Background: Retrieving gene and disease information from a very large collection of biomedical abstracts to provide doctors with clinical decision support is one of the important research directions of Precision Medicine.
Method: We propose a new method for the retrieval of biomedical articles utilizing expanded word and co-word implementations and conducting Cuckoo Search to optimize parameters of the retrieval function in the final stage of the proposed method. The specific goal is to retrieve biomedical abstracts of articles addressing treatments. The method employed in this manuscript first implements the BM25 algorithm to compute the score of the abstract, then we propose a method utilizing the BM25, an improved version of BM25, to compute the scores of expanded words and co-word that lead to a composite retrieval function. Afterward, the retrieval function is optimized using Cuckoo Search. The proposed method is utilized to find both disease and gene in the abstract of the same biomedical article. By doing so, the relevance of articles would tend to increase so would the score of the biomedical article. Besides, the manuscript discusses the influence of different parameters on the retrieval algorithm and summarizes the parameters to meet various retrieval needs.
Results: All data are taken from medical articles provided in the Text Retrieval Conference (TREC) utilizing Clinical Decision Support (CDS) Tracks of 2017, 2018, and 2019 in Precision Medicine. 120 standard topics are tested. Three test indicators are employed to make comparisons among the methods utilized. To conduct comparable experiments, only the BM25 algorithm and its improved version of it are utilized. The experimental results show that the proposed algorithm achieves both better results and ranking outcomes.
Conclusion: The proposed algorithm, an improved version of the BM25 algorithm, utilizes both co-word implementation and Cuckoo Search and verifies that the proposed algorithm produces better results on a large number of experimental sets. On the other hand, a relatively simple query expansion method is implemented in this manuscript. As a future direction of this research, both the ontology and semantic network to expand the query vocabulary is planned to be conducted.