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

Opportunities for improving animal welfare in rodent models of epilepsy and seizures

Animal models of epilepsy and seizures, mostly involving mice and rats, are used to understand the pathophysiology of the different forms of epilepsy and their comorbidities, to identify biomarkers, and to discover new antiepileptic drugs and treatments for comorbidities. Such models represent an important area for application of the 3Rs (replacement, reduction and refinement of animal use). This report provides background information and recommendations aimed at minimising pain, suffering and distress in rodent models of epilepsy and seizures in order to improve animal welfare and optimise the quality of studies in this area. The report includes practical guidance on principles of choosing a model, induction procedures, in vivo recordings, perioperative care, welfare assessment, humane endpoints, social housing, environmental enrichment, reporting of studies and data sharing. In addition, some model-specific welfare considerations are discussed, and data gaps and areas for further research are identified. The guidance is based upon a systematic review of the scientific literature, survey of the international epilepsy research community, consultation with veterinarians and animal care and welfare officers, and the expert opinion and practical experience of the members of a Working Group convened by the United Kingdom's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs)

Mind the developmental gap: Identifying adverse drug effects across childhood to evaluate biological mechanisms from growth and development

Adverse drug reactions are a leading cause of morbidity and mortality that costs billions of dollars for the healthcare system. In children, there is increased risk for adverse drug reactions with potentially lasting adverse effects into adulthood. The current pediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. We hypothesize that adverse drug reactions manifest from the interaction between drug exposure and dynamic biological processes during child growth and development. While pediatric pharmacologists have studied and recognized this interaction, the evidence from these studies have focused on a few, well-known drug toxicities largely within animal models that have limited translation to children and their clinical care. Moreover, preclinical studies during drug development do not consider growth and maturation of children, which severely limits our knowledge of drug safety in this population. Post-marketing pediatric drug safety studies, on the other hand, leverage large amounts of observations to identify and characterize adverse drug events in the pediatric population after drugs enter the market. However, these observational studies have been limited to event surveillance and have not focused on evaluating why adverse drug events may manifest in children. We hypothesize that by developing statistical methodologies with prior knowledge of dynamic, shared information during development, we can improve the detection of adverse drug events in children. We further hypothesize that detecting adverse drug events in this way also improves the evaluation of dynamic biological and physiological processes during child growth and development. In chapter 1, we described the pediatric drug safety landscape, dynamic processes from pediatric developmental biology, and motivation for a large-scale and data-driven approach to study the interaction between drug treatment and child development. In chapter 2, using drug event reports collected by the Food and Drug Administration (FDA), we evaluated statistical models for identifying temporal trends of adverse effects across childhood. We found the generalized additive model (GAM), as compared to a popular disproportionality method, show improved detection performance especially of rare pediatric adverse drug events. In chapter 3, we applied covariate-adjusted drug-event GAMs in a systematic way to develop a resource of nearly half a million adverse drug event (ADE) risk estimates across child development stages. We showed that not only do significant ADEs through childhood recapitulate dynamic organ and system maturation, but we also provide granular, development-specific risk for known pediatric drug effects that were previously unknown. Importantly, this approach facilitated the evaluation of dynamic biological processes, such as drug-metabolizer gene expression levels across childhood, that we observed coincided with dynamic risk of adverse drug effects. In chapter 4, we performed several case studies showing population-level evidence for well-known pediatric adverse drug reactions using our generated resource. In addition, we developed an accessible web portal, the Pediatric Drug Safety portal (PDSportal), to retrieve from our resource the population-level evidence of user-specified adverse drug events in the pediatric population across child development stages. In conclusion, we summarize three key research directions in data-driven pediatric drug safety research: quantifying child vs. adult drug safety profiles, predicting pre-clinical drug toxicity across childhood, and detecting genetic susceptibility of pediatric adverse drug events. Our results demonstrate that developing pediatric drug safety methods directly for children using data-driven approaches improves both identification and evaluation of adverse drug events during the period of child growth and development

Using the Literature to Identify Confounders

Prior work in causal modeling has focused primarily on learning graph structures and parameters to model data generating processes from observational or experimental data, while the focus of the literature-based discovery paradigm was to identify novel therapeutic hypotheses in publicly available knowledge. The critical contribution of this dissertation is to refashion the literature-based discovery paradigm as a means to populate causal models with relevant covariates to abet causal inference. In particular, this dissertation describes a generalizable framework for mapping from causal propositions in the literature to subgraphs populated by instantiated variables that reflect observational data. The observational data are those derived from electronic health records. The purpose of causal inference is to detect adverse drug event signals. The Principle of the Common Cause is exploited as a heuristic for a defeasible practical logic. The fundamental intuition is that improbable co-occurrences can be “explained away” with reference to a common cause, or confounder. Semantic constraints in literature-based discovery can be leveraged to identify such covariates. Further, the asymmetric semantic constraints of causal propositions map directly to the topology of causal graphs as directed edges. The hypothesis is that causal models conditioned on sets of such covariates will improve upon the performance of purely statistical techniques for detecting adverse drug event signals. By improving upon previous work in purely EHR-based pharmacovigilance, these results establish the utility of this scalable approach to automated causal inference

Machine learning and computational methods to identify molecular and clinical markers for complex diseases – case studies in cancer and obesity

In biomedical research, applied machine learning and bioinformatics are the essential disciplines heavily involved in translating data-driven findings into medical practice. This task is especially accomplished by developing computational tools and algorithms assisting in detection and clarification of underlying causes of the diseases. The continuous advancements in high-throughput technologies coupled with the recently promoted data sharing policies have contributed to presence of a massive wealth of data with remarkable potential to improve human health care. In concordance with this massive boost in data production, innovative data analysis tools and methods are required to meet the growing demand. The data analyzed by bioinformaticians and computational biology experts can be broadly divided into molecular and conventional clinical data categories. The aim of this thesis was to develop novel statistical and machine learning tools and to incorporate the existing state-of-the-art methods to analyze bio-clinical data with medical applications. The findings of the studies demonstrate the impact of computational approaches in clinical decision making by improving patients risk stratification and prediction of disease outcomes. This thesis is comprised of five studies explaining method development for 1) genomic data, 2) conventional clinical data and 3) integration of genomic and clinical data. With genomic data, the main focus is detection of differentially expressed genes as the most common task in transcriptome profiling projects. In addition to reviewing available differential expression tools, a data-adaptive statistical method called Reproducibility Optimized Test Statistic (ROTS) is proposed for detecting differential expression in RNA-sequencing studies. In order to prove the efficacy of ROTS in real biomedical applications, the method is used to identify prognostic markers in clear cell renal cell carcinoma (ccRCC). In addition to previously known markers, novel genes with potential prognostic and therapeutic role in ccRCC are detected. For conventional clinical data, ensemble based predictive models are developed to provide clinical decision support in treatment of patients with metastatic castration resistant prostate cancer (mCRPC). The proposed predictive models cover treatment and survival stratification tasks for both trial-based and realworld patient cohorts. Finally, genomic and conventional clinical data are integrated to demonstrate the importance of inclusion of genomic data in predictive ability of clinical models. Again, utilizing ensemble-based learners, a novel model is proposed to predict adulthood obesity using both genetic and social-environmental factors. Overall, the ultimate objective of this work is to demonstrate the importance of clinical bioinformatics and machine learning for bio-clinical marker discovery in complex disease with high heterogeneity. In case of cancer, the interpretability of clinical models strongly depends on predictive markers with high reproducibility supported by validation data. The discovery of these markers would increase chance of early detection and improve prognosis assessment and treatment choice