Dataset Information

An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services.

ABSTRACT: Background: There is interest in the use geospatial data for development of acute stroke services given the importance of timely access to acute reperfusion therapy. This paper aims to introduce clinicians and citizen scientists to the possibilities offered by open source softwares (R and Python) for analyzing geospatial data. It is hoped that this introduction will stimulate interest in the field as well as generate ideas for improving stroke services. Method: Instructions on installation of libraries for R and Python, source codes and links to census data are provided in a notebook format to enhance experience with running the software. The code illustrates different aspects of using geospatial analysis: (1) creation of choropleth (thematic) map which depicts estimate of stroke cases per post codes; (2) use of map to help define service regions for rehabilitation after stroke. Results: Choropleth map showing estimate of stroke per post codes and service boundary map for rehabilitation after stroke. Conclusions The examples in this article illustrate the use of a range of components that underpin geospatial analysis. By providing an accessible introduction to these areas, clinicians and researchers can create code to answer clinically relevant questions on topics such as service delivery and service demand.

SUBMITTER: Padgham M

PROVIDER: S-EPMC6693386 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services.

Padgham Mark M Boeing Geoff G Cooley David D Tierney Nicholas N Sumner Michael M Phan Thanh G TG Beare Richard R

Frontiers in neurology 20190807

<b>Background:</b> There is interest in the use geospatial data for development of acute stroke services given the importance of timely access to acute reperfusion therapy. This paper aims to introduce clinicians and citizen scientists to the possibilities offered by open source softwares (R and Python) for analyzing geospatial data. It is hoped that this introduction will stimulate interest in the field as well as generate ideas for improving stroke services. <b>Method:</b> Instructions on inst ...[more]

PMID: 31440197

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Project description:BackgroundThe study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heterogeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data.ResultsWe have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data.ConclusionsThe proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Project description:Introduction: Geographic visualizations have been used to understand disease since the nineteenth century. We developed a software that creates simple visualizations which can be used as a decision support tool for pre-hospital acute stroke transportation planning. In this study, we test the usability of this software to improve user experience and assess the interpretability of the visualizations it produces as it relates to planning and optimizing stroke systems of care. Materials and Methods: Healthcare practitioners and administrators working within the acute stroke system in Alberta, Canada were invited to participate. Participants were randomized to either the geographic visualization or 2-D temporospatial diagrams. Using a standardized script participants were asked to complete tasks and interpret the visualizations produced by the software. The computer screen and audio were recorded. Recordings were transcribed verbatim and analyzed using inductive thematic analyses. The number of errors made and time to task completion were also analyzed. Results: Eighteen participants (8 physicians, 5 healthcare administrators, 3 paramedics, and 2 nurses) were enrolled. Mean age was 41.22 years (SD: 10.55) and 8 participants were female. It took users a mean of 1.59 min (SD: 0.71) to complete all 10 tasks for the geographic visualizations and a mean of 1.08 min (SD: 0.33) to complete all 15 tasks for the 2-D temporospatial diagrams. Map users made a median of 2 errors (IQR: 4), 2-D temporospatial diagram users also made a median of 2 errors (IQR: 1.5). All but one map user correctly interpreted all maps, only three of the eight 2-D temporospatial diagram correctly interpreted all diagrams. In the qualitative analysis three common themes were identified: comments on the user interface, comments on the visualization tool(s), and suggestions for improvement. Most study participants mentioned that the software would be useful in their work. Conclusions: Healthcare professional from several different aspects of stroke care see geographic visualizations in transport decision making to be a useful tool. The software demonstrated high usability. However, several suggestions were made to improve user experience as well as additional features which could be developed and become the subject of future studies.

Dataset Information

An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services.

Publications

An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services.

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