A data model applied to the knowledge-based ICAS

Image-guided computer aided surgery system (ICAS) contributes to safeness and success of surgery operations by means of displaying anatomical structures and showing correlative information to surgeons in the process of operation. Based on analysis of requirements for ICAS, a new concept of clinical knowledge-based ICAS was proposed. Designing a reasonable data structure model is essential for realizing this new concept. The traditional data structure is limited in expressing and reusing the clinical knowledge such as locating an anatomical object, topological relations of anatomical objects and correlative clinical attributes. A data structure model called mixed adjacency lists by octree-path-chain (MALOC) was outlined, which can combine patient's images with clinical knowledge, as well as efficiently locate the instrument and search the objects' information. The efficiency of data structures was analyzed and experimental results were given in comparison to other traditional data structures. The result of the nasal surgery experiment proves that MALOC is a proper model for clinical knowledge-based ICAS that has advantages in not only locating the operative instrument precisely but also proving surgeons with real-time operation-correlative information. It is shown that the clinical knowledge-based ICAS with MALOC model has advantages in terms of safety and success of surgical operations, and help in accurately locating the operative instrument and providing operation-correlative knowledge and information to surgeons in the process of operations.     

Web-based biomedical literature mining

With an upsurge in biomedical literature, using data-mining method to search new knowledge from literature has drawing more attention of scholars. In this study, taking the mining of non-coding gene literature from the network database of PubMed as an example, we first preprocessed the abstract data, next applied the term occurrence frequency (TF) and inverse document frequency (IDF) (TF-IDF) method to select features, and then established a biomedical literature data-mining model based on Bayesian algorithm. Finally, we assessed the model through area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, precision rate and recall rate. When 1 000 features are selected, AUC, specificity, sensitivity, accuracy rate, precision rate and recall rate are 0.868 3, 84.63%, 89.02%, 86.83%, 89.02% and 98.14%, respectively. These results indicate that our method can identify the targeted literature related to a particular topic effectively.