MIAWARE Software:3D Medical Image Analysis with Automated Reporting Engine and Ontology-based Search

which was designed and developed for doctor/radiologist assistance. It allows to analyze an image stack from computed axial tomography scan of lungs (thorax) and, at the same time, to mark all pathologies on images and report their characteristics. The reporting process is normalized- radiologists cannot describe pathological changes with their own words, but can only use some terms from a specific vocabulary set provided by the software. Consequently, a normalized radiological report is automatically generated. Furthermore, MIAWARE software is accompanied with an intelligent search engine for medical reports, based on the relations between parts of the lungs. A logical structure of the lungs is introduced to the search algorithm through the specially developed ontology. As a result, a deductive report search was obtained, which may be helpful for doctors while diagnosing patients ’ cases. Finally, the MIAWARE software can be considered also as a teaching tool for future radiologists and physicians.

Graph-Based Methods for Discovery Browsing with Semantic Predications

We present an extension to literature-based discovery that goes beyond making discoveries to a principled way of navigating through selected aspects of some biomedical domain. The method is a type of “discovery browsing” that guides the user through the research literature on a specified phenomenon. Poorly understood relationships may be explored through novel points of view, and potentially interesting relationships need not be known ahead of time. In a process of “cooperative reciprocity” the user iteratively focuses system output, thus controlling the large number of relationships often generated in literature-based discovery systems. The underlying technology exploits SemRep semantic predications represented as a graph of interconnected nodes (predication arguments) and edges (predicates). The system suggests paths in this graph, which represent chains of relationships. The methodology is illustrated with depressive disorder and focuses on the interaction of inflammation, circadian phenomena, and the neurotransmitter norepinephrine. Insight provided may contribute to enhanced understanding of the pathophysiology, treatment, and prevention of this disorder

Clustering cliques for graph-based summarization of the biomedical research literature

BACKGROUND: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts). RESULTS: SemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings. CONCLUSIONS: For 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively