A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support

Roham, Mehrdad; Gabrielyan, Anait R.; Archer, Norm

doi:10.5772/intechopen.90266

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Actually, knowledge visualization is critical to data analysis and allows for capture the intrinsic structure and pattern of data, thus giving the opportunity to quick access to learning (Khamis et al , 2018). Moreover, knowledge visualization is essential when there is a need to augment human capabilities and present knowledge to the user in a way that gives the factual information that is useful for taking decisions (Roham et al , 2019; Zanakis and Becerra-Fernandez, 2005).…”

Section: Theory Backgroundmentioning

confidence: 99%

A knowledge visualization approach to identify and discovery inner areas: a pilot application in the province of Lecce

Moretto

Elia

Schirinzi³

et al. 2021

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PurposeThe paper aims to propose a knowledge visualization approach and algorithm to support public decision makers to define the inner areas, which represents a strategic topic in the European debate about territorial inequality and development.Design/methodology/approachThe study has been developed by following the design science research, which includes six steps: problem identification and motivation; identification of the objectives for a solution; design and development; demonstration; evaluation; and communication. As for the design and development step, the proposed approach and algorithm ground on association mining to discover hidden relationships existing among municipalities. They have been applied to analyse the 97 municipalities of the Lecce province, and each municipality has been described through 30 multi-domain indicators organized into seven categories, whose data have been collected from institutional datasets, local sources or web-scraping process.FindingsA set of complementary analyses has been generated through the construction of dynamic and interactive knowledge maps that show “similar” municipalities according to the indicators selected.Originality/valueThe approach and algorithm proposed allow discovering similarities existing among distinct municipalities, based on the analysis of a set of multi-domain indicators. The approach may complement or completely substitute the existing ones used to define inner areas, thus overcoming both the methodological limits of the “top-down” line imposed by the central legislator, and the “bottom-up” paradox consisting in the illusion that single (and often small) towns have the economic and cognitive resources necessary to implement effective territorial mapping and development strategies. In such a way, policy makers can be aware on similarities existing among distinct towns and can thus share cognitive and financial resources to define a common plan and a set of practices for territorial development.

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Section: Theory Backgroundmentioning

confidence: 99%

A knowledge visualization approach to identify and discovery inner areas: a pilot application in the province of Lecce

Moretto

Elia

Schirinzi³

et al. 2021

View full text Add to dashboard Cite

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“…Clinical data visualization that enables straightforward interpretation and evaluation of information is a hot topic in medicine, owing to the growing volume of clinical data (41,42).…”

Section: Sankey Visualizations Of Risk Variationmentioning

confidence: 99%

Understanding Social Risk Variation Across Reintegration of Post-Ischemic Stroke Patients

et al. 2021

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The quality of life of post-ischemic stroke patients during reintegration is affected by a range of factors, including the risk of insufficient social and family support, as well as socio-economic status. A patient who is unable to access needed social support, such as home health care or a day center, is at a greater risk of poorer quality of life during reintegration.Consequently, the key goals of post-stroke reintegration are to improve patient outcomes across these factors, to inform reintegration decisions, as well as design personalized interventions for patients with social risk. This chapter presents a case-study of 240 patients of the Catalonia region of Spain that uses data visualization techniques (known as Sankey diagrams) to provide insight into changes in quality of life risk factors such as gender, and stroke severity, during reintegration. As supported by the case-study, social risk is a complex and multifactorial phenomenon that can vary significantly for an individual over the course of stroke rehabilitation and reintegration.

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“…Visualization of data makes people understand meaning of data much quicker than a textual description. Visualization techniques in [35] are classified into 7 categories: 3D/volumetric charts (3D brain maps, interactive geo-spatial maps), icons, maps, multidimensional charts (area charts, bar graphs, bipartite graphs, box plots, bubble charts, causal network visualizations and heatmaps, key performance indicators, line graphs, pie charts and scatter plots), tables, temporal/timeline graphs (simple time series graphs with or without color coding) and textual descriptions. It has been found in [36] that positional and colour visual encodings are recommended for detection tasks and time series visualisations.…”

Section: Visualization Of Time Series and Sequential Patternsmentioning

confidence: 99%

Visualization of Generic Utility of Sequential Patterns

et al. 2020

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Most of the literature on utility pattern mining (UPM) assumes that the particular patterns' utility in known in advance. Concurrently, in frequent pattern mining (FPM) it is assumed that all patterns take the same value. In reality, the information about the utility of patterns is not or hardly available in most cases. Moreover, the utility and frequency of the particular pattern are not directly proportional. An algorithm for estimating a generic pattern utility has been recently proposed, but the numeric results might be difficult to interpret. In particular, in datasets with many independent instances or groups. In this paper, we present an approach to generating utility bitmaps that provide visual representation of the numeric data obtained using generic pattern utility algorithm. We demonstrate validity of this approach on two datasets: PAMAP2 Physical Activity Monitoring Data Set, an open dataset from the UCI Machine Learning Repository, and an ECG dataset collected using Biopac Student Lab during Ruffier's test. For PAMAP2 dataset, utility bitmaps allow for immediate separation of various physical activities. Variation between participants are present, but do not overshadow differences between the activity types. For the ECG dataset, utility bitmaps immediately indicate age and fitness differences between the participants, even thought this information was not available to the algorithm. In both cases, partial similarity in bitmaps can be traced back to partial similarity in activities or participants generating the data. Based on these tests, the approach seems to be promising for exploratory analysis of large collections of long time series and possibly other sequential patterns such as distance series common in sports data analysis and depth series common in petroleum engineering.

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A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support

Cited by 3 publications

References 27 publications

A knowledge visualization approach to identify and discovery inner areas: a pilot application in the province of Lecce

A knowledge visualization approach to identify and discovery inner areas: a pilot application in the province of Lecce

Understanding Social Risk Variation Across Reintegration of Post-Ischemic Stroke Patients

Visualization of Generic Utility of Sequential Patterns

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