A NAS-Based Risk Prediction Model and Interpretable System for Amyloidosis

Primary light chain amyloidosis is a rare hematologic disease with multi-organ involvement. Nearly one-third of patients with amyloidosis experience five or more consultations before diagnosis, which may lead to a poor prognosis due to delayed diagnosis. Early risk prediction based on artificial intelligence is valuable for clinical diagnosis and treatment of amyloidosis. For this disease, we propose an Evolutionary Neural Architecture Searching (ENAS) based risk prediction model, which achieves high-precision early risk prediction using physical examination data as a reference factor. To further enhance the value of clinic application, we designed a natural language-based interpretable system around the NAS-assisted risk prediction model for amyloidosis, which utilizes a large language model and Retrieval-Augmented Generation (RAG) to achieve further interpretation of the predicted conclusions. We also propose a document-based global semantic slicing approach in RAG to achieve more accurate slicing and improve the professionalism of the generated interpretations. Tests and implementation show that the proposed risk prediction model can be effectively used for early screening of amyloidosis and that the interpretation method based on the large language model and RAG can effectively provide professional interpretation of predicted results, which provides an effective method and means for the clinical applications of AI.