Knowledge-Driven Methods for Geographic Information Extraction in the Biomedical Domain

abstract: Accounting for over a third of all emerging and re-emerging infections, viruses represent a major public health threat, which researchers and epidemiologists across the world have been attempting to contain for decades. Recently, genomics-based surveillance of viruses through methods such as virus phylogeography has grown into a popular tool for infectious disease monitoring. When conducting such surveillance studies, researchers need to manually retrieve geographic metadata denoting the location of infected host (LOIH) of viruses from public sequence databases such as GenBank and any publication related to their study. The large volume of semi-structured and unstructured information that must be reviewed for this task, along with the ambiguity of geographic locations, make it especially challenging. Prior work has demonstrated that the majority of GenBank records lack sufficient geographic granularity concerning the LOIH of viruses. As a result, reviewing full-text publications is often necessary for conducting in-depth analysis of virus migration, which can be a very time-consuming process. Moreover, integrating geographic metadata pertaining to the LOIH of viruses from different sources, including different fields in GenBank records as well as full-text publications, and normalizing the integrated metadata to unique identifiers for subsequent analysis, are also challenging tasks, often requiring expert domain knowledge. Therefore, automated information extraction (IE) methods could help significantly accelerate this process, positively impacting public health research. However, very few research studies have attempted the use of IE methods in this domain. This work explores the use of novel knowledge-driven geographic IE heuristics for extracting, integrating, and normalizing the LOIH of viruses based on information available in GenBank and related publications; when evaluated on manually annotated test sets, the methods were found to have a high accuracy and shown to be adequate for addressing this challenging problem. It also presents GeoBoost, a pioneering software system for georeferencing GenBank records, as well as a large-scale database containing over two million virus GenBank records georeferenced using the algorithms introduced here. The methods, database and software developed here could help support diverse public health domains focusing on sequence-informed virus surveillance, thereby enhancing existing platforms for controlling and containing disease outbreaks.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Phylodynamics of H5N1 Highly Pathogenic Avian Influenza in Europe, 2005-2010: Potential for Molecular Surveillance of New Outbreaks.

Previous Bayesian phylogeographic studies of H5N1 highly pathogenic avian influenza viruses (HPAIVs) explored the origin and spread of the epidemic from China into Russia, indicating that HPAIV circulated in Russia prior to its detection there in 2005. In this study, we extend this research to explore the evolution and spread of HPAIV within Europe during the 2005-2010 epidemic, using all available sequences of the hemagglutinin (HA) and neuraminidase (NA) gene regions that were collected in Europe and Russia during the outbreak. We use discrete-trait phylodynamic models within a Bayesian statistical framework to explore the evolution of HPAIV. Our results indicate that the genetic diversity and effective population size of HPAIV peaked between mid-2005 and early 2006, followed by drastic decline in 2007, which coincides with the end of the epidemic in Europe. Our results also suggest that domestic birds were the most likely source of the spread of the virus from Russia into Europe. Additionally, estimates of viral dispersal routes indicate that Russia, Romania, and Germany were key epicenters of these outbreaks. Our study quantifies the dynamics of a major European HPAIV pandemic and substantiates the ability of phylodynamic models to improve molecular surveillance of novel AIVs

Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records

Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it’s unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients’ self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR). To enable this comparison, we encoded patients CVD information from the free-text data of EDRs into a structured format using natural language processing (NLP). Overall, our NLP approach achieved promising performance extracting patients’ CVD-related information. We observed disagreement between self-reported EDR data and physician-diagnosed EMR data

STAMINA: Bioinformatics Platform for Monitoring and Mitigating Pandemic Outbreaks

Data Availability Statement: All data driven applications used the our world in data COVID-19 datasets, complimented by proprietary datasets share by the STAMINA consortium.Copyright © 2022 by the authors. This paper presents the components and integrated outcome of a system that aims to achieve early detection, monitoring and mitigation of pandemic outbreaks. The architecture of the platform aims at providing a number of pandemic-response-related services, on a modular basis, that allows for the easy customization of the platform to address user’s needs per case. This customization is achieved through its ability to deploy only the necessary, loosely coupled services and tools for each case, and by providing a common authentication, data storage and data exchange infrastructure. This way, the platform can provide the necessary services without the burden of additional services that are not of use in the current deployment (e.g., predictive models for pathogens that are not endemic to the deployment area). All the decisions taken for the communication and integration of the tools that compose the platform adhere to this basic principle. The tools presented here as well as their integration is part of the project STAMINA.The paper presented is based on research undertaken as part of the European Commission-funded project STAMINA (Grant Agreement 883441)

Exploring the phylodynamics, genetic reassortment and RNA secondary structure formation patterns of orthomyxoviruses by comparative sequence analysis

RNA viruses are among the most virulent microorganisms that threaten the health of humans and livestock. Among the most socio-economically important of the known RNA viruses are those found in the family Orthomyxovirus. In this era of rapid low-cost genome sequencing and advancements in computational biology techniques, many previously difficult research questions relating to the molecular epidemiology and evolutionary dynamics of these viruses can now be answered with ease. Using sequence data together with associated meta-data, in chapter two of this dissertation I tested the hypothesis that the Influenza A/H1N1 2009 pandemic virus was introduced multiple times into Africa, and subsequently dispersed heterogeneously across the continent. I further tested to what degree factors such as road distances and air travel distances impacted the observed pattern of spread of this virus in Africa using a generalised linear modelbased approach. The results suggested that their were multiple simultaneous introductions of 2009 pandemic A/H1N1 into Africa, and geographical distance and human mobility through air travel played an important role towards dissemination. In chapter three, I set out to test two hypotheses: (1) that there is no difference in the frequency of reassortments among the segments that constitute influenza virus genomes; and (2) that there is epochal temporal reassortment among influenza viruses and that all geographical regions are equally likely sources of epidemiologically important influenza virus reassortant lineages. The findings suggested that surface segments are more frequently exchanges than internal genes and that North America/Asia, Oceania, and Asia could be the most likely source locations for reassortant Influenza A, B and C virus lineages respectively. In chapter four of this thesis, I explored the formation of RNA secondary structures within the genomes of orthomyxoviruses belonging to five genera: Influenza A, B and C, Infectious Salmon Anaemia Virus and Thogotovirus using in silico RNA folding predictions and additional molecular evolution and phylogenetic tests to show that structured regions may be biologically functional. The presence of some conserved structures across the five genera is likely a reflection of the biological importance of these structures, warranting further investigation regarding their role in the evolution and possible development of antiviral resistance. The studies herein demonstrate that pathogen genomics-based analytical approaches are useful both for understanding the mechanisms that drive the evolution and spread of rapidly evolving viral pathogens such as orthomyxoviruses, and for illuminating how these approaches could be leveraged to improve the management of these pathogens