Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disease Research

The document discusses two scientific journals - the Journal of Laboratory Automation (JALA) and the Journal of Biomolecular Screening (JBS). It provides information on their editors, impact factors, indexing in databases, scientific advisors and editorial boards. The goal of both journals is to publish research applying technological advances to scientific exploration and discovery of new therapeutics. Authors are encouraged to submit their work to these peer-reviewed, MEDLINE-indexed journals.

This paper describes the methods of the Treatment In Morning versus Evening (TIME) study, a large prospective randomized open-label blinded endpoint study comparing morning versus evening dosing of antihypertensive medications. The TIME study recruits participants through advertising, primary and secondary care, and patient databases in the UK. Participants self-enroll and consent on a secure website, and are randomized to morning or evening dosing. Follow-ups are conducted by automated email at 1 month and every 3 months thereafter. The study uses a prospective randomized open-label blinded endpoint design to establish if evening dosing is more cardioprotective than morning dosing.

The document discusses Peter Embi's approach to presenting on clinical and translational research informatics literature from the past year. It provides an overview of Embi's search strategy and categorization of papers, which involved searching literature databases and recommendations from colleagues. The presentation will focus on summarizing representative papers within categories like data sharing/reuse, methods and systems, recruitment and eligibility, and trends in clinical research informatics.

Clinical research informatics involves using informatics tools to support clinical research activities like managing clinical trials data and conducting secondary research using clinical data. It can help make clinical trials more efficient by facilitating study design, subject recruitment from electronic medical records and social media, data collection and management, and data analysis. National initiatives aim to develop informatics tools and networks to help researchers access data and accelerate clinical research.

NegBio is a tool that detects negation and uncertainty in radiology reports with high accuracy. It first uses MetaMap to map report text to medical concepts, then applies dependency parsing and rules to identify negative and equivocal findings. Evaluated on several datasets, NegBio significantly outperformed previous state-of-the-art methods, achieving F-scores over 95% in some cases. Future work includes exploring its use in other clinical texts beyond radiology reports. NegBio is available as open source software.

The Clinical and Molecular Epidemiology Shared Resource (CMESR) provides services including the collection, processing, and storage of biological samples and clinical data from various cancer patient populations. It assists with IRB protocols, questionnaire data entry and analysis, and sample and data requests for cancer research. The CMESR is located at the Lombardi Cancer Center and houses samples and data on prostate, head and neck, colon, lung, and breast cancers.

The document discusses the importance of standardized practices and quality control for biobanks in order to facilitate research using human biosamples. It notes that the UC system's biobanks currently use diverse practices. The UC Biobanking and Research Integration Across Disciplines (BRAID) working group aims to establish standards to recognize high-quality biobanks within the UC system and help them share samples.

Discussion of provenance usage in the Learning Health System paradigm, as implemented in the TRANSFoRm project, with focus on security requirements and how they can be addressed using provenance graph abstraction.

tranSMART Community Meeting 5-7 Nov 13 - Session 5: Advancing tranSMART Analytical Capabilities with Knowledge Content Sirimon Ocharoen, Thomson Reuters To effectively analyze data in tranSMART, biological analysis/knowledge-based approach is needed. Through a case study, we will demonstrate how system biology content can be integrated in tranSMART to enable functional analysis and biological interpretation. We will also share our experience and user feedbacks from various projects.

An overview of the oncology clinical trials network (CTNeT) which is being implemented throughout Texas. The non-profit network is a first of its kind and combines the innovative science of Texas cancer centers with the expertise and resources of both academic and community oncologists throughout the state. To learn more, visit www.ctnet.org

This document discusses standardizing clinical guidelines for adverse event classification using the Adverse Event Reporting Ontology (AERO). It describes how AERO encodes guidelines like Brighton Collaboration for diagnosing conditions like anaphylaxis from vaccine reports. It also discusses how AERO can integrate data like classifying reports in the Vaccine Adverse Event Reporting System according to encoded guidelines and linking to other datasets like DrugBank. The goal is automated adverse event diagnosis and integration of reporting data.

Bryan Soper has extensive experience in pharmaceutical competitive intelligence, medical writing, and data analysis. He currently performs contract work analyzing clinical trials and assessing drug approval likelihoods for Genentech. Previously he has analyzed cancer models and clinical trials to identify correlations. He holds a PhD in Molecular Biology from Cornell University and has worked as a postdoctoral scholar at UCSF investigating drug targets.

Large Scale Data Management Computation and Analysis for Quantitative Imaging Research Talk at the 2016 QIN Annual Meeting — covers resources developed for the Quantitative Imaging Network. Includes TCIA data curation, APIs, supported data types, as well as co-located computing and systematic phenotyping of imaging biomarkers

This document summarizes a presentation on errors in non-gynaecological cytology and methods for their identification and prevention. It discusses different types of errors, methods for error detection including cytologic-histologic correlation, and data on error frequencies from multiple institutions. Common error sites included lung, bladder, and thyroid specimens. Root cause analysis found sources of error included interpretive issues, sampling errors, and lack of communication between clinicians and pathologists. Quality improvement approaches discussed standardized diagnostics, improved specimen adequacy assessments, and immediate specimen interpretation. A case study on thyroid FNA errors found interventions like standardized diagnoses and immediate interpretation improved sensitivity and reduced non-interpretability and atypical diagnoses.

Poster we presented at 2017 AMIA Joint Summits on Clinical and Translational Research Informatics. In this research data delivery project, we explored a less traveled path of building a clinical data mart for a registry study on kidney transplant patients, based on the institutional instance of the EMR data, translated into the OMOP (Observational Medical Outcomes Partnership) common data model.