smith478/medical-summarization-and-transcription

Tools for transcribing and summarizing medical audio data

Medical summarization and transcription

This repo will contain explorations on using LLMs and ASR to process different types of medical data. Here is an example of the streamlit app. Below is a sample transcribed patient history. This can then be summarized to pull out pertinant information. The audio clip used here is ./audio/sample_patient_history.wav.

Getting started

Docker

The docker image can be built using ./Dockerfile. You can build it using the following command, run from the root directory

docker build --build-arg WB_API_KEY=<your_api_key> . -f Dockerfile --rm -t llm-finetuning:latest

Run docker container

First navigate to this repo on your local machine. Then run the container:

docker run --gpus all --name medical-summarization-and-transcription -it --rm -p 8888:8888 -p 8501:8501 -p 8000:8000 --entrypoint /bin/bash -w /medical-summarization-and-transcription -v $(pwd):/medical-summarization-and-transcription llm-finetuning:latest

Run jupyter from the container

Inside the Container:

jupyter lab --ip 0.0.0.0 --no-browser --allow-root --NotebookApp.token=''

Host machine access this url:

localhost:8888/<YOUR TREE HERE>

Run streamlit from the container

Inside the container:

streamlit run app.py

Host machine:

localhost:8501

Examples

• Evalueate pre-trained models on pubmed dataset: ./notebooks/dataset.ipynb
• Adjust audio sample rate to preprocess for Whisper model: ./notebooks/speech_sample_rate.ipynb
• Speech to text to summarization streamlit app: ./app.py

TODO

1. Evaluate pre-trained models on pubmed dataset
   1. Models:
      1. https://huggingface.co/Falconsai/medical_summarization
      2. https://huggingface.co/facebook/bart-large-cnn/discussions
      3. https://huggingface.co/Stancld/longt5-tglobal-large-16384-pubmed-3k_steps
   2. Dataset:
      1. https://huggingface.co/datasets/ccdv/pubmed-summarization
   3. Evaluation metric: rouge
2. Train model on pubmed dataset
3. Audio transcription: transcribe conversations between patients and doctors
   1. Models:
      1. https://huggingface.co/openai/whisper-large-v3
      2. https://huggingface.co/distil-whisper/distil-large-v2
      3. https://huggingface.co/pyannote/speaker-diarization-3.1
   2. Datasets:
      1. https://github.com/nazmulkazi/dataset_automated_medical_transcription
      2. https://mtsamples.com/
4. Experiment with phi-3-mini 4k and 128k instruct models, experiment with llama 3 8B, 8B Instruct models.