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Found 6 papers, 0 papers with code
Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data
In our experiments, we observe that clinicalBERT and PubMed-BERT, when combined with attention fusion, can achieve an accuracy of 73% in multiclass chronic diseases and diabetes prediction.
Identification of Craving Maps among Marijuana Users via the Analysis of Functional Brain Networks with High-Order Attention Graph Neural Networks
The excessive consumption of marijuana can induce substantial psychological and social consequences.
Parkinson's Disease classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features
This work proposes a multimodal approach encompassing both image and non-image features, leveraging contrastive cross-view graph fusion for PD classification.
Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)
The stock market is characterized by a complex relationship between companies and the market.
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE
The proposed models achieved AUC scores above 0. 83 for hospital transfer risk prediction based on the measurements of past 1, 2, and 3 days, outperforming baseline machine learning methods.
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers
The images of the training set are embedded into a low-dimensional space by using diffusion maps.
