Thank you for your feedback and major points about needing concrete experimental validation. Its obvious that the ideas need to be supported by rigorous experimental results and detailed technical documentation.
I completely understand your preference for seeing actual data tables, performance metrics, and architectural diagrams rather than high-level descriptions. So I'm pleased to share that we're targeting our first formal publications for late Q3/early Q4 2024.
Best,
Volodymyr
Pugachov PRO
VolodymyrPugachov
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11 days ago
Digital Heart Model: Initial Research Launch 🚀
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 ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 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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12 days ago
Digital Heart Model: Initial Research Launch 🚀
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 ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 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!
replied to
their
post
12 days ago
Digital Heart Model: Initial Research Launch 🚀
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 ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 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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12 days ago
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3707
Digital Heart Model: Initial Research Launch 🚀
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 ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 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!
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 ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 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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12 days ago
Hi Nicolay,
Initially, we aimed to chain together CNNs, Vision Transformers and GNNs into a unified pipeline for feature extraction across modalities. As we dug deeper, however, do we think that perhaps developing and fine-tuning a medicine-capable foundation model—one pre-trained on vast, multimodal cardiac data—would give us a far more flexible core for the Digital Heart Model?
In our current approach:
Foundation Model Pre‑training
We pre‑train an extensive, multimodal network on imaging (MRI/CT, histopathology) and ECG waveforms. This provides us with rich spatial, textural, and temporal representations in a single model.
Fine‑tuning for Clinical Tasks
From that base, we fine‑tune on specific tasks—event prediction, arrhythmia classification, segmentation—to achieve our target AUCs and Dice scores. This strategy leverages transfer learning to enhance performance, particularly when labelled clinical data are scarce.
Integration with Physics‑based Simulation
Finally, we will couple the fine-tuned AI core with a physical heart model, solving biomechanical and hemodynamic equations, to enable virtual interventions and personalised simulations.
We are finalising a detailed framework diagram and a preprint that outlines each of these steps, providing scientific rationale, training regimes, and fusion strategies. We will be publishing these diagrams and full results—including model checkpoints and code—on Hugging Face and arXiv.org in our upcoming posts. Stay tuned there for the deep dive! 🚀
Happy to dive into any specific aspect as we roll things out.
Best,
Volodymyr
posted
an
update
14 days ago
Post
3707
Digital Heart Model: Initial Research Launch 🚀
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 ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 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!
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 ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 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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4 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
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
posted
an
update
4 months ago
Post
1784
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