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| title: Patient Treatment Plan Summarizer | |
| emoji: 👁 | |
| colorFrom: purple | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 6.11.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # Overview | |
| The goal of this application is to demonstrate how a healthcare clinic staff could quickly identify the outcome of each of the day's patient visits. | |
| Using Gradio, a dropdown provides a sampling of 25 patient IDs (the maximum patients the clinic can see in one day). | |
| A healthcare staff member can select a patient, see the motivation and summary from the record. | |
| If a synopsis of treatment is needed to quickly understand the treatment plan, optionally they can press a button to generate a synopsis of next-steps for this patient. | |
| This generated summary answers questions like: | |
| - Is the patient taking medication for treatment? | |
| - Are they being admitted to the hospital? | |
| - Have they completed their treatment and follow-ups and therefore are back to normal? | |
| # DataSet | |
| "AGBonnet/augmented-clinical-notes" is a DataSet of 30,000+ records. | |
| The dataset is available on HuggingFace containing medical notes from patient visits. | |
| The columns in the dataset contain Patient IDs, motivation for the patient seeking healthcare, a transcript of the discussion, a summary of the discussion, and a treatment/discharge plan provided in JSON format. | |
| # Model | |
| The model used is the microsoft/MediPhi-Clinical model with medical domain knowledge. | |
| Being domain-specific results in better at text-generation summarizing patient next-steps compared to generic models which struggle to comprehend medical terminology. | |
| Code: | |
| `generator = pipeline("text-generation", model="microsoft/MediPhi-Clinical", max_new_tokens=256, do_sample=True)` | |
| # Task | |
| The summarization pipeline is deprecated in HuggingFace as of `transformers` library version 5.0.0. | |
| The text-generation pipeline is used to effectively summarize the clinical notes in this dataset by providing the treatment plan from the dataset as context to the model. | |
| By prompting the model with instructions to generate patient instructions based on that context, it summarizes and clarifies the next-steps for the patient and the healtcare staff reading the record. | |
| # Limitations and Ethical Considerations | |
| The summarization model does use public models and may have inherit biases. | |
| Additionally, the model may not generate a perfect representation of the treatment plan in its summarization. | |
| Therefore, healthcare staff must use this as an assistive device, not as the source of truth. | |