Spatial-symbolic Query Engine in Anatomy

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

BACKGROUND

In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look?

QUESTIONS

Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.

RESULTS

We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information-a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.

CONCLUSIONS

We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.

Discussion of "Representation of People's Decisions in Health Information Systems: A Complementary Approach for Understanding Health Care Systems and Population Health"

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Representation of People's Decisions in Health Information Systems: A Complementary Approach for Understanding Health Care Systems and Population Health" written by Fernan Gonzalez Bernaldo de Quiros, Adriana Ruth Dawidowski, and Silvana Figar. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of de Quiros, Dawidowski, and Figar. In subsequent issues the discussion can continue through letters to the editor.

Feasibility of Population Health Analytics and Data Visualization for Decision Support in the Infectious Diseases Domain: A pilot study

OBJECTIVE

Big data or population-based information has the potential to reduce uncertainty in medicine by informing clinicians about individual patient care. The objectives of this study were: 1) to explore the feasibility of extracting and displaying population-based information from an actual clinical population's database records, 2) to explore specific design features for improving population display, 3) to explore perceptions of population information displays, and 4) to explore the impact of population information display on cognitive outcomes.

METHODS

We used the Veteran's Affairs (VA) database to identify similar complex patients based on a similar complex patient case. Study outcomes measures were 1) preferences for population information display 2) time looking at the population display, 3) time to read the chart, and 4) appropriateness of plans with pre- and post-presentation of population data. Finally, we redesigned the population information display based on our findings from this study.

RESULTS

The qualitative data analysis for preferences of population information display resulted in four themes: 1) trusting the big/population data can be an issue, 2) embedded analytics is necessary to explore patient similarities, 3) need for tools to control the view (overview, zoom and filter), and 4) different presentations of the population display can be beneficial to improve the display. We found that appropriateness of plans was at 60% for both groups (t9=-1.9; p=0.08), and overall time looking at the population information display was 2.3 minutes versus 3.6 minutes with experts processing information faster than non-experts (t8= -2.3, p=0.04).

CONCLUSION

A population database has great potential for reducing complexity and uncertainty in medicine to improve clinical care. The preferences identified for the population information display will guide future health information technology system designers for better and more intuitive display.

From bed to bench: bridging from informatics practice to theory: an exploratory analysis

BACKGROUND

In 2009, Applied Clinical Informatics (ACI)--focused on applications in clinical informatics--was launched as a companion journal to Methods of Information in Medicine (MIM). Both journals are official journals of the International Medical Informatics Association.

OBJECTIVES

To explore which congruencies and interdependencies exist in publications from theory to practice and from practice to theory and to determine existing gaps. Major topics discussed in ACI and MIM were analyzed. We explored if the intention of publishing companion journals to provide an information bridge from informatics theory to informatics practice and vice versa could be supported by this model. In this manuscript we will report on congruencies and interdependences from practice to theory and on major topics in MIM.

METHODS

Retrospective, prolective observational study on recent publications of ACI and MIM. All publications of the years 2012 and 2013 were indexed and analyzed.

RESULTS

Hundred and ninety-six publications were analyzed (ACI 87, MIM 109). In MIM publications, modelling aspects as well as methodological and evaluation approaches for the analysis of data, information, and knowledge in biomedicine and health care were frequently raised - and often discussed from an interdisciplinary point of view. Important themes were ambient-assisted living, anatomic spatial relations, biomedical informatics as scientific discipline, boosting, coding, computerized physician order entry, data analysis, grid and cloud computing, health care systems and services, health-enabling technologies, health information search, health information systems, imaging, knowledge-based decision support, patient records, signal analysis, and web science. Congruencies between journals could be found in themes, but with a different focus on content. Interdependencies from practice to theory, found in these publications, were only limited.

CONCLUSIONS

Bridging from informatics theory to practice and vice versa remains a major component of successful research and practice as well as a major challenge.

Spatial-symbolic Query Engine in Anatomy

OBJECTIVES

Currently, the primary means for answering anatomical questions such as 'what vital organs would potentially be impacted by a bullet wound to the abdomen?' is to look them up in textbooks or to browse online sources. In this work we describe a semantic web service and spatial query processor that permits a user to graphically pose such questions as joined queries over separately defined spatial and symbolic knowledge sources.

METHODS

Spatial relations (e.g. anterior) were defined by two anatomy experts, and based on a 3-D volume of labeled images of the thorax, all the labeled anatomical structures were queried to retrieve the target structures for every query structure and every spatial relation. A web user interface and a web service were designed to relate existing symbolic information from the Foundational Model of Anatomy ontology (FMA) with spatial information provided by the spatial query processor, and to permit users to select anatomical structures and define queries.

RESULTS

We evaluated the accuracy of results returned by the queries, and since there is no independent gold standard, we used two anatomy experts' opinions as the gold standard for comparison. We asked the same experts to define the gold standard and to define the spatial relations. The F-measure for the overall evaluation is 0.90 for rater 1 and 0.56 for rater 2. The percentage of observed agreement is 99% and Cohen's kappa coefficient reaches 0.51. The main source of disagreement relates to issues with the labels used in the dataset, and not with the tool itself.

CONCLUSIONS

In its current state the system can be used as an end-user application but it is likely to be of most use as a framework for building end-user applications such as displaying the results as a 3-D anatomical scene. The system promises potential practical utility for obtaining and navigating spatial and symbolic data.