Notes

Any fenestrated continual ancient hypoglossal artery sheltering the cracked aneurysm: An incident statement.
Two types of non-lattice based ontology quality assurance (OQA) tasks were highlighted to demonstrate use cases of WINS in sense-making of such non-lattice subgraphs.Palliative care is a specialized service with proven efficacy in improving patients' quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e.g., knowledge) related to its adoption. Traditionally, these determinants are measured with questionnaires. In this study, we explored Twitter to reveal these determinants guided by the Integrated Behavioral Model. A secondary goal is to assess the feasibility of extracting user demographics from Twitter data-a significant shortcoming in existing studies that limits our ability to explore more fine-grained research questions (e.g., gender difference). Thus, we collected, preprocessed, and geocoded palliative care-related tweets from 2013 to 2019 and then built classifiers to 1) categorize tweets into promotional vs. consumer discussions, and 2) extract user gender. Using topic modeling, we explored whether the topics learned from tweets are comparable to responses of palliative care-related questions in the Health Information National Trends Survey.Despite an abundance of information in clinical genetic testing reports, information is oftentimes not well documented/utilized for decision making. Unstructured information in genetic reports can contribute to long-term patient management and future translational research. Thus, we proposed a knowledge model that could manage unstructured information in medical genetic reports and facilitate knowledge extraction, curation and updating. For this pilot study, we used a dataset including 1,565 cancer genetics reports of Mayo Clinic patients. We used a previously developed, data-driven discovery pipeline that involves both semantic annotation and co-occurrence association analysis to establish a knowledge model. We showed that compared to genetic reports, around 56% of testing results are missing or incomplete in the clinical notes. We built a genetic report knowledge model and highlighted four key semantic groups including "Genes and Gene Products" and "Treatments". Coverage of term annotation was 99.5%. Accuracies of term annotation and relationship extraction were 98.9% and 92.9% respectively.With widespread adoption of electronic health records (EHRs), Real World Data and Real World Evidence (RWE) have been increasingly used by FDA for evaluating drug safety and effectiveness. However, integration of heterogeneous drug safety data sources and systems remains an impediment for effective pharmacovigilance studies. In an ongoing project, we have developed a next generation pharmacovigilance signal detection framework known as ADEpedia-on-OHDSI using the OMOP common data model (CDM). The objective of the study is to demonstrate the feasibility of the framework for integrating both spontaneous reporting data and EHR data for improved signal detection with a case study of immune-related adverse events. We first loaded the OMOP CDM with both recent and legacy FAERS (FDA Adverse Event Reporting System) data (from the time period between Jan. 2004 and Dec. 2018). We also integrated the clinical data from the Mayo Clinic EHR system for six oncological immunotherapy drugs. We implemented a signal detection algorithm and compared the timelines of positive signals detected from both FAERS and EHR data. We found that the signals detected from EHRs are 4 months earlier than signals detected from FAERS database (depending on the signal detection methods used) for the ipilimumab-induced hypopituitarism. Our CDM-based approach would be useful to provide a scalable solution to integrate both drug safety data and EHR data to generate RWE for improved signal detection.This study presents a novel workflow for identifying and analyzing blood pressure readings in clinical narratives using a Convolution Neural Network. The network performs three tasks identifying blood pressure readings, determining the exactness of the readings, and then classifying the readings into three classes general, treatment, and suggestion. The system can be easily set up and deployed by people who are not experts in clinical Natural Language Processing. The validation results on an independent test set show the first two of the three tasks achieve a precision, recall, and F-measure over or close to 95%, and the third task achieves an overall accuracy of 85.4%. The study demonstrates that the proposed workflow is effective for extracting blood pressure data in clinical notes. check details The workflow is general and can be easily adapted to analyze other clinical concepts for phenotyping tasks.Interoperability between heterogenous (health) IT systems relies on standards, which are communicated to system vendors in the form of so-called conformance profiles. Clinical information systems are often subjected to mandatory conformance testing and certification prior to being admitted into the health information exchange (HIE). The requirements specified in conformance profiles are therefore instrumental for ensuring the correctness and safety of the emerging HIE network. How can we ensure the quality and safety of conformance requirements themselves? We have adapted a system-theoretic hazard analysis method (STPA) for this purpose and applied it to an industrial case study in British Columbia, the Clinical Data eXchange (CDX) system. Our results indicate that the method is effective in detecting missing and erroneous constraints.Laboratory tests are a common aspect of clinical care and are the primary source of clinical genomic data. However, most laboratories use PDF documents to store and exchange the results of these tests. This locks the data into a static format and leaves the results only human-readable. The ordering clinician uses the results, but after that the information is unlikely to be used again. Future use would require a clinician to know that the test was performed, know where to find the PDF report, and take the time to open it and determine relevance to that future scenario. New computational standards such as SMART on FHIR and CDS Hooks present opportunities to better utilize these results, both physically upon receipt and asynchronously in future clinical encounters for that patient. Full app available at https//github.com/mwatkin8/FHIR-Lab-Reports-App. Demo available at http//hematite.genetics.utah.edu/FHIR-Lab-Reports/.
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