Authors: Mohammad Ali Labbaf Khaniki, Sahabeh Saadati, Mohammad Manthouri
Abstract: This paper presents a novel Natural Language Processing (NLP) framework for
enhancing medical diagnosis through the integration of advanced techniques in
data augmentation, feature extraction, and classification. The proposed
approach employs back-translation to generate diverse paraphrased datasets,
improving robustness and mitigating overfitting in classification tasks.
Leveraging Decoding-enhanced BERT with Disentangled Attention (DeBERTa) with
Dynamic Contextual Positional Gating (DCPG), the model captures fine-grained
contextual and positional relationships, dynamically adjusting the influence of
positional information based on semantic context to produce high-quality text
embeddings. For classification, an Attention-Based Feedforward Neural Network
(ABFNN) is utilized, effectively focusing on the most relevant features to
improve decision-making accuracy. Applied to the classification of symptoms,
clinical notes, and other medical texts, this architecture demonstrates its
ability to address the complexities of medical data. The combination of data
augmentation, contextual embedding generation, and advanced classification
mechanisms offers a robust and accurate diagnostic tool, with potential
applications in automated medical diagnosis and clinical decision support. This
method demonstrates the effectiveness of the proposed NLP framework for medical
diagnosis, achieving remarkable results with an accuracy of 99.78%, recall of
99.72%, precision of 99.79%, and an F1-score of 99.75%. These metrics not only
underscore the model’s robust performance in classifying medical texts with
exceptional precision and reliability but also highlight its superiority over
existing methods, making it a highly promising tool for automated diagnostic
systems.
Source: http://arxiv.org/abs/2502.07755v1
