Post
1748
Introducing BioClinicalBERT-Triage: A Medical Triage Classification Model
I'm excited to share my latest project: a fine-tuned model for medical triage classification!
What is BioClinicalBERT-Triage?
BioClinicalBERT-Triage is a specialized model that classifies patient-reported symptoms into appropriate triage categories. Built on the foundation of emilyalsentzer/Bio_ClinicalBERT, this model helps healthcare providers prioritize patient care by analyzing symptom descriptions and medical history.
Why I Built This
As healthcare systems face increasing demands, efficient triage becomes crucial. This model aims to support healthcare professionals in quickly assessing the urgency of medical situations, particularly in telehealth and high-volume settings.
Model Performance
The model was trained on 42,513 medical symptom descriptions, using an 80:20 train/test split. After 3 epochs of training, the model achieved:
Final training loss: 0.3246
Processing speed: 13.99 samples/second
The loss steadily decreased throughout training:
Early training (epoch 0.24): 0.5796
Mid-training (epoch 1.65): 0.4308
Final (epoch 2.82): 0.3246
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
Limitations & Ethical Considerations
This model is designed to support, not replace, clinical decision-making. It should always be used under the supervision of qualified healthcare professionals. While it performs well on common presentations, it may be less accurate for rare conditions or unusual symptom descriptions.
Try It Out
I'd love to hear your feedback if you use this model in your projects! Check out the full model card here: VolodymyrPugachov/BioClinicalBERT-Triage
#medical #healthcare #bert #nlp #triage #classification
I'm excited to share my latest project: a fine-tuned model for medical triage classification!
What is BioClinicalBERT-Triage?
BioClinicalBERT-Triage is a specialized model that classifies patient-reported symptoms into appropriate triage categories. Built on the foundation of emilyalsentzer/Bio_ClinicalBERT, this model helps healthcare providers prioritize patient care by analyzing symptom descriptions and medical history.
Why I Built This
As healthcare systems face increasing demands, efficient triage becomes crucial. This model aims to support healthcare professionals in quickly assessing the urgency of medical situations, particularly in telehealth and high-volume settings.
Model Performance
The model was trained on 42,513 medical symptom descriptions, using an 80:20 train/test split. After 3 epochs of training, the model achieved:
Final training loss: 0.3246
Processing speed: 13.99 samples/second
The loss steadily decreased throughout training:
Early training (epoch 0.24): 0.5796
Mid-training (epoch 1.65): 0.4308
Final (epoch 2.82): 0.3246
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
Limitations & Ethical Considerations
This model is designed to support, not replace, clinical decision-making. It should always be used under the supervision of qualified healthcare professionals. While it performs well on common presentations, it may be less accurate for rare conditions or unusual symptom descriptions.
Try It Out
I'd love to hear your feedback if you use this model in your projects! Check out the full model card here: VolodymyrPugachov/BioClinicalBERT-Triage
#medical #healthcare #bert #nlp #triage #classification