Clinical Decision Support Systems using NLP and Computer Vision and their Integration in Healthcare
Kassym-Jomart Tokayev
L.N. Gumilyov Eurasian National University, city of Nur-Sultan
Khurshed Iqbal
UCOZ Campus, BUITEMS, Department of management sciences
Fatema Aly Younis
Faculty of Science, Biochemistry Department - Alexandria University
Keywords: Natural Language Processing (NLP), Clinical Decision Support Systems (CDSS), Computer Vision, Multimodal fusion, Personalized healthcare
Abstract
NLP techniques are employed to extract and analyze information from unstructured clinical text, including medical notes, research articles, and patient records. Named Entity Recognition (NER) is utilized to identify and classify entities such as medical terms, medications, diseases, and symptoms within the text. Text classification algorithms, such as Support Vector Machines (SVM) or deep learning models like Recurrent Neural Networks (RNN) and Transformers, can be employed to categorize clinical text into relevant domains, such as diagnosis, treatment, or prognosis. Furthermore, sentiment analysis techniques can determine the sentiment or emotion expressed in patient feedback or physician notes. Computer Vision techniques are applied to medical imaging data, including X-rays, CT scans, and MRI images, to aid in diagnosis and treatment decisions. Image segmentation algorithms are utilized to identify and separate different anatomical structures or abnormalities within medical images. Object detection and recognition methods are employed to identify specific features or pathologies within the images. Deep learning models like Convolutional Neural Networks (CNN) and their variants are commonly used for image classification, localization, and detection tasks in healthcare. Additionally, image registration techniques can align and compare images from different modalities or time points to monitor disease progression and treatment efficacy. The integration of NLP and Computer Vision in CDSS enables a comprehensive analysis of patient data by combining textual information with visual data. Multimodal fusion techniques can be applied to merge textual and visual data, providing a more holistic understanding of the patient's condition. By combining the outputs from NLP and Computer Vision, CDSS can provide more accurate and context-aware recommendations for diagnosis, treatment planning, and personalized care.
