Notes

Genome-wide molecular phylogenetic looks at as well as multiplying experiments which usually disclose the transformative background an intermediate phase associated with speciation of a giant water bug.
Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. We developed an FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data that are used in the PheWAS study. As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients with genetic reports from Foundation Medicine as well as the corresponding lab tests and diagnosis from Mayo EHRs. A PheWAS study is conducted and 81 significant genotype-phenotype associations are identified, in which 36 significant associations for cancers are validated based on a literature review.Non-lattice subgraphs are often indicative of structural anomalies in ontological systems. Visualization of SNOMED CT's non-lattice subgraphs can help make sense of what has been asserted in the hierarchical ("is-a") relation. More importantly, it can demonstrate what has not been asserted, or "is-not-a," using Closed-World Assumption for such subgraphs. A feature-rich web-based interactive graph-visualization engine called WINS is introduced, for supporting non-lattice based analysis of ontological systems such as SNOMED CT. A faceted search interface is designed for querying conjunctively specified non-lattice subgraphs. To manage the large number of possible nonlattice subgraphs, MongoDB is used for storing and processing sets of concepts, relationships, and subgraphs, as well as for query optimization. WINS' interactive visualization interface is implemented in the open source package D3.js. 14 versions of SNOMED CT (US editions from March 2012 to September 2018), with about 170,000 subgraphs in each version, were extracted and imported into WINS. 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. 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/.An important task in biomedical literature precise search is to identify paper describing a certain disease. The tradi- tional topic identification approaches based on neural network can be used to recognize the disease topic of literature. CA-074 Me molecular weight To achieve better performance, we propose a novel word graph-based method for disease topic identification in this paper. Word graphs are constructed from literature title and abstract. Graph features are extracted and used for disease topic classification using a logistic regression or random forest classifier. Experiment results showed the word graph features outperformed disease mention frequency by a large margin. Our approach achieved better perfor- mance in identifying disease topic compared to hierarchical attention networks, which is a deep learning approach for document classification. We also demonstrated the use of the proposed method in identifying the disease topic in an application scenario.Simvastatin is a commonly used medication for lipid management and cardiovascular disease, however, the risk of adverse events (AEs) with its use increases via drug-drug interaction (DDI) exposures. Patients were extracted if initially diagnosed with cardiovascular disease and newly initiated simvastatin therapy. The cohort was divided into a DDI-exposed group and a non-DDI exposed group. The DDI-exposed group was further divided into gemfibrozil, clarithromycin, and erythromycin exposure groups. The outcome was defined as a composite of predefined AEs. Our results show that the simvastatin-DDI group had a higher illness burden with longer simvastatin exposure time and more medical care follow-up compared with the simvastatin-non-DDI exposed group. AEs occurred more frequently in subjects exposed to interacting drugs with a higher risk for clarithromycin and erythromycin exposed subjects than for gemfibrozil subjects.Atrial fibrillation (AF) is the most common cardiac arrhythmia as well as a significant risk factor in heart failure and coronary artery disease. AF can be detected by using a short ECG recording. However, discriminating atrial fibrillation from normal sinus rhythm, other arrhythmia and strong noise, given a short ECG recording, is challenging. Towards this end, we propose MultiFusionNet, a deep learning network that uses a multiplicative fusion method to combine two deep neural networks trained on different sources of knowledge, i.e., extracted features and raw data. Thus, MultiFusionNet can exploit the relevant extracted features to improve upon the utilization of the deep learning model on the raw data. Our experiments show that this approach offers the most accurate AF classification and outperforms recently published algorithms that either use extracted features or raw data separately. Finally, we show that our multiplicative fusion method for combining the two sub-networks outperforms several other combining methods.
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