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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