Medial EarlySign Python
Project description
Medial EarlySign Python Library
Our platform is designed to transform complex, semi-structured Electronic Medical Records (EMR) into machine-learning-ready data and reproducible model pipelines. The framework is optimized for the unique challenges of sparse, time-series EMR data, delivering low memory usage and high-speed processing at scale.
It was conceived as a TensorFlow for machine learning on medical data.
All software is now open-sourced under the MIT license. Some of the models developed by Medial EarlySign that are currently in production are available exclusively through our partners.
The framework was battle-tested in production across multiple healthcare sites and was a key component of an award-winning submission to the CMS AI Health Outcomes Challenge.
Why Use This Platform?
- High-Performance Processing: Engineered for large-scale, sparse EMR time-series data where general-purpose libraries like pandas fall short.
- Reusable Pipelines: Save valuable engineering time by providing shareable, tested pipelines and methods.
- Built-in Safeguards: Mitigate common pitfalls like data leakage and time-series-specific overfitting.
- Production-Ready: Designed for easy deployment using Docker or minimal distroless Linux images.
Core Components
The platform is built on three key pillars:
- MedRepository: A compact, efficient data repository and API for storing and accessing EMR signals. Querying categorical signals like perscriptions and diagnosis in an easy and efficient API.
- MedModel: An end-to-end machine learning pipeline that takes data from MedRepository or JSON EMR inputs to produce predictions and explainability outputs. It supports both training and inference.
- Medial Tools: A suite of utilities for training, evaluation, and workflow management, including bootstrap analysis, fairness checks, and explainability.
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