clinical_report_generator_t5
Overview
A fine-tuned T5 (Text-to-Text Transfer Transformer) model designed to generate structured clinical summaries from raw physician notes and lab results. It transforms unstructured EHR data into a standard SOAP (Subjective, Objective, Assessment, Plan) format.
Model Architecture
The model follows a standard Encoder-Decoder Transformer framework:
- Shared Embeddings: Utilizes the same embedding space for encoder and decoder.
- Attention: Multi-head self-attention with relative position biases.
- Training Objective: Trained on the "MIMIC-III" dataset using a sequence-to-sequence objective.
- Inference: Optimized for beam search to ensure medical terminology consistency.
Intended Use
- Administrative Automation: Reducing the clerical burden on healthcare providers by drafting report summaries.
- Data Standardization: Converting various note styles into a unified clinical format.
- Education: Aiding medical students in learning structured reporting.
Limitations
- Hallucination Risk: Like all generative models, it may occasionally "hallucinate" medical facts or dosages. Human validation is mandatory.
- Privacy: The model must only be used within HIPAA-compliant environments to prevent accidental leakage of PHI (Protected Health Information).
- Biomedical Scope: Performance is best on general internal medicine; niche surgical specialties may require further fine-tuning.
