Human concept cognition and semantic relations in the unified medical language system: A coherence analysis.

There is almost a universal agreement among scholars in information retrieval (IR) research that knowledge representation needs improvement. As core component of an IR system, improvement of the knowledge representation system has so far involved manipulation of this component based on principles such as vector space, probabilistic approach, inference network, and language modeling, yet the required improvement is still far from fruition. One promising approach that is highly touted to offer a potential solution exists in the cognitive paradigm, where knowledge representation practice should involve, or start from, modeling the human conceptual system. This study based on two related cognitive theories: the theory-based approach to concept representation and the psychological theory of semantic relations, ventured to explore the connection between the human conceptual model and the knowledge representation model (represented by samples of concepts and relations from the unified medical language system, UMLS). Guided by these cognitive theories and based on related and appropriate data-analytic tools, such as nonmetric multidimensional scaling, hierarchical clustering, and content analysis, this study aimed to conduct an exploratory investigation to answer four related questions. Divided into two groups, a total of 89 research participants took part in two sets of cognitive tasks. The first group (49 participants) sorted 60 food names into categories followed by simultaneous description of the derived categories to explain the rationale for category judgment. The second group (40 participants) performed sorting 47 semantic relations (the nonhierarchical associative types) into 5 categories known a priori. Three datasets resulted as a result of the cognitive tasks: food-sorting data, relation-sorting data, and free and unstructured text of category descriptions. Using the data analytic tools mentioned, data analysis was carried out and important results and findings were obtained that offer plausible explanations to the 4 research questions. Major results include the following: (a) through discriminant analysis category members were predicted consistently in 70% of the time; (b) the categorization bases are largely simplified rules, naïve explanations, and feature-based; (c) individuals theoretical explanation remains valid and stays stable across category members; (d) the human conceptual model can be fairly reconstructed in a low-dimensional space where 93% of the variance in the dimensional space is accounted for by the subjects performance; (e) participants consistently classify 29 of the 47 semantic relations; and, (f) individuals perform better in the functional and spatial dimensions of the semantic relations classification task and perform poorly in the conceptual dimension

Exploring the use of concept spaces to improve medical information retrieval

Artificial Intelligence Lab, Department of MIS, University of ArizonaThis research investigated the application of techniques successfully used in previous information retrieval research, to the more challenging area of medical informatics. It was performed on a biomedical document collection testbed, CANCERLIT, provided by the National Cancer Institute (NCI) , which contains information on all types of cancer therapy. The quality or usefulness of terms suggested by three different thesauri, one based on MeSH terms, one based solely on terms from the document collection, and one based on the Unified Medical Language System UMLS Metathesaurus, was explored with the ultimate goal of improving CANCERLIT information search and retrieval. Researchers affiliated with the University of Arizona Cancer Center evaluated lists of related terms suggested by different thesauri for 12 different directed searches in the CANCERLIT testbed. The preliminary results indicated that among the thesauri, there were no statistically significant differences in either term recall or precision. Surprisingly, there was almost no overlap of relevant terms suggested by the different thesauri for a given search. This suggests that recall could be significantly improved by using a combined thesaurus approach

The impact of concept map visualizations on the information behavior, perceptions of performance, learning and use with novices in the information retrieval context

In examining undergraduate students in the information retrieval environment for the impact of computer generated concept maps, two primary research questions were considered: 1) what is the impact of display type on the novice searcher’s information behavior; and 2) what is the impact of different display types on the user’s perceptions of performance, knowledge and overall use of the system.Sixty participants in this experiment were given hypothetical information needs on two different medical topics (cholesterol, depression). Participants’ explored one of three interactive visualization displays using these medical topics, answered a pre- and post-test instrument and then completed a final questionnaire on their perceptions of the displays. Different types of inferential statistical tests were used to examine the research questions. When appropriate, factorial ANOVAs, mixed between-within ANOVAs, and chi square tests of independence were conducted.Five main findings resulted from this research: 1) for all display types (LIST, SOM, PFNET) there is an increase in the number of participant search terms and in the incorporation of MeSH terminology from the visualizations following exposure to those displays; 2) there is a relationship between the display type and the interface level from which PFNET participants chose terms; 3) searchers’ feelings of confidence, satisfaction, success, and relevance increased across all groups after system interaction; however, pretest feelings of confidence and satisfaction seem to be dependent upon the participant’s self-reported prior knowledge of the search topic; 4) feelings of confidence and satisfaction on the topic participants reported less pre-test knowledge on (cholesterol) shifted to match post-test ratings of confidence and satisfaction on the topic they had more pre-test knowledge on (depression); and 5) participants rated the PFNET system more visually appealing, easier to understand and more likely to be used in the future if given the option. Overall findings suggest that all displays were useful to the participants in this experiment and that the PFNET display was particularly useful for the novice searcher.Ph.D., Information Science -- Drexel University, 200

Quality framework for semantic interoperability in health informatics: definition and implementation

Aligned with the increased adoption of Electronic Health Record (EHR) systems, it is recognized that semantic interoperability provides benefits for promoting patient safety and continuity of care. This thesis proposes a framework of quality metrics and recommendations for developing semantic interoperability resources specially focused on clinical information models, which are defined as formal specifications of structure and semantics for representing EHR information for a specific domain or use case. This research started with an exploratory stage that performed a systematic literature review with an international survey about the clinical information modelling best practice and barriers. The results obtained were used to define a set of quality models that were validated through Delphi study methodologies and end user survey, and also compared with related quality standards in those areas that standardization bodies had a related work programme. According to the obtained research results, the defined framework is based in the following models: Development process quality model: evaluates the alignment with the best practice in clinical information modelling and defines metrics for evaluating the tools applied as part of this process. Product quality model: evaluates the semantic interoperability capabilities of clinical information models based on the defined meta-data, data elements and terminology bindings. Quality in use model: evaluates the suitability of adopting semantic interoperability resources by end users in their local projects and organisations. Finally, the quality in use model was implemented within the European Interoperability Asset register developed by the EXPAND project with the aim of applying this quality model in a broader scope to contain any relevant material for guiding the definition, development and implementation of interoperable eHealth systems in our continent. Several European projects already expressed interest in using the register, which will now be sustained by the European Institute for Innovation through Health Data