Year: 2023
Type: conference-paper
Abstract: In diagnosing challenging conditions such as Alzheimer's disease (AD), while imaging is an important reference, non-image data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role. However, limited by the ability of artificial intelligence models to mine such information, most of the existing models only use image data, and cannot make comprehensive use of non-image data. We based our approach on an existing pretrained large language model (LLM) to enhance the model's ability to utilize non-image data on diagnosing AD. Our test results on the ADNI dataset show that the approached achieved state-of-the-art (SOTA) performance.
Authors: Yingjie Feng, Xiaoyin Xu, Yueting Zhuang, Min Zhang
Institution: Zhejiang University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4391383134