Nita200/educator-anchored-hitl-roberta

ROBERTA Educator-Anchored HITL — Educator-Anchored HITL Clinical Reasoning Classifier

This model is a fine-tuned version of roberta-base trained on the MedNLI dataset, used as a proxy for clinical reasoning scenarios in healthcare education.

It was refined using an educator-anchored Human-in-the-Loop (HITL) workflow across 20 rounds of incremental, simulated educator-guided corrections, as described in:

Educator-Anchored Human-in-the-Loop Learning: A Simulation Study of Transformer Models for Clinical Reasoning Assessment in Healthcare Education — [citation pending]

⚠️ Important: Simulation Study Disclaimer

This model is a research artefact from a simulation study, not a clinically validated or deployment-ready tool. Specifically:

• —Training data (MedNLI) is a proxy for clinical reasoning, sourced from MIMIC-III clinical notes — it is not authentic learner submissions from a healthcare education context.
• —HITL "educator corrections" during refinement were simulated (automated ground-truth relabelling of misclassified examples), not provided by real human educators.
• —Do not use this model for actual clinical decision-making, patient safety assessment, or student grading without further validation by qualified healthcare educators.

This model is intended for reproducibility and further research only.

Labels

Usage

from transformers import pipeline

clf = pipeline(
    "text-classification",
    model="Nita200/educator-anchored-hitl-roberta",
)
result = clf(
    "Patient has chest pain. Student assessment: possible GERD. "
    "[SEP] Rationale: The patient's history is consistent with GERD "
    "given the absence of cardiac risk factors."
)
print(result)

Training

• —Base model: roberta-base
• —Dataset: MedNLI (PhysioNet credentialed access required), original 80/10/10 train/validation/test split preserved
• —HITL configuration: v3 (catastrophic-forgetting-mitigated)
• —HITL rounds completed: 20 (stops early if the correction pool is exhausted before reaching the maximum of 20 rounds)
• —Corrections per round: 50
• —Replay buffer size: 100 (seed examples resampled each round to anchor prior representations and prevent catastrophic forgetting)
• —Learning rate: 5e-6
• —Epochs per round: 1
• —Seed/pool split: 70% seed / 30% pool

This configuration was selected after a systematic three-version comparison (see paper Section 4.3 and 5.2) showing that a naive incremental fine-tuning configuration (higher learning rate, larger correction batches, no replay buffer) produces catastrophic forgetting. Five-fold cross-validation (paper Section 5.7) confirms that the AUC stability achieved under this configuration generalises across independent data splits, while the magnitude of accuracy improvement in any single run is split-dependent.