NLP-driven integration of electrophysiology and traditional Chinese medicine for enhanced diagnostics and management of postpartum pain

Abstract

Postpartum pain encompasses a range of physical and emotional discomforts, often influenced by hormonal changes, physical recovery, and individual psychological states. The complex interactions between the variables can make it difficult for traditional diagnostic techniques to fully capture, creating inadequacies and inefficient management techniques. The aims to develop a comprehensive diagnostic and management framework for postpartum pain by integrating Natural Language Processing (NLP), electrophysiological data, and Traditional Chinese Medicine (TCM) principles. The seeks to enhance the accuracy of postpartum pain diagnosis, uncover meaningful correlations between TCM diagnoses and physiological markers, and optimize personalized treatment strategies. The focuses on analyzing textual data from patient-reported symptoms, medical records, and TCM diagnosis notes. Data pre-processing involves text cleaning and tokenization, followed by feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) to capture meaningful patterns. For diagnostics and management, a Refined Coyote Optimized Deep Recurrent Neural Network (RCO-DRNN) is employed to analyze and predict pain profiles, combining insights from TCM diagnoses with physiological markers. The results highlight the effectiveness of RCO-DRNN in accurately diagnosing pain types and offering personalized and holistic management strategies. This approach represents a significant advancement in integrating data-driven methodologies with traditional medical practices, providing a more comprehensive framework for postpartum pain management. The RCO-DRNN continuously beats the other models after thorough evaluation using metrics like MSE, MAE, and R², obtaining the lowest MSE (0.005), the smallest MAE (0.04), and the highest R² (0.98).