Metadata-Version: 2.4
Name: medpython
Version: 1.0.1
Summary: Medial EarlySign Python
Author: Alon Lanyado
License-Expression: MIT
Project-URL: Github, https://github.com/Medial-EarlySign/MR_LIBS
Project-URL: Wiki, https://medial-earlysign.github.io/MR_Wiki/
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: C++
Classifier: Programming Language :: C
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENCE
Requires-Dist: numpy>=1.21.0
Requires-Dist: pandas
Requires-Dist: plotly
Requires-Dist: ipython
Dynamic: license-file

# 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](https://www.cms.gov/priorities/innovation/innovation-models/artificial-intelligence-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.

## Getting Started

*   **Build a new model:** Follow the step-by-step [Tutorials](https://medial-earlysign.github.io/MR_Wiki/Tutorials/index.html) to build a model from scratch.
*   **Use an existing model:** Browse the collection of [Models](https://medial-earlysign.github.io/MR_Wiki/Models/index.html).
