Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Enabling precision medicine in neonatology, an integrated repository for preterm birth research.

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research

Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, and Tailoring Individual Treatments

Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases often have diverse underlying causes and can manifest differently among patients. Besides the external factors, the development of chronic disease is also influenced by internal signals. The DNA sequence-level differences have been proven responsible for constant predisposition to chronic diseases. Given these challenges, data must be analyzed at various scales, ranging from single nucleotide polymorphisms (SNPs) to individuals and populations, to better understand disease mechanisms and provide precision medicine. Therefore, this research aimed to develop an automated pipeline from building predictive models and estimating individual treatment effects based on the structured data of general electronic health records (EHRs) to identifying genetic variations (e.g., SNPs) associated with diseases to unravel the genetic underpinnings of chronic diseases. First, we used structured EHRs to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. In this step, we employed causal inference methods (constraint-based and functional causal models) for feature selection and utilized Markov chains, attention long short-term memory (LSTM), and Gaussian process (GP). SHapley Additive exPlanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs) further extended the work to identify important clinical features. Next, I developed a novel counterfactual-based method to predict individual treatment effects (ITE) from observational data. To discern a “balanced” representation so that treated and control distributions look similar, we disentangled the doctor’s preference from the covariance and rebuilt the representation of the treated and control groups. We use integral probability metrics to measure distances between distributions. The expected ITE estimation error of a representation was the sum of the standard generalization error of that representation and the distance between the distributions induced. Finally, we performed genome-wide association studies (GWAS) based on the stage information we extracted from our unsupervised disease progression model to identify the biomarkers and explore the genetic correction between the disease and its phenotypes

Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression

Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally, focusing on depression-related parts of the BDMM, we evaluated correspondence with results from logistic regression, text-mining and molecular-level measures for comorbidities such as genetic overlap and the interactome-based association score. We used a subset of the UK Biobank Resource, a cross-sectional dataset including 247 diseases and 117,392 participants who filled out a detailed questionnaire about mental health. The sparse comorbidity map confirmed that depressed patients frequently suffer from both psychiatric and somatic comorbid disorders. Notably, anxiety and obesity show strong and direct relationships with depression. The BDMM identified further directly co-morbid somatic disorders, e.g. irritable bowel syndrome, fibromyalgia, or migraine. Using the subnetwork of depression and metabolic disorders for functional analysis, the interactome-based system-level score showed the best agreement with the sparse disease network. This indicates that these epidemiologically strong disease-disease relations have improved correspondence with expected molecular-level mechanisms. The substantially fewer number of comorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise methods suggested. The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet: bioinformatics.mit.bme.hu/UKBNetworks

Autism spectrum disorder: molecular profiling analysis and identification of candidate genes through complex Systems Biology approaches

Los trastornos del espectro autista (TEA) engloban una amplia gama de afecciones neurológicas y del desarrollo caracterizadas por alteraciones en las habilidades sociales, conductas repetitivas, habla y comunicación no verbal. Existen muchos subtipos de autismo, influenciados por una combinación de factores genéticos, neurológicos, inmunológicos y ambientales y frecuentemente acompañados de una carga sustancial de comorbilidad. La gran variabilidad clínica y etiológica entre los individuos con TEA hace que la biología de sistemas sea el enfoque más prometedor en la búsqueda de tratamientos eficaces. En esta tesis doctoral se exploran diferentes estrategias de biología de sistemas para descifrar la heterogeneidad clínica y neurobiológica del autismo mediante la búsqueda de genes candidatos. Nuestro objetivo es desentrañar la complejidad de los mecanismos neurológicos subyacentes a los TEA, sus comorbilidades y las posibles limitaciones evolutivas diferenciadoras, para identificar nuevos genes y rutas biológicas clave en los resultados funcionales, contribuyendo al avance de la medicina personalizada.Autism spectrum disorders (ASD) encompass a wide range of neurological and developmental conditions characterized by alterations in social skills, repetitive behaviors, speech and nonverbal communication. There are many subtypes of autism, influenced by a combination of genetic, neurological, immunological and environmental factors and often accompanied by a substantial burden of comorbidity. The enormous clinical and etiological variability among individuals with ASD makes systems biology the most promising approach in the search for effective treatments. In this doctoral thesis different strategies of the emerging field of systems biology are explored to better understand the clinical and neurobiological heterogeneity of autism by using genome-wide search for autism candidate genes. Our goal is to disentangle the complexity of ASD underlying neurological mechanisms, overlapping genes, comorbidities and differential evolutionary constraints, in order to identify novel genes and biological pathways that may specifically impact functional outcomes, contributing to advance in the field of personalized medicine.Tesis Univ. Jaén. Departamento de Biología Experimental. Leída el 24 de junio de 2021

