Abstract

Experts use clinical terminology to assess or draw conclusions about particular outcomes based on patients' symptoms. The specialists' clinical terms might be referred to as aspects, and the main work in any sentimental analysis is aspect extraction. The results produced by a pretrained model are the primary focus of the current natural language processing techniques for aspect extraction from a given text. These pre-trained models perform admirably for everyday English, but they struggle to produce precise results when it comes to clinical terminologies. to discuss the problems with clinical terminology. There are several transformer-based learning clinical models that outperform these clinical data, including Clinical Big Bird, Clinician Bert, and Clinical Long former. Large datasets yield better results from these models. This research compares the performance of these models and assesses coherence and context in addition to evaluating the Bio models' performance with sequential clinical content. The most effective outcomes of these models will be shared with an artificial bee colony for additional optimization and integration with AI-based applications.

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