The CAMH Neuroinformatics Platform: A Hospital-Focused Brain-CODE Implementation

Investigations of mental illness have been enriched by the advent and maturation of neuroimaging technologies and the rapid pace and increased affordability of molecular sequencing techniques, however, the increased volume, variety and velocity of research data, presents a considerable technical and analytic challenge to curate, federate and interpret. Aggregation of high-dimensional datasets across brain disorders can increase sample sizes and may help identify underlying causes of brain dysfunction, however, additional barriers exist for effective data harmonization and integration for their combined use in research. To help realize the potential of multi-modal data integration for the study of mental illness, the Centre for Addiction and Mental Health (CAMH) constructed a centralized data capture, visualization and analytics environment—the CAMH Neuroinformatics Platform—based on the Ontario Brain Institute (OBI) Brain-CODE architecture, towards the curation of a standardized, consolidated psychiatric hospital-wide research dataset, directly coupled to high performance computing resources

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

METADATA MANAGEMENT FOR CLINICAL DATA INTEGRATION

Clinical data have been continuously collected and growing with the wide adoption of electronic health records (EHR). Clinical data have provided the foundation to facilitate state-of-art researches such as artificial intelligence in medicine. At the same time, it has become a challenge to integrate, access, and explore study-level patient data from large volumes of data from heterogeneous databases. Effective, fine-grained, cross-cohort data exploration, and semantically enabled approaches and systems are needed. To build semantically enabled systems, we need to leverage existing terminology systems and ontologies. Numerous ontologies have been developed recently and they play an important role in semantically enabled applications. Because they contain valuable codified knowledge, the management of these ontologies, as metadata, also requires systematic approaches. Moreover, in most clinical settings, patient data are collected with the help of a data dictionary. Knowledge of the relationships between an ontology and a related data dictionary is important for semantic interoperability. Such relationships are represented and maintained by mappings. Mappings store how data source elements and domain ontology concepts are linked, as well as how domain ontology concepts are linked between different ontologies. While mappings are crucial to the maintenance of relationships between an ontology and a related data dictionary, they are commonly captured by CSV files with limits capabilities for sharing, tracking, and visualization. The management of mappings requires an innovative, interactive, and collaborative approach. Metadata management servers to organize data that describes other data. In computer science and information science, ontology is the metadata consisting of the representation, naming, and definition of the hierarchies, properties, and relations between concepts. A structural, scalable, and computer understandable way for metadata management is critical to developing systems with the fine-grained data exploration capabilities. This dissertation presents a systematic approach called MetaSphere using metadata and ontologies to support the management and integration of clinical research data through our ontology-based metadata management system for multiple domains. MetaSphere is a general framework that aims to manage specific domain metadata, provide fine-grained data exploration interface, and store patient data in data warehouses. Moreover, MetaSphere provides a dedicated mapping interface called Interactive Mapping Interface (IMI) to map the data dictionary to well-recognized and standardized ontologies. MetaSphere has been applied to three domains successfully, sleep domain (X-search), pressure ulcer injuries and deep tissue pressure (SCIPUDSphere), and cancer. Specifically, MetaSphere stores domain ontology structurally in databases. Patient data in the corresponding domains are also stored in databases as data warehouses. MetaSphere provides a powerful query interface to enable interaction between human and actual patient data. Query interface is a mechanism allowing researchers to compose complex queries to pinpoint specific cohort over a large amount of patient data. The MetaSphere framework has been instantiated into three domains successfully and the detailed results are as below. X-search is publicly available at https://www.x-search.net with nine sleep domain datasets consisting of over 26,000 unique subjects. The canonical data dictionary contains over 900 common data elements across the datasets. X-search has received over 1800 cross-cohort queries by users from 16 countries. SCIPUDSphere has integrated a total number of 268,562 records containing 282 ICD9 codes related to pressure ulcer injuries among 36,626 individuals with spinal cord injuries. IMI is publicly available at http://epi-tome.com/. Using IMI, we have successfully mapped the North American Association of Central Cancer Registries (NAACCR) data dictionary to the National Cancer Institute Thesaurus (NCIt) concepts

Challenges and opportunities beyond structured data in analysis of electronic health records

Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time-consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well-designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

Antennas and Electromagnetics Research via Natural Language Processing.

Advanced techniques for performing natural language processing (NLP) are being utilised to devise a pioneering methodology for collecting and analysing data derived from scientific literature. Despite significant advancements in automated database generation and analysis within the domains of material chemistry and physics, the implementation of NLP techniques in the realms of metamaterial discovery, antenna design, and wireless communications remains at its early stages. This thesis proposes several novel approaches to advance research in material science. Firstly, an NLP method has been developed to automatically extract keywords from large-scale unstructured texts in the area of metamaterial research. This enables the uncovering of trends and relationships between keywords, facilitating the establishment of future research directions. Additionally, a trained neural network model based on the encoder-decoder Long Short-Term Memory (LSTM) architecture has been developed to predict future research directions and provide insights into the influence of metamaterials research. This model lays the groundwork for developing a research roadmap of metamaterials. Furthermore, a novel weighting system has been designed to evaluate article attributes in antenna and propagation research, enabling more accurate assessments of impact of each scientific publication. This approach goes beyond conventional numeric metrics to produce more meaningful predictions. Secondly, a framework has been proposed to leverage text summarisation, one of the primary NLP tasks, to enhance the quality of scientific reviews. It has been applied to review recent development of antennas and propagation for body-centric wireless communications, and the validation has been made available for comparison with well-referenced datasets for text summarisation. Lastly, the effectiveness of automated database building in the domain of tunable materials and their properties has been presented. The collected database will use as an input for training a surrogate machine learning model in an iterative active learning cycle. This model will be utilised to facilitate high-throughput material processing, with the ultimate goal of discovering novel materials exhibiting high tunability. The approaches proposed in this thesis will help to accelerate the discovery of new materials and enhance their applications in antennas, which has the potential to transform electromagnetic material research

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications