Multimodal Deep Learning and NLP-Based Intelligent Healthcare Assistant for Clinical Decision Support Systems

Abstract

The fast evolution of artificial intelligence in the medical field has made it possible to create intelligent clinical decision support systems that can enhance patient care and diagnosis. This article introduces Multimodal Deep Learning and NLP-Based Intelligent Healthcare Assistant that is developed to process heterogeneous medical data, such as electronic health records, medical images, laboratory reports, and clinical text. The suggested framework combines Convolutional Neural Networks (CNNs) to analyze medical images, Bidirectional Long Short-Term Memory (Bi-LSTM) networks to process sequential healthcare data, and the Transformer-based Natural Language Processing (NLP) to understand the meaning of a clinical note and patient history. A multimodal fusion mechanism that is based on attention is used in order to integrate the extracted features and come up with correct diagnostic recommendations. The system is able to predict diseases, assess risks, and support clinical processes in real-time, making diagnostic processes of healthcare professionals less complex. Experimental analysis shows the proposed model has better classification accuracy, precision, recall, and F1-score than traditional machine-learned and single-modal deep-learned methods. Clinical predictions are also made to be more robust and reliable with the integration of multimodal data. The smart healthcare assistant suggested is a highly efficient, scalable, and data-driven system that can be used as the next-generation clinical decision support system and personalised healthcare applications.