A set-transformer that embeds test names with GPT text vectors and predicts abnormal glucose, cholesterol, ferritin, and uric acid from incomplete lab panels, with AUC gains over MLP baselines.
A comparative analysis of machine learning models for the detection of undiagnosed diabetes patients,
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Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests
A set-transformer that embeds test names with GPT text vectors and predicts abnormal glucose, cholesterol, ferritin, and uric acid from incomplete lab panels, with AUC gains over MLP baselines.