The authors report that a BiLSTM-CRF feature extractor combined with XGBoost and logistic regression outperforms several baseline models for diabetes risk prediction on a private Beijing health-check dataset.
Graphical Structural Learning of rs-fMRI data in Heavy Smokers
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abstract
Recent studies revealed structural and functional brain changes in heavy smokers. However, the specific changes in topological brain connections are not well understood. We used Gaussian Undirected Graphs with the graphical lasso algorithm on rs-fMRI data from smokers and non-smokers to identify significant changes in brain connections. Our results indicate high stability in the estimated graphs and identify several brain regions significantly affected by smoking, providing valuable insights for future clinical research.
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Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
The authors report that a BiLSTM-CRF feature extractor combined with XGBoost and logistic regression outperforms several baseline models for diabetes risk prediction on a private Beijing health-check dataset.