Named Entity Recognition in Medical Field using NLP

Computer Science

This research paper presents a novel approach for recommending medical specialists based on patients' symptoms using deep learning techniques. The project aims to address the challenge of accurately matching patients with the most appropriate healthcare professionals, thereby enhancing healthcare efficiency and patient outcomes. The data set utilized consists of symptom descriptions labeled with corresponding medical conditions and specialist recommendations. Initially, the data set undergoes preprossessing, including natural language processing (NLP) techniques and TF-IDF vectorization, to transform the raw text data into a format suitable for machine learning and deep learning algorithms. Subsequently, various machine learning algorithms such as Naive Bayes, Decision Trees, Random Forest, and Support Vector Machines (SVM) are applied to the preprocessed data. The results demonstrate promising performance, with Naive Bayes achieving an accuracy of 87.92%.In addition to traditional machine learning approaches, deep learning models including Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) neural networks are employed to further enhance the accuracy of specialist recommendations. The LSTM model achieves an accuracy of 83.75%, while the MLP model achieves an impressive accuracy of 98.33%.The findings of this research underscore the potential of deep learning techniques in improving the accuracy and efficiency of medical specialist recommendation systems. The results also highlight the importance of leveraging advanced computational methods in healthcare decision making processes.

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