Model Card for Patient-Friendly Clinical Discharge Summarizer (T5-small)
Model Details
Model Description
Fine-tuned google/t5-small to simplify behavioral health discharge notes (MIMIC-IV-BHC) into patient-friendly summaries. Part of the Patient-Friendly Summarization of Clinical Discharge Notes project; also explored with a RAG variant for added factual grounding.
- Developed by: Dhyan Patel & Vidit Gandhi
- Model type: Encoder–decoder transformer (seq2seq)
- Language(s): English
- License: Apache-2.0
- Finetuned from:
google/t5-small
Model Sources
- Repository:
- Dataset: MIMIC-IV-BHC (PhysioNet)
Uses
Direct Use
- Generate lay summaries from behavioral health discharge notes.
Downstream Use
- Embed in EHR/patient portals; pair with RAG to ground definitions and instructions.
Out-of-Scope
- Automated diagnosis/prescribing or any unsupervised clinical decision-making.
Bias, Risks, and Limitations
- May omit subtle clinical nuance; behavioral health notes can include sensitive content—human review is required.
- Risk of hallucinations if fed incomplete context.
How to Get Started
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "your-username/patient-friendly-mimic-iv-bhc-t5-small"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
text = "Paste a BHC discharge note..."
inputs = tok(text, return_tensors="pt", truncation=True, max_length=512)
summary_ids = model.generate(**inputs, max_length=128)
print(tok.decode(summary_ids[0], skip_special_tokens=True))