Dataset Information

Quantifying visual similarity in clinical iconic graphics.

ABSTRACT:

Objective

The use of icons and other graphical components in user interfaces has become nearly ubiquitous. The interpretation of such icons is based on the assumption that different users perceive the shapes similarly. At the most basic level, different users must agree on which shapes are similar and which are different. If this similarity can be measured, it may be usable as the basis to design better icons.

Design

The purpose of this study was to evaluate a novel method for categorizing the visual similarity of graphical primitives, called Presentation Discovery, in the domain of mammography. Six domain experts were given 50 common textual mammography findings and asked to draw how they would represent those findings graphically. Nondomain experts sorted the resulting graphics into groups based on their visual characteristics. The resulting groups were then analyzed using traditional statistics and hypothesis discovery tools. Strength of agreement was evaluated using computational simulations of sorting behavior.

Measurements

Sorter agreement was measured at both the individual graphical and concept-group levels using a novel simulation-based method. "Consensus clusters" of graphics were derived using a hierarchical clustering algorithm.

Results

The multiple sorters were able to reliably group graphics into similar groups that strongly correlated with underlying domain concepts. Visual inspection of the resulting consensus clusters indicated that graphical primitives that could be informative in the design of icons were present.

Conclusion

The method described provides a rigorous alternative to intuitive design processes frequently employed in the design of icons and other graphical interface components.

SUBMITTER: Payne PR

PROVIDER: S-EPMC1090466 | biostudies-literature | 2005 May-Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantifying visual similarity in clinical iconic graphics.

Payne Philip R O PR Starren Justin B JB

Journal of the American Medical Informatics Association : JAMIA 20050131 3

<h4>Objective</h4>The use of icons and other graphical components in user interfaces has become nearly ubiquitous. The interpretation of such icons is based on the assumption that different users perceive the shapes similarly. At the most basic level, different users must agree on which shapes are similar and which are different. If this similarity can be measured, it may be usable as the basis to design better icons.<h4>Design</h4>The purpose of this study was to evaluate a novel method for cat ...[more]

PMID: 15684136

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Project description:BackgroundVisualization of Concepts in Medicine (VCM) is a compositional iconic language that aims to ease information retrieval in Electronic Health Records (EHR), clinical guidelines or other medical documents. Using VCM language in medical applications requires alignment with medical reference terminologies. Alignment from Medical Subject Headings (MeSH) thesaurus and International Classification of Diseases - tenth revision (ICD10) to VCM are presented here. This study aim was to evaluate alignment quality between VCM and other terminologies using different measures of inter-alignment agreement before integration in EHR.MethodsFor medical literature retrieval purposes and EHR browsing, the MeSH thesaurus and the ICD10, both organized hierarchically, were aligned to VCM language. Some MeSH to VCM alignments were performed automatically but others were performed manually and validated. ICD10 to VCM alignment was entirely manually performed. Inter-alignment agreement was assessed on ICD10 codes and MeSH descriptors, sharing the same Concept Unique Identifiers in the Unified Medical Language System (UMLS). Three metrics were used to compare two VCM icons: binary comparison, crude Dice Similarity Coefficient (DSCcrude), and semantic Dice Similarity Coefficient (DSCsemantic), based on Lin similarity. An analysis of discrepancies was performed.ResultsMeSH to VCM alignment resulted in 10,783 relations: 1,830 of which were manually performed and 8,953 were automatically inherited. ICD10 to VCM alignment led to 19,852 relations. UMLS gathered 1,887 alignments between ICD10 and MeSH. Only 1,606 of them were used for this study. Inter-alignment agreement using only validated MeSH to VCM alignment was 74.2% [70.5-78.0]CI95%, DSCcrude was 0.93 [0.91-0.94]CI95%, and DSCsemantic was 0.96 [0.95-0.96]CI95%. Discrepancy analysis revealed that even if two thirds of errors came from the reviewers, UMLS was nevertheless responsible for one third.ConclusionsThis study has shown strong overall inter-alignment agreement between MeSH to VCM and ICD10 to VCM manual alignments. VCM icons have now been integrated into a guideline search engine (http://www.cismef.org) and a health terminologies portal (http://www.hetop.eu).

Project description:Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.

Dataset Information

Quantifying visual similarity in clinical iconic graphics.

Objective

Design

Measurements

Results

Conclusion

Publications

Quantifying visual similarity in clinical iconic graphics.

Similar Datasets

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