| | --- |
| | library_name: transformers |
| | pipeline_tag: summarization |
| | tags: |
| | - seq2seq |
| | - summarization |
| | - clinical |
| | - patient-friendly |
| | - mimic-iv-bhc |
| | language: |
| | - en |
| | license: apache-2.0 |
| | datasets: |
| | - mimic-iv-bhc |
| | base_model: google/t5-small |
| | model_index: |
| | - name: Patient-Friendly Clinical Discharge Summarizer (T5-small) |
| | results: |
| | - task: |
| | type: summarization |
| | name: Abstractive Summarization |
| | dataset: |
| | name: MIMIC-IV-BHC (Behavioral Health Care Notes) |
| | type: physionet |
| | split: test |
| | metrics: |
| | - type: rouge1 |
| | value: 0.2126 |
| | - type: rouge2 |
| | value: 0.0958 |
| | - type: rougeL |
| | value: 0.1547 |
| | - type: bleu |
| | value: 0.0042 |
| | - type: bertscore_f1 |
| | value: 0.8339 |
| | --- |
| | # 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:** <your model URL> |
| | - **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 |
| | ```python |
| | 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)) |
| | |
