Vidit202/t5-mimic-summary

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))