BERT-6L-CSL Sepsis Diag is a BERT-style Transformer model designed for in-hospital mortality prediction from ICD diagnosis text. The model is built to process diagnosis information and predict outcomes related to sepsis, using a mode focused on diagnosis data. It employs an architecture with one encoder and six layers per branch, and is implemented in PyTorch.
Key technical specifications include a vocabulary size of 4,805, a hidden size of 128, four attention heads, a feed-forward dimension of 2,048, and a maximum sequence length of 30. The total number of parameters in the model is 4,206,978. The model can be loaded and used for inference in PyTorch, with example code provided for loading checkpoints and running predictions on input text.
BERT-6L-CSL Sepsis Diag is built for tasks involving sepsis diagnosis and mortality prediction from structured diagnosis codes. The model is available on the Hugging Face platform and is categorized as a BERT-style Transformer for clinical prediction tasks.
BERT 6L CSL Sepsis Diag sits in PulseGate's Other AI category. It enables healthcare researchers to predict sepsis mortality from ICD diagnosis text using deep learning models. It is built as an open-source project for healthcare researchers. BERT 6L CSL Sepsis Diag is open source under the Open Source license. BERT 6L CSL Sepsis Diag is available on the web and API.
Behind BERT 6L CSL Sepsis Diag is fansen, and the product first shipped in 2023. Key capabilities include sepsis prediction, BERT architecture, and text classification.
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