Biomedical Ontologies and Text Mining for Biomedicine and Healthcare

다국어 초록 (Multilingual Abstract)

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple-topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

목차 (Table of Contents)

• 1. INTRODUCTION
• 2. BIOMEDICAL ONTOLOGIES
• 3. DOCUMENT CLUSTERING
• 4. SWANSON'S UNDISCOVERED PUBLIC KNOWLEDGE (UDPK)
• 5. DOCUMENT CLASSIFICATION
• 6. INFORMATION EXTRACTION
• 7. EVALUATION
• 8. CONCLUSIONS
• REFERENCES