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Facilitating and Enhancing Biomedical Knowledge Translation: An in Silico Approach to Patient-centered Pharmacogenomic Outcomes Research

Current research paradigms such as traditional randomized control trials mostly rely on relatively narrow efficacy data which results in high internal validity and low external validity. Given this fact and the need to address many complex real-world healthcare questions in short periods of time, alternative research designs and approaches should be considered in translational research. In silico modeling studies, along with longitudinal observational studies, are considered as appropriate feasible means to address the slow pace of translational research. Taking into consideration this fact, there is a need for an approach that tests newly discovered genetic tests, via an in silico enhanced translational research model (iS-TR) to conduct patient-centered outcomes research and comparative effectiveness research studies (PCOR CER). In this dissertation, it was hypothesized that retrospective EMR analysis and subsequent mathematical modeling and simulation prediction could facilitate and accelerate the process of generating and translating pharmacogenomic knowledge on comparative effectiveness of anticoagulation treatment plan(s) tailored to well defined target populations which eventually results in a decrease in overall adverse risk and improve individual and population outcomes. To test this hypothesis, a simulation modeling framework (iS-TR) was proposed which takes advantage of the value of longitudinal electronic medical records (EMRs) to provide an effective approach to translate pharmacogenomic anticoagulation knowledge and conduct PCOR CER studies. The accuracy of the model was demonstrated by reproducing the outcomes of two major randomized clinical trials for individualizing warfarin dosing. A substantial, hospital healthcare use case that demonstrates the value of iS-TR when addressing real world anticoagulation PCOR CER challenges was also presented

Real-world and genetic evidence for the management of opioid epidemic and COVID-19 pandemic: population-based studies

Background: The widespread use of opioids and the emergence of SARS-CoV-2 represent two pressing public health crises that require evidence-based decision-making. This thesis comprises six interconnected studies aimed at translating large, routinely-collected and Biobank data into reliable evidence to inform the management of the ongoing opioid epidemic and COVID-19 pandemic. Methods: Four data sources were studied: primary care records from SIDIAP (Spain), US claims, and the UK Biobank. Exposures included opioids, ibuprofen, COVID-19 vaccines, human leucocyte antigen (HLA) genes, and SARS-CoV-2 infections. Cohort studies were used as the primary design, complemented by statistical techniques including regression, propensity scores, survival analyses, and negative and positive control outcomes, as well as Mendelian randomization. Study outcomes were adverse drug events, COVID-19 susceptibility and severity, and related complications. Results: Incident opioid use increased in Catalonia from 2007 to 2019, with Tramadol being the most frequently used opioid in 2019. Compared to codeine, tramadol was associated with a higher risk of all-cause mortality, cardiovascular events, and fractures. No differential risk of COVID-19 was observed among users of ibuprofen versus other analgesics. When compared to two doses of ChAdOx1, vaccination with BNT162b2 was associated with lower risks of COVID-19 infection and hospitalization, respectively, during the study period when the Delta variant was dominant. Six independent HLA alleles significantly affected antibody response to COVID-19 vaccines, and the aggregated genetic score had a strong, collective, and causal influence on breakthrough COVID-19. COVID-19 infection was associated with an increased risk of venous thromboembolism (VTE) within 30 days, with highest risk in unvaccinated individuals. People with older age, male sex, obesity, and inherited thrombophilia were also at a higher VTE risk post-COVID-19. Conclusion: The integration of real-world and linked biobank data can be effectively leveraged using advanced analytical tools to generate timely and actionable evidence to tackle global health crises like the ongoing opioids epidemic and COVID-19 pandemic

Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects

Zolotareva O, Saik OV, Königs C, et al. Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects. Scientific Reports. 2019;9(1): 16302.Asthma and hypertension are complex diseases coinciding more frequently than expected by chance. Unraveling the mechanisms of comorbidity of asthma and hypertension is necessary for choosing the most appropriate treatment plan for patients with this comorbidity. Since both diseases have a strong genetic component in this article we aimed to find and study genes simultaneously associated with asthma and hypertension. We identified 330 shared genes and found that they form six modules on the interaction network. A strong overlap between genes associated with asthma and hypertension was found on the level of eQTL regulated genes and between targets of drugs relevant for asthma and hypertension. This suggests that the phenomenon of comorbidity of asthma and hypertension may be explained by altered genetic regulation or result from drug side effects. In this work we also demonstrate that not only drug indications but also contraindications provide an important source of molecular evidence helpful to uncover disease mechanisms. These findings give a clue to the possible mechanisms of comorbidity and highlight the direction for future research