AI & ML interests

Developing Models that accelerate healthcare research and improve the patients' outcome

VolodymyrPugachovย 
posted an update about 2 months ago
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Digital Heart Model: Initial Research Launch ๐Ÿš€

I am excited to announce the launch of research on the Digital Heart Model (DHM), an AI-driven digital twin designed to transform personalized cardiovascular care. DHM integrates multimodal data, focusing initially on cardiac imaging, histopathological imaging, and ECG data, to predict patient outcomes and optimize interventions.

Initial Model and Dataset Overview:

Base Model: Multimodal AI foundation combining Convolutional Neural Networks (CNN), Vision Transformers (ViT), and Graph Neural Networks (GNN).

Datasets: Cardiac MRI and CT imaging datasets, histopathological cardiac tissue images, and extensive ECG waveform data.

Expected Results from First Iteration:

Cardiac event prediction (e.g., myocardial infarction) accuracy: AUC โ‰ฅ 0.90

Arrhythmia detection and classification accuracy: AUC โ‰ฅ 0.88

Enhanced segmentation accuracy for cardiac imaging: Dice Score โ‰ฅ 0.85

๐Ÿ” Next Steps:

Conducting initial retrospective validation.

Preparing for prospective clinical validation.

Stay tuned for updates as we redefine cardiovascular precision medicine!

Connect with us for collaboration and insights!
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VolodymyrPugachovย 
posted an update 5 months ago
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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