A Survey of Visual Analytics for Public Health

Preim, Bernhard; Lawonn, Kai

doi:10.1111/cgf.13891

Cited by 74 publications

(47 citation statements)

References 149 publications

(354 reference statements)

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“…Although this work is based on our experiences, we have done our best to do a targeted interdisciplinary review that can serve as a guide to clinical data projects. Our work is related to previous surveys in visual analytics in health care [ 188 ] and interactive systems [ 137 ]. Our survey is unique in that it provides a taxonomy on designing systems for amplifying expertise and focuses on the clinical data pipeline.…”

Section: Discussionmentioning

confidence: 99%

Amplifying Domain Expertise in Clinical Data Pipelines

2020

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Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

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Section: Discussionmentioning

confidence: 99%

Amplifying Domain Expertise in Clinical Data Pipelines

2020

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“…Visual analytics enhances the understanding of data by combining computational models with interactive visualizations [26][27][28]. A recent survey on visual analytics highlights the need for more research in supporting the use of social media data in public health practice [29]. Our study is meant to demonstrate how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on Twitter.…”

Section: Ojphimentioning

confidence: 99%

“…In addition, when analytic models are employed, there is an additional risk of not understanding how the data was analyzed. To address these challenges, there is a need for exploring approaches in which computer scientists and public health stakeholders work together to design visual analytic solutions [29].…”

Section: Ojphimentioning

confidence: 99%

Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study

Ola

Sedig

2020

OJPHI

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Social media allows for the exploration of online discussions of health issues outside of traditional health spaces. Twitter is one of the largest social media platforms that allows users to post short comments (i.e., tweets). The unrestricted access to opinions and a large user base makes Twitter a major source for collection and quick dissemination of some health information. Health organizations, individuals, news organizations, businesses, and a host of other entities discuss health issues on Twitter. However, the enormous number of tweets presents challenges to those who seek to improve their knowledge of health issues. For instance, it is difficult to understand the overall sentiment on a health issue or the central message of the discourse. For Twitter to be an effective tool for health promotion, stakeholders need to be able to understand, analyze, and appraise health information and discussions on this platform. The purpose of this paper is to examine how a visual analytic study can provide insight into a variety of health issues on Twitter. Visual analytics enhances the understanding of data by combining computational models with interactive visualizations. Our study demonstrates how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on Twitter. In this paper, we report on the process of data collection, analysis of data, and representation of results. We present our findings and discuss the implications of this work to support the use of Twitter for health promotion.

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“…Visual Cohort Analysis Preim et al [PL20] provided an extensive overview of visual analytics approaches for public health data in their survey. Further related work in the field of visual analytics of patient cohorts includes the work of Angelelli et al [AOH * 14].…”

Section: Related Workmentioning

confidence: 99%

RadEx: Integrated Visual Exploration of Multiparametric Studies for Radiomic Tumor Profiling

Mörth

Wagner-Larsen

Hodneland

et al. 2020

Computer Graphics Forum

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Better understanding of the complex processes driving tumor growth and metastases is critical for developing targeted treatment strategies in cancer. Radiomics extracts large amounts of features from medical images which enables radiomic tumor profiling in combination with clinical markers. However, analyzing complex imaging data in combination with clinical data is not trivial and supporting tools aiding in these exploratory analyses are presently missing. In this paper, we present an approach that aims to enable the analysis of multiparametric medical imaging data in combination with numerical, ordinal, and categorical clinical parameters to validate established and unravel novel biomarkers. We propose a hybrid approach where dimensionality reduction to a single axis is combined with multiple linked views allowing clinical experts to formulate hypotheses based on all available imaging data and clinical parameters. This may help to reveal novel tumor characteristics in relation to molecular targets for treatment, thus providing better tools for enabling more personalized targeted treatment strategies. To confirm the utility of our approach, we closely collaborate with experts from the field of gynecological cancer imaging and conducted an evaluation with six experts in this field.

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A Survey of Visual Analytics for Public Health

Cited by 74 publications

References 149 publications

Amplifying Domain Expertise in Clinical Data Pipelines

Amplifying Domain Expertise in Clinical Data Pipelines

Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study

RadEx: Integrated Visual Exploration of Multiparametric Studies for Radiomic Tumor Profiling

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