Mistral_calibrative_few

Model Description

This model is the few-shot trained calibrative fine-tuned version of Multi-CONFE (Confidence-Aware Medical Feature Extraction), built on unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit. It demonstrates exceptional data efficiency by achieving near state-of-the-art performance while training on only 12.5% of the available data, with particular emphasis on confidence calibration and hallucination reduction.

Intended Use

This model is designed for extracting clinically relevant features from medical patient notes with high accuracy and well-calibrated confidence scores in low-resource settings. It's particularly useful for automated assessment of medical documentation, such as USMLE Step-2 Clinical Skills notes, when training data is limited.

Training Data

The model was trained on just 100 annotated patient notes (12.5% of the full dataset) from the NBME - Score Clinical Patient Notes Kaggle competition dataset. This represents approximately 10 examples per clinical case type. The dataset contains USMLE Step-2 Clinical Skills patient notes covering 10 different clinical cases, with each note containing expert annotations for multiple medical features that need to be extracted.

Training Procedure

Training involved a two-phase approach:

Instructive Few-Shot Fine-Tuning: Initial alignment of the model with the medical feature extraction task using Mistral Nemo Instruct as the base model.
Calibrative Fine-Tuning: Integration of confidence calibration mechanisms, including bidirectional feature mapping, complexity-aware confidence adjustment, and dynamic thresholding.

Training hyperparameters:

Base model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
LoRA rank: 32
Training epochs: 14 (instructive phase) + 5 (calibrative phase)
Learning rate: 2e-4 (instructive phase), 1e-4 (calibrative phase)
Optimizer: AdamW (8-bit)
Hallucination weight: 0.2
Missing feature weight: 0.5
Confidence threshold: 0.7

Performance

On the USMLE Step-2 Clinical Skills notes dataset:

Precision: 0.982
Recall: 0.964
F1 Score: 0.973

The model achieves this impressive performance with only 12.5% of the training data used for the full model, demonstrating exceptional data efficiency. It reduces hallucination by 84.9% and missing features by 85.0% compared to vanilla models. This makes it particularly valuable for domains where annotated data may be scarce or expensive to obtain.

Limitations

The model was evaluated on standardized USMLE Step-2 Clinical Skills notes and may require adaptation for other clinical domains.
Some errors stem from knowledge gaps in specific medical terminology or inconsistencies in annotation.
Performance on multilingual or non-standardized clinical notes remains untested.
While highly effective, it still performs slightly below the full-data model (F1 score 0.973 vs. 0.981).

Ethical Considerations

Automated assessment systems must ensure fairness across different student populations. While the calibration mechanism enhances interpretability, systematic bias testing is recommended before deployment in high-stakes assessment scenarios. When using this model for educational assessment, we recommend:

Implementing a human-in-the-loop validation process
Regular auditing for demographic parity
Clear communication to students about the use of AI in assessment

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "Manal0809/Mistral_calibrative_few"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)

# Example input
patient_note = """HPI: 35 yo F with heavy uterine bleeding. Last normal period was 6 month ago. 
LMP was 2 months ago. No clots.
Changes tampon every few hours, previously 4/day. Menarche at 12.
Attempted using OCPs for menstrual regulation previously but unsuccessful.
Two adolescent children (ages unknown) at home.
Last PAP 6 months ago was normal, never abnormal.
Gained 10-15 lbs over the past few months, eating out more though.
Hyperpigmented spots on hands and LT neck that she noticed 1-2 years ago.
SH: state social worker; no smoking or drug use; beer or two on weekends;
sexually active with boyfriend of 14 months, uses condoms at first but no longer uses them."""

features_to_extract = ["35-year", "Female", "heavy-periods", "symptoms-for-6-months", 
                       "Weight-Gain", "Last-menstrual-period-2-months-ago", 
                       "Fatigue", "Unprotected-Sex", "Infertility"]

# Format input as shown in the paper
input_text = f"""###instruction: Extract medical features from the patient note.
###patient_history: {patient_note}
###features: {features_to_extract}
### Annotation:"""

# Generate output
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=512,
    temperature=0.2,
    num_return_sequences=1
)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

Model Card Author

Manal Abumelha - [email protected]

Manal0809
/

Mistral_calibrative_few