2206A Student Center
Using Social Network Analysis and Natural Language Processing to Describe Communication Practices of Interdisciplinary Teams in Primary Care
The Electronic Medical Record (EMR) serves different purposes including documentation of care and billing. One part of the EMR at the University of Missouri Hospital and Clinics is the Message Center. Many people, including healthcare providers, nurses, social workers, therapists, office staff, and nurse care managers (known as the interdisciplinary team, or IDT) work together to deliver healthcare.
This research examines how the Message Center is used in primary care by nurse care managers to document care coordination activities, including communication between patients, patient identified family or significant other, and the IDT. Care coordination activities, and the focus of those activities will be extracted and described. Social network analysis will be used to expose and map the communication between different users of message center. This research aims to describe communication practices across healthcare settings.
Dr. Henry Wan, Ph.DDate:
Seasonal Influenza Vaccine: Not easy shot to get
During the past nearly 50 years, antigenic variants of subtype H3N2 influenza A viruses have frequently emerged, causing significant public health challenges. The manner in which these variants emerge and their patterns of spread are not well understood. We identified 15 antigenic drift events with 16 antigenic variants during 1968–2016 by using a novel genomic sequence–based antigenicity inference method on ~40,000 H3N2 viruses. New antigenic variants were shown to emerge from certain locations in other continents rather than from Asia alone, and variants emerged year-round and took <2 months to spread across multiple continents. The uncertainty of the location of antigenic variant emergence and the rapidity of viral spread pose great challenges for influenza surveillance. Our findings suggest that a more robust H3N2 virus surveillance strategy, including sampling from unrepresented areas (e.g., tropical locations) and outside influenza seasons, would help identify the emergence of antigenic variants.
2206A Student Center
REDESIGN: RDF-based Differential Signaling Framework for Precision Medicine Analytics
Pathway-based analysis holds promise to be instrumental in precision and personalized medicine analytics. However, the majority of pathway-based analysis methods utilize “fixed” or “rigid” data sets that limit their ability to account for complex biological inter-dependencies. Here, we present REDESIGN: RDF-based Differential Signaling Pathway informatics framework. The distinctive feature of the REDESIGN is that it is designed to run on “flexible” ontology-enabled data sets of curated signal transduction pathway maps to uncover high explanatory differential pathway mechanisms on gene-to-gene level. The experiments on two morphoproteomic cases demonstrated REDESIGN’s capability to generate actionable hypotheses in precision/personalized medicine analytics.
2206A Student Center
MU-LOC: A Deep Neural Network Method for Predicting Mitochondrially Localized Proteins in Plants
Targeting and translocation of proteins to the appropriate subcellular compartments is crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained deep neural network and support vector machine predictors. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.
2206A Student Center
An Analysis of Diabetes Mobile Applications Features Compared to AADE7TM: Addressing Self-Management Behaviors in People with Diabetes
Diabetes Self-management (DSM) applications (apps) have been designed to improve knowledge of diabetes and self-management behaviors. However, few studies have systematically examined if diabetes apps followed the American Association of Diabetes Educators (AADE) Self-Care BehaviorsTM guidelines. The purpose of this study was to compare the features of current DSM apps to the AADE7TM guidelines. In two major app stores, we used three search terms to capture a wide range of diabetes apps. Apps were excluded based on five exclusion criteria. A multidisciplinary team analyzed and classified the features of each app based on the AADE7TM. We conducted interviews with six diabetes physicians and educators for their opinions on the distribution of the features of DSM apps. Out of 1,050 apps retrieved, 173 apps were identified as eligible during November 2015 and 137 apps during December 2017. We found an unbalanced DSM app development trend based on AADE7TM guidelines. Future diabetes apps should attempt to incorporate features under evidence-based guidelines such as AADE7TM to better support the self-management behavior changes of people with diabetes.
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