AUTOMATIC MEDICAL TRANSCRIPTS SUMMARIZATION USING MACHINE LEARNING TECHNIQUES

Abstract

The healthcare sector and biomedical domain are essential for public health and medical advancement, providing services from clinical care to research. Healthcare facilities offer crucial services like check-ups and disease management, while the biomedical domain drives medical innovation through research and experimentation. With the increasing volume of biomedical literature, automatic text summarization is vital for efficiently extracting insights. These algorithms, equipped with domain-specific knowledge, simplify complex information, facilitating knowledge dissemination and collaboration. Additionally, in the rapidly evolving field of biomedical research, automatic summarization systems ensure timely access to up-to-date information by monitoring and summarizing the latest literature and databases. There are two main approaches of Automatic Text Summarization: Extractive and Abstractive. Extractive summarization involves selecting and extracting specific sentences or phrases directly from the source text, prioritizing their frequency or relevance to compose the summary. In contrast, Abstractive summarization interprets and paraphrases the content to create new sentences conveying the essential meaning in a concise form. In this research work, extractive text summarization techniques in biomedical domain are explored, focusing on issues such as redundancy, coherence, and the risk of overlooking crucial information. Extractive summarization techniques in the biomedical domain utilize various algorithms and approaches, including Frequency-based Methods, Graph-based Algorithms, and Machine Learning Approaches, to identify and extract key sentences or phrases from biomedical documents. Hybrid approaches combine multiple techniques to improve accuracy and coverage, effectively summarizing complex biomedical texts while addressing challenges such as redundancy and information loss. To address the identified research gaps, numerous novel approaches have been proposed for biomedical text summarization. Firstly, a novel approach using the Methathesaurus from UMLS to extract named entity concepts is proposed which applies the BERT method to generate concise summaries from Pubmed and Mtsamples. Further, an unsupervised approach focusing on semantic similarity and keyword-phrase extraction for both single- document and multi-document summarization is proposed. Furthermore, to further improve vi upon the results, a distinctive framework utilizing deep neural networks for contextually aware summarization of biomedical literature is proposed which employs a binary classifier and bidirectional long-short term memory recurrent neural network. To validate the proposed approaches, comparisons are made with baseline methods in biomedical text summarization, including a recent graph-based approach with the FP- Growth method. The results indicate that the last proposed approach outperforms state-of- the-art methods, achieving the highest ROUGE score of 0.96, surpassing the scores of the first and second approach (0.74, 0.76). The research concludes that the proposed methods demonstrate superior results in the medical domain compared to existing state-of-the-art techniques, highlighting the efficacy of the developed summarization approaches for biomedical literature.