---
title: MiniMed EHR Analyst
emoji: ⚡
colorFrom: purple
colorTo: purple
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
license: apache-2.0
---

Created as part of the 2023 KREW Hackathon: https://pseudo-lab.github.io/huggingface-hackathon23/en/

DRAFT IN PROGRESS

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

This program represents a groundbreaking intersection of open-source technology and healthcare, opening up new possibilities for patient care and medical research.

The script you're looking at is a powerful tool that leverages the capabilities of Hugging Face's state-of-the-art language models fine-tuned on medical data. 
It's designed to analyze Electronic Health Records (EHRs), which are digital versions of patients' paper charts. EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users.

By connecting open EHR data systems like OpenEMR with Hugging Face's open-source language models, we can unlock a wealth of insights. 
OpenEMR is a popular open-source electronic health records and medical practice management solution, and its integration with Hugging Face's models can revolutionize how we understand and use EHR data.

The script begins by loading a pre-trained model and tokenizer from Hugging Face's model hub. 
The model, pseudolab/K23_MiniMed, has been fine-tuned on medical data, making it capable of understanding and generating text based on patient data.
We are still working on troubleshooting config issues with the k23_Minimed model, which currently prevent use-ability here.

The script then sets up a file uploader that allows you to upload a CSV file containing patient data. 
This data is then prepared for the model: it's converted into a string, tokenized, and truncated if necessary.

The implications of this are profound. 
With this tool, healthcare providers can quickly analyze patient data, identify patterns, and make informed decisions.
Researchers can study large volumes of data and uncover insights that could lead to new treatments or improved patient care. 
And because it's all built on open-source technology, the tool is accessible to anyone and can be continually improved by the community.
This is an act of open Mutual Aid in the medical sector!

In short, this script is more than just a piece of code.
It's a step towards a future where open-source technology and healthcare go hand in hand, leading to better outcomes for patients and exciting advancements in medical research.

Welcoming open collaboration.