| Summary: | 碩士 === 臺北醫學大學 === 醫學資訊研究所 === 93 === Medical information exits in scientific literatures, various databases, and patient data in the form of medical records, images and laboratory test reports, etc. Sufficient information is important for clinician to complete the jobs such as disease diagnosis, treatment, prevention and research. This research is focused on the topic of severe acute respiratory syndrome (SARS) and the knowledge management system named Ontology-based SARS KMS has been developed to integrate SARS related data resources including biomedical literature, electronic medical records (EMR), standard operating procedures (SOP) in a public hospital, and news reports.
In addition, the performances of three information retrieval (IR) models: Vector Space Model (VSM), Hidden Markov Model (HMM), and Topical Mixture Model (TMM) for biomedical information retrieval were studied to design an IR procedure incorporated in the Ontology-based SARS KMS.
For a new disease breaking out, most clinicians don’t have enough disease related knowledge so that they may not use correct keywords for searching. To solve this problem, the SARS Ontology structure was applied to design the query expansion procedure, which can expand user’s query to include more related keywords to help them find relevant information.
In conclusion, this research used the ontology structure to cover the SARS related knowledge including biomedicine, diagnostics, epidemiology, management, and syndrome and applied IR methods to help clinical personnel retrieve information from biomedical literature, EMR, SOP, and news reports. Althought this research is focused on the SARS topic, the same concepts and procedures with proper modification can also be applied to other diseases.
|
|---|
