Frozen Encoder BioBERT Medical Triage Classifier
Model Description
This repository contains a frozen-encoder BioBERT sequence classification model. The pretrained encoder is kept frozen and only the classification head is trained.
The model receives a short English symptom description and predicts one of the following triage-oriented labels:
self_monitorconsult_gpurgent
This model was developed as part of the SortMed academic project, a medical pre-triage assistant prototype built for a bachelor's thesis by Cristian Untaru at the West University of Timișoara, Faculty of Informatics.
The model is intended for research and educational use. It is not a diagnostic system and is not a certified medical device.
Intended Use
This model is intended to be used in an academic medical pre-triage prototype for:
- classifying English symptom descriptions into broad urgency categories;
- comparing transformer-based models for medical text classification;
- evaluating full fine-tuning against parameter-efficient fine-tuning methods;
- supporting experimental NLP research in symptom-based medical pre-triage.
Example input:
I have chest pain, shortness of breath, and I feel dizzy.
Example output:
urgent
Out-of-Scope Use
This model must not be used as:
- a replacement for professional medical advice;
- a diagnostic system;
- an emergency decision-making tool;
- a certified clinical triage system;
- a standalone healthcare product;
- a tool for non-English medical text without additional validation.
For severe, worsening, or emergency symptoms, users should contact emergency services or a qualified healthcare professional.
Labels
The model predicts one of three labels:
| Label | Meaning |
|---|---|
self_monitor | Symptoms appear mild and may be monitored, assuming no worsening or additional warning signs. |
consult_gp | A general practitioner or non-emergency medical professional should be consulted. |
urgent | Symptoms may require urgent medical attention or emergency evaluation. |
The label mapping is included in label_map.json and in the model configuration.
Expected mapping:
{
0: "self_monitor",
1: "consult_gp",
2: "urgent",
}
Training Data
The model was trained on the cristian-untaru/symcat-medical-triage-dataset, a SymCAT-derived dataset prepared for academic experiments in medical pre-triage classification.
The dataset contains English symptom-based text examples labeled into three triage classes:
self_monitorconsult_gpurgent
Dataset split used for training:
| Split | Examples |
|---|---|
| Train | 490 |
| Validation | 105 |
| Test | 106 |
| Total | 701 |
The dataset labels were produced using rule-based triage logic for academic experimentation. They are not clinically validated annotations from medical professionals.
MedQuAD was not used to train this classifier. In the broader SortMed system, MedQuAD is used separately for retrieval of related medical information through the cristian-untaru/medquad-retrieval-pretriage dataset.
Training Procedure
Base checkpoint:
dmis-lab/biobert-v1.1
| Parameter | Value |
|---|---|
| Fine-tuning method | Frozen Encoder + Trainable Classification Head |
| Maximum sequence length | 128 |
| Epochs | 8 |
| Train batch size | 16 |
| Evaluation batch size | 32 |
| Learning rate | 0.0002 |
| Weight decay | 0.01 |
| Warmup ratio | 0.1 |
| Mixed precision | FP16 |
| Early stopping patience | 3 |
| Best checkpoint metric | f1 |
| Random seed | 42 |
| Trainable modules | pooler, classifier |
Additional training metadata is available in training_config.json.
Evaluation Results
The model was evaluated on the held-out test split.
| Metric | Test Score |
|---|---|
| Accuracy | 0.6792 |
| Macro Precision | 0.7097 |
| Macro Recall / Sensitivity | 0.6689 |
| Macro Specificity | 0.8341 |
| Macro F1 | 0.6760 |
| Macro AUC OvR | 0.8579 |
| Macro IoU / Jaccard | 0.5108 |
| Test Loss | 0.8153 |
Per-Class Results
Per-class metrics are not currently published as a separate artifact in this repository. The aggregate test metrics above are the available published evaluation results.
These results should be interpreted in the context of the small dataset size and the academic nature of the experiment.
How to Use
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
repo_id = "cristian-untaru/frozen-encoder-biobert-medical-triage"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
text = "I have chest pain, shortness of breath, and I feel dizzy."
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
padding=True,
max_length=128
)
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.softmax(outputs.logits, dim=-1)
predicted_class_id = torch.argmax(probabilities, dim=-1).item()
predicted_label = model.config.id2label[predicted_class_id]
confidence = probabilities[0][predicted_class_id].item()
print("Predicted label:", predicted_label)
print("Confidence:", round(confidence, 4))
This model loads with the standard Transformers API and does not require trust_remote_code=True.
To verify the label mapping:
print(model.config.id2label)
Expected output:
{0: "self_monitor", 1: "consult_gp", 2: "urgent"}
Repository Files
| File | Description |
|---|---|
config.json | Model architecture, label mapping, and classification configuration. |
model.safetensors | Model weights in SafeTensors format. |
tokenizer.json | Tokenizer vocabulary and processing pipeline. |
tokenizer_config.json | Tokenizer configuration. |
training_config.json | Human-readable training configuration. |
label_map.json | Mapping between numeric class IDs and triage labels. |
README.md | Model card documentation. |
.gitattributes | Git LFS configuration for large model files. |
Limitations
This model has several important limitations:
- It was trained on a small academic dataset.
- The labels are rule-based and not clinically validated by medical experts.
- It does not perform medical diagnosis.
- It does not consider patient age, sex, medical history, medication history, vital signs, comorbidities, pregnancy status, allergies, or physical examination findings.
- It may produce incorrect predictions for rare, ambiguous, incomplete, severe, or contradictory symptom descriptions.
- It was trained for English text only.
- It should not be used for emergency triage without medical supervision.
- Its predictions depend on the quality and completeness of the user-provided text.
Ethical and Safety Considerations
Medical pre-triage systems can influence user decisions in sensitive healthcare situations. This model should therefore be used only with clear disclaimers and with safeguards that encourage users to seek professional medical care when appropriate.
Any real-world deployment would require:
- clinical expert review;
- validation on larger and more representative medical datasets;
- safety testing;
- bias and fairness evaluation;
- monitoring for incorrect or unsafe predictions;
- clear user-facing warnings;
- escalation rules for emergency symptoms.
Medical Disclaimer
This model is intended only for academic, research, and prototype development purposes. It is not a substitute for professional medical advice, diagnosis, treatment, or emergency care.
If symptoms are severe, sudden, worsening, or potentially life-threatening, users should contact emergency services or a qualified healthcare professional immediately.
Related Datasets
Related Models
Full Fine-Tuned Models
cristian-untaru/distilbert-medical-triagecristian-untaru/biobert-medical-triagecristian-untaru/roberta-medical-triagecristian-untaru/biomedbert-medical-triage
LoRA Models
cristian-untaru/lora-distilbert-medical-triagecristian-untaru/lora-biobert-medical-triagecristian-untaru/lora-roberta-medical-triagecristian-untaru/lora-biomedbert-medical-triage
Bottleneck MLP Adapter Models
cristian-untaru/bottleneck-mlp-distilbert-medical-triagecristian-untaru/bottleneck-mlp-biobert-medical-triagecristian-untaru/bottleneck-mlp-roberta-medical-triagecristian-untaru/bottleneck-mlp-biomedbert-medical-triage