Medical Intent Classification Using Ensemble and Deep Learning Models

Javeria Nawal ()
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Javeria Nawal: Department of Computer Science, Namal University, Mianwali 42210, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6 Special Issue: 7, issue 7, 207-219

Abstract: Introduction:Medical chatbots are innovative solutions that leverage Natural Language Processing (NLP) and Artificial Intelligence (AI) to enhance communication efficiency between healthcare providers and patients. In the realm of conversational AI, intent classification—the task of understanding a user's intent from natural language input—is both a complex and crucial aspect of the technology. This process is vital for ensuring that chatbots can accurately interpret and respond to patient queries in a meaningful and contextually appropriate manner.Novelty Statement:This research proposes a hybrid approach that combines transformer-based embeddings with traditional deep learning models to reduce both complexity and computational cost in medical intent classification. By integrating the strengths of advanced transformer techniques with more established models, this approach aims to improve efficiency without sacrificing performance, making it more suitable for real-world healthcare applications.Material and Method:This study investigates the use of context-aware word embeddings, including word2vec and sentence transformers, to capture rich semantic information from medical text. To refine the unstructured data, we apply various NLP preprocessing techniques, such as text cleaning, stopword removal, and lemmatization. For classification, we utilize a combination of ensemble-based and deep learning methods, including XGBoost, Random Forest, LSTM, and Bi-LSTM. These methods are tested on real-world data from 6,662 patients, with the dataset containing 25 distinct classes.Result and Discussion: Empirical analysis demonstrates that the Bi-LSTM model, when combined with sentence transformers, achieves an accuracy of 95.23%, outperforming state-of-the-art models reported in the relevant literature.Concluding Remarks:This research is expected to be highly beneficial to healthcare professionals by enhancing information extraction and enabling more effective handling of patient queries.

Keywords: NLP; Intent Classification; Word Embedding; Sentence Transformers; Health Informatics; Transformer Models (search for similar items in EconPapers)
Date: 2024
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