Accelerating the exploitation of (bio)medical knowledge using linked data

Early identification and treatment of a diseases, especially when chronic, can reduce severe complications for the patients, doctors, and the society as a whole. Therefore, becoming aware and having insight about the state of the art findings on diseases, if communicated properly to different stakeholders, will benefit all. The medical research field, however, is vast and dynamically evolves with new discoveries. Additionally, new results are being continuously generated. The new discoveries on diseases address their diagnosis, prognosis, and possible treatment pathways for each disease, which are typically published in medical articles. Research results, however, are not reflected in practice by practitioners, unless they are officially verified by governments and authoritative health institutes, and appear in medical guidelines. Developing the medical guidelines requires identifying every relevant medical article, traversing through and validating it, as well as gathering and inter-relating that data to the information from other relevant sources, such as the drug interaction databases. Therefore, optimal exploitation of medical advances and research results by all its stakeholders, being researchers, practitioners, and patients, is essential. This, however, is hindered due to both the lack of integration of their typically disparate information, and the lack of facilities for coherent, up-to-date, and personalized access by their stakeholders. The few researches that address these issues do not sufficiently address the needed dynamism in data, lack intuitiveness in their use, and present a rather limited amount of information, which is usually obtained from a single source. This research aims to address these gaps through the development of BioMed Xplorer, presenting a model and a tool that enables researchers to rapidly query and explore biomedical knowledge from multiple sources, while preserving provenance data, and presenting all inter-linked information through an intuitive and personalized user interface. Results are further validated by some domain experts, through contrasting it against the state of the art, and with a task-based validation experimenting with the real case of updating medical guidelines