Large language models improve Alzheimer's disease diagnosis using multi-modality data

• URL: http://arxiv.org/abs/2305.19280v1
• Date: Fri, 26 May 2023 18:42:19 GMT
• Title: Large language models improve Alzheimer's disease diagnosis using multi-modality data
• Authors: Yingjie Feng, Jun Wang, Xianfeng Gu, Xiaoyin Xu, and Min Zhang
• Abstract summary: Non-imaging patient data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role in diagnosis. We use a currently very popular pre-trained large language model (LLM) to enhance the model's ability to utilize non-image data.
• Score: 19.535491994272245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In diagnosing challenging conditions such as Alzheimer's disease (AD), imaging is an important reference. Non-imaging patient data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role in diagnosis. Effect. However, limited by the ability of artificial intelligence models to mine such information, most of the existing models only use multi-modal image data, and cannot make full use of non-image data. We use a currently very popular pre-trained large language model (LLM) to enhance the model's ability to utilize non-image data, and achieved SOTA results on the ADNI dataset.

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