological and statistical challenges. Further research and discussion are needed to understand the costs and benefits of enacting quarantine, and this includes a discussion of how quantitative benefit should be communicated to decision-makers and the public, and evaluated.</p