Visibility of medical informatics regarding bibliometric indices and databases.

BACKGROUND: The quantitative study of the publication output (bibliometrics) deeply influences how scientific work is perceived (bibliometric visibility). Recently, new bibliometric indices and databases have been established, which may change the visibility of disciplines, institutions and individuals. This study examines the effects of the new indices on the visibility of Medical Informatics. METHODS: By objective criteria, three sets of journals are chosen, two representing Medical Informatics and a third addressing Internal Medicine as a benchmark. The availability of index data (index coverage) and the aggregate scores of these corpora are compared for journal-related (Journal impact factor, Eigenfactor metrics, SCImago journal rank) and author-related indices (Hirsch-index, Egghes G-index). Correlation analysis compares the dependence of author-related indices. RESULTS: The bibliometric visibility depended on the research focus and the citation database: Scopus covers more journals relevant for Medical Informatics than ISI/Thomson Reuters. Journals focused on Medical Informatics' methodology were negatively affected by the Eigenfactor metrics, while the visibility profited from an interdisciplinary research focus. The correlation between Hirsch-indices computed on citation databases and the Internet was strong. CONCLUSIONS: The visibility of smaller technology-oriented disciplines like Medical Informatics is changed by the new bibliometric indices and databases possibly leading to suitably changed publication strategies. Freely accessible author-related indices enable an easy and adequate individual assessment.

How to configure blended problem based learning-results of a randomized trial.

BACKGROUND: Problem based learning (PBL) may be successfully complemented by Blended Learning approaches. However, the precise effect of combining different eLearning components in PBL-courses is yet unclear. AIM: This study aims at comparing the effects of the different combinations of three interactive components (Wiki, Chat, and, Interactive Diagnostic Context - a diagnostic related link collection to expert sources) on learning, aspects of group process, and individual learner satisfaction. METHODS: A mixed-method study addressed all possible combinations of the three components. Tests and controls were assigned by group randomization. Hypotheses were tested by the Friedman- and the Mann-Whitney U test. RESULTS: The results prove Wiki to positively influence the ratings given by students to the course, whereas no evidence supports the expected advantages of Chat. The Interactive Diagnostic Context led to only few significant effects concerning the diagnostic approach and general score. CONCLUSION: Blended PBL (bPBL) profits best from supporting asynchronous communication (i.e. Wiki). Synchronous communication components and hypothesis-driven information retrieval do not yield further improvements. Thus, bPBL should concentrate on technically fostering the group process and avoid overloading the course configuration by other eLearning components.

The publication echo: effects of retrieving literature in PubMed by year of publication.

OBJECTIVES: In PubMed search forms, the publication date refers to both the date of electronic and printed publication. This fact is documented in PubMed, but difficult to anticipate by the users and can provoke misinterpretations of search results. The Technical Note aims at systematically investing the effect (referred to as the publication echo), clarifying onset and extent of the publication echo, and comments on its impact. METHODS: Papers with ambiguous publication dates are systematically retrieved and a trend analysis with seasonal decomposition on monthly publication data is performed. RESULTS: First doubled search results were found for 1999, their number since then rapidly increasing. Up to 17.6% of all articles of a year are found to be published electronically and in print, which can be before or afterwards. Maximum delay between the two dates is three years, except for one singular publication, where it is five years. Publication trends are exponential and linear when considering echoed and echo-cleaned data, respectively. CONCLUSIONS: As a conclusion, we suggest using a query formulation that unambiguously retrieves literature from PubMed by the date of publication.

Blended learning positively affects students' satisfaction and the role of the tutor in the problem-based learning process: results of a mixed-method evaluation.

Problem-based learning (PBL) is an established didactic approach in medical education worldwide. The impact of PBL depends on the tutors' quality and the students' motivation. To enhance students' motivation and satisfaction and to overcome the problems with the changing quality of tutors, online learning and face-to-face classes were systematically combined resulting in a blended learning scenario (blended problem-based learning--bPBL). The study aims at determining whether bPBL increases the students' motivation and supports the learning process with respect to the students' cooperation, their orientation, and more reliable tutoring. The blended PBL was developed in a cooperation of students and teachers. The well-established Seven Jump-scheme of PBL was carefully complemented by eLearning modules. On the first training day of bPBL the students start to work together with the online program, but without a tutor, on the final training day the tutor participates in the meeting for additional help and feedback. The program was evaluated by a mixed-method study. The traditional PBL course was compared with the blended PBL by means of qualitative and quantitative questionnaires, standardized group interviews, and students' test results. In addition log-files were analyzed. A total of 185 third-year students and 14 tutors took part in the study. Motivation, subjective learning gains and satisfaction achieved significantly higher ratings by the bPBL students compared with the students learning by traditional PBL. The tutors' opinion and the test results showed no differences between the groups. Working with the web-based learning environment was assessed as very good by the students. According to the log-file analysis, the web-based learning module was frequently used and improved the cooperation during the self-directed learning.

Extended query refinement for medical image retrieval.

The impact of image pattern recognition on accessing large databases of medical images has recently been explored, and content-based image retrieval (CBIR) in medical applications (IRMA) is researched. At the present, however, the impact of image retrieval on diagnosis is limited, and practical applications are scarce. One reason is the lack of suitable mechanisms for query refinement, in particular, the ability to (1) restore previous session states, (2) combine individual queries by Boolean operators, and (3) provide continuous-valued query refinement. This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement. The various mechanisms of man-machine interaction during a retrieval session are grouped into four classes: (1) output modules, (2) parameter modules, (3) transaction modules, and (4) process modules, all of which are controlled by a detailed query logging. The query logging is linked to a relational database. Nested loops for interaction provide a maximum of flexibility within a minimum of complexity, as the entire data flow is still controlled within a single Web page. Our approach is implemented to support various modalities, orientations, and body regions using global features that model gray scale, texture, structure, and global shape characteristics. The resulting extended query refinement has a significant impact for medical CBIR applications.

Automatic categorization of medical images for content-based retrieval and data mining.

Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.

Colour texture analysis for quantitative laryngoscopy.

OBJECTIVE: Whilst considerable progress has been made in enhancing the quality of indirect laryngoscopy and image processing, the evaluation of clinical findings is still based on the clinician's judgement. The aim of this paper was to examine the feasibility of an objective computer-based method for evaluating laryngeal disease. MATERIAL AND METHODS: Digitally recorded images obtained by 90 degree- and 70 degree-angled indirect rod laryngoscopy using standardized white balance values were made of 16 patients and 19 healthy subjects. The digital images were evaluated manually by the clinician based on a standardized questionnaire, and suspect lesions were marked and classified on the image. Following colour separation, normal vocal cord areas as well as suspect lesions were analyzed automatically using co-occurrence matrices, which compare colour differences between neighbouring pixels over a predefined distance. RESULTS: Whilst colour histograms did not provide sufficient information for distinguishing between healthy and diseased tissues, consideration of the blue content of neighbouring pixels enabled a correct classification in 81.4% of cases. If all colour channels (red, green and blue) were regarded simultaneously, the best classification correctness obtained was 77.1%. CONCLUSIONS: Although only a very basic classification differentiating between healthy and diseased tissue was attempted, the results showed progress compared to grey-scale histograms, which have been evaluated before. The results document a first step towards an objective, machine-based classification of laryngeal images, which could provide the basis for further development of an expert system for use in indirect laryngoscopy.