Destination Area: Data and Decisions (D&D)
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The DA Data and Decisions advances the human condition and society with better decisions through data. D&D integrates all DAs and SGAs with data analytics and decision sciences. Work in this area embraces equity in the human condition by seeking the equitable distribution and availability of physical safety and well-being, psychological well-being, respect for human dignity, and access to crucial material and social resources throughout the world’s diverse communities. D&D also addresses policymaking and policy analysis, collaborating at the intersection of scientific evidence, governance, and analyses to translate scholarship into practice.
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Browsing Destination Area: Data and Decisions (D&D) by Department "Fralin Life Sciences Institute"
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- EpiViewer: an epidemiological application for exploring time series dataThorve, Swapna; Wilson, Mandy L.; Lewis, Bryan L.; Swarup, Samarth; Vullikanti, Anil Kumar S.; Marathe, Madhav V. (2018-11-22)Background Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher’s recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources – data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. Results In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves. Conclusions EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.
- Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource CenterWattam, Alice R.; Davis, James J.; Assaf, Rida; Boisvert, Sebastien; Brettin, Thomas; Bun, Christopher; Conrad, Neal; Dietrich, Emily M.; Disz, Terry L.; Gabbard, Joseph L.; Gerdes, Svetlana; Henry, Christopher S.; Kenyon, Ronald W.; Machi, Dustin; Mao, Chunhong; Nordberg, Eric K.; Olsen, Gary J.; Murphy-Olson, Daniel E.; Olson, Robert D.; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Shukla, Maulik; Vonstein, Veronika; Warren, Andrew S.; Xia, Fangfang; Yoo, Hyunseung; Stevens, Rick L. (2017-01-04)The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user- created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by `virtual integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.
- SIPsmartER delivered through rural, local health districts: adoption and implementation outcomesPorter, Kathleen J.; Brock, Donna J.; Estabrooks, Paul A.; Perzynski, Katelynn M.; Hecht, Erin R.; Ray, Pamela; Kružliaková, Natalie; Cantrell, Eleanor S.; Zoellner, Jamie M. (2019-09-18)Background SIPsmartER is a 6-month evidenced-based, multi-component behavioral intervention that targets sugar-sweetened beverages among adults. It consists of three in-person group classes, one teach-back call, and 11 automated phone calls. Given SIPsmartER’s previously demonstrated effectiveness, understanding its adoption, implementation, and potential for integration within a system that reaches health disparate communities is important to enhance its public health impact. During this pilot dissemination and implementation trial, SIPsmartER was delivered by trained staff from local health districts (delivery agents) in rural, Appalachian Virginia. SIPsmartER’s execution was supported by consultee-centered implementation strategies. Methods In this mixed-methods process evaluation, adoption and implementation indicators of the program and its implementation strategy (e.g., fidelity, feasibility, appropriateness, acceptability) were measured using tracking logs, delivery agent surveys and interviews, and fidelity checklists. Quantitative data were analyzed with descriptive statistics. Qualitative data were inductively coded. Results Delivery agents implemented SIPsmartER to the expected number of cohorts (n = 12), recruited 89% of cohorts, and taught 86% of expected small group classes with > 90% fidelity. The planned implementation strategies were also executed with high fidelity. Delivery agents completing the two-day training, pre-lesson meetings, fidelity checklists, and post-lesson meetings at rates of 86, 75, 100, and 100%, respectively. Additionally, delivery agents completed 5% (n = 3 of 66) and 10% (n = 6 of 59) of teach-back and missed class calls, respectively. On survey items using 6-point scales, delivery agents reported, on average, higher feasibility, appropriateness, and acceptability related to delivering the group classes (range 4.3 to 5.6) than executing missed class and teach-back calls (range 2.6 to 4.6). They also, on average, found the implementation strategy activities to be helpful (range 4.9 to 6.0). Delivery agents identified strengths and weakness related to recruitment, lesson delivery, call completion, and the implementation strategy. Conclusions In-person classes and the consultee-centered implementation strategies were viewed as acceptable, appropriate, and feasible and were executed with high fidelity. However, implementation outcomes for teach-back and missed class calls and recruitment were not as strong. Findings will inform the future full-scale dissemination and implementation of SIPsmartER, as well as other evidence-based interventions, into rural health districts as a means to improve population health.
- Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical frameworkAbedi, Vida; Khan, Ayesha; Chaudhary, Durgesh; Misra, Debdipto; Avula, Venkatesh; Mathrawala, Dhruv; Kraus, Chadd; Marshall, Kyle A.; Chaudhary, Nayan; Li, Xiao; Schirmer, Clemens M.; Scalzo, Fabien; Li, Jiang; Zand, Ramin (2020-08)Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.