Search for: All records

Given the ubiquity of unstructured biomedical data, significant obstacles still remain in achieving accurate and fast access to online biomedical content. Accompanying semantic annotations with a growing volume biomedical content on the internet is critical to enhancing search engines’ context-aware indexing, improving search speed and retrieval accuracy. We propose a novel methodology for annotation recommendation in the biomedical content authoring environment by introducing the socio-technical approach where users can get recommendations from each other for accurate and high quality semantic annotations. We performed experiments to record the system level performance with and without socio-technical features in three scenarios of different context to evaluate the proposed socio-technical approach. At a system level, we achieved 89.98% precision, 89.61% recall, and an 89.45% F1-score for semantic annotation recollection. Similarly, a high accuracy of 90% is achieved with the socio-technical approach compared to without, which obtains 73% accuracy. However almost equable precision, recall, and F1- score of 90% is gained by scenario-1 and scenario-2, whereas scenario-3 achieved relatively less precision, recall and F1-score of 88%. We conclude that our proposed socio-technical approach produces proficient annotation recommendations that could be helpful for various uses ranging from context-aware indexing to retrieval accuracy. 

Each day a vast amount of unstructured content is generated in the biomedical domain from various sources such as clinical notes, research articles and medical reports. Such content contain a sufficient amount of efficient and meaningful information that needs to be converted into actionable knowledge for secondary use. However, accessing precise biomedical content is quite challenging because of content heterogeneity, missing and imprecise metadata and unavailability of associated semantic tags required for search engine optimization. We have introduced a socio-technical semantic annotation optimization approach that enhance the semantic search of biomedical contents. The proposed approach consist of layered architecture. At First layer (Preliminary Semantic Enrichment), it annotates the biomedical contents with the ontological concepts from NCBO BioPortal. With the growing biomedical information, the suggested semantic annotations from NCBO Bioportal are not always correct. Therefore, in the second layer (Optimizing the Enriched Semantic Information), we introduce a knowledge sharing scheme through which authors/users could request for recommendations from other users to optimize the semantic enrichment process. To guage the credibility of the the human recommended, our systems records the recommender confidence score, collects community voting against previous recommendations, stores percentage of correctly suggested annotation and translates that into an index to later connect right users to get suggestions to optimize the semantic enrichment of biomedical contents. At the preliminary layer of annotation from NCBO, we analyzed the n-gram strategy for biomedical word boundary identification. We have found that NCBO recognizes biomedical terms for n-gram-1 more than for n-gram-2 to n-gram-5. Similarly, a statistical measure conducted on significant features using the Wilson score and data normalization. In contrast, the proposed methodology achieves an suitable accuracy of ≈90% for the semantic optimization approach.