| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程學系 === 106 === The objectives of this study is to extract the relationship between the specified nouns from natural language sentences and applies them in the biomedical literature to quickly find useful relationships in the literature. Although this study is based on the biomedical literature, researchers in various fields can also use this method to quickly and correctly retrieve the literature and materials they need when discussing relevant literature in their field.
The data sets used in this study were divided into two parts, and the two parts of data were individually independent in the experiments. The first part is based on the official US completed disease studies and drug pairings on the Clinical trials (https://clinicaltrials.gov) website and the relevant Medline abstracts to the target disease-drug pairs is retrieved through the PubMed database (https://www.ncbi.nlm.nih. gov/pubmed). The data is divided into two categories: from the PubMed article abstracts to find the sentences containing the drugs that clinical trials mentioned the drug able to treat some specified disease, regarded as positive sentences. If the same disease can not be treated by drugs or the disease and drugs have no connection, the sentences are considered as negatives.
The other part is provided by SemEval 2013 Task 9, which includes MedLine abstracts and a corpus of DrugBank's database. SemEval 2013 Task 9 is a competition for drug interactions from the biomedical literature (SemEval 2013 Task 9: Extraction Of Drug-Drug Interactions from Biomedical Texts), which divides the interactions between drugs into five categories: Advice, Effect, Mechanism, Int, and False.
This study dose the feature extraction through a multi-level machine learning method with basic word conversion and natural language sentence analysis. In this study, the best results in the drug-disease relationship identification experiment were 75.7% for Accuracy, 76.3% for Precision, 74.6% for Recall, and 75.5% for F-score. The best results for the drug-drug relationship identification experiment were 47.8% precision rate, 72.4% recall rate and 57.6% F-score.
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