Instructions to use CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification") model = AutoModelForSequenceClassification.from_pretrained("CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
emilyalsentzer/Bio_ClinicalBERT with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing."
Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter D Galer, Nina J Ghosn, Adam S Greenblatt, Tara Jennings, Alana Kornspun, Catherine V Kulick-Soper, Jal M Panchal, Akash R Pattnaik, Brittany H Scheid, Danmeng Wei, Micah Weitzman, Ramya Muthukrishnan, Joongwon Kim, Brian Litt, Colin A Ellis, Dan Roth, Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing, Journal of the American Medical Informatics Association, 2022;, ocac018, https://doi.org/10.1093/jamia/ocac018
Bio_ClinicalBERT_for_seizureFreedom_classification classifies patients has having seizures or being seizure free using the HPI and/or Interval History paragraphs from a medical note.
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