Skip to main content

Add your description here

Project description

Medilinda-ML 💊

PyPI version License: MIT Python Version

A complete machine learning pipeline to predict the causality of Adverse Drug Reactions (ADRs) from patient and medication data. This package provides tools for data preprocessing, feature engineering, model training, and evaluation, with seamless MLflow integration for experiment tracking.

Overview

The goal of Medilