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Shantanu B

shanbhardwaj
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AI & ML interests

medical, health, bio

Recent Activity

upvoted a collection 24 days ago
MedGemma Release
upvoted an article 2 months ago
nanoVLM: The simplest repository to train your VLM in pure PyTorch
reacted to VolodymyrPugachov's post with ➕ 4 months ago
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
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