An AI-Driven Clinical Text-Based Decision Support System for Pancreatic Cancer Diagnosis

Abstract

The high mortality rate of pancreatic cancer and its detection at a late stage require advanced early diagnostic methods. The proposed AI-based clinical decision support system that leverages natural language processing (NLP) to extract key information from medical texts, such as patient records and reports. Using machine learning algorithms, including recurrent neural networks and transformer models, the system aims to identify early signs of pancreatic cancer with high accuracy. This research aims to bridge the gap between the increasing complexity of medical data and the need for user-friendly diagnostic tools. The model focus on a text analytics approach, integrating NLP techniques such as named entity recognition and sentiment analysis with machine learning for predictive modeling. The system interface will help healthcare professionals make informed decisions with a great accuracy for treatment recommendations. By facilitating early detection and providing actionable insights, the model hopes to significantly reduce the burden of pancreatic cancer.