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- Creators: Baral, Chitta
- Creators: Hamilton, Kerry
Description
Systematic Reviews (SRs) aim to synthesize the totality of evidence for clinical practice and are important in making clinical practice guidelines and health policy decisions. However, conducting SRs manually is a laborious and time-consuming process. This challenge is growing due to the increase in the number of databases to search and the papers being published. Hence, the automation of SRs is an essential task. The goal of this thesis work is to develop Natural Language Processing (NLP)-based classifiers to automate the title and abstract-based screening for clinical SRs based on inclusion/exclusion criteria. In clinical SRs, a high-sensitivity system is a key requirement. Most existing methods for SRs use binary classification systems trained on labeled data to predict inclusion/exclusion. While previous studies have shown that NLP-based classification methods can automate title and abstract-based screening for SRs, methods for achieving high-sensitivity have not been empirically studied. In addition, the training strategy for binary classification has several limitations: (1) it ignores the inclusion/exclusion criteria, (2) lacks generalization ability, (3) suffers from low resource data, and (4) fails to achieve reasonable precision at high-sensitivity levels. This thesis work presents contributions to several aspects of the clinical systematic review domain. First, it presents an empirical study of NLP-based supervised text classification and high-sensitivity methods on datasets developed from six different SRs in the clinical domain. Second, this thesis work provides a novel approach to view SR as a Question Answering (QA) problem in order to overcome the limitations of the binary classification training strategy; and propose a more general abstract screening model for different SRs. Finally, this work provides a new QA-based dataset for six different SRs which is made available to the community.
ContributorsParmar, Mihir Prafullsinh (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Thesis advisor) / Riaz, Irbaz B (Committee member) / Arizona State University (Publisher)
Created2021
Description
Water is a scarce resource that is recycled through wastewater treatment plants (WWTPs) to help fulfill the demand for water. Agriculture is a large consumer of water, indicating that WWTP-treated water is proportionally applied to crops at a high rate. Recycled water is highly regulated but is capable of containing high-risk pathogens and contaminants despite the efforts of physical and microbial treatments throughout the WWTP process. WWTPs are also producers of biosolids, treated sewage sludge regulated by the EPA that can be applied in agricultural settings to act as a fertilizer. Biosolids are a useful fertilizer as they are rich in nitrogen and contain many beneficial nutrients for soil and crops. Due to biosolids being a by-product of recycled water, they are susceptible to containing the same pathogens and contaminants that can be transferred in the WWTP systems. Antibiotic resistance (AR) is an ever-growing threat on a global scale and is one of the areas of concern for consideration of pathogen spread from WWTPs. Antibiotic resistance bacteria, created through mutation of bacterial plasmids producing antibiotic resistance genes (ARGs), have been quantified and studied to help mitigate the risk posed by continued AR spread in the environment. This study aims to produce a comprehensive collection of quantified ARG concentration data in biosolids, as well as producing a QMRA model integrating Monte Carlo distributions to provide groundwork for understanding of the direct dosage and consumption of ARGs to the standard U.S. citizen. The study determined that sul1, sul2, tetM, and tetO are ARGs of high concern in biosolid samples based on current concentration data of biosolid samples. The resulting dose models and gene concentration distributions provide data to support the need to mitigate AR risk presented by agricultural biosolid application.
ContributorsMorgan, Grace (Author) / Hamilton, Kerry (Thesis director) / Muenich, Rebecca (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2022-05