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| 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. | |