Query based biomedical document retrieval for clinical information access with the semantic similarity

The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of biomedical literature has opened up additional opportunities to apply Information Retrieval and NLP methods for mining existing archives. Therefore, a query based retrieval application (QBR) based on hybrid similarity of string and semantic similarity can help medical professionals in their ongoing research. There are multiple benefits of utilizing various NLP applications, for example, information retrieval engine, and clinical diagnosis frameworks for decision support in medical field. These applications depend on the capacity to gauge Hybrid textual similarity (HTS) and N-Gram similarity. Hybrid similarity is the combination of weighting function and word embedding models providing similarity scores with optimum results. In this work, the main focus is on building of a new biomedical document retrieval model which can pull relevant literature for clinical decision support system based on the specific query. There is also an attempt to compare the statistical and NLP based approaches of query based biomedical document retrieval with the baseline systems. Analysis of the proposed method inclusive of semantic word embeddings shows promising results for both of the suggested similarities. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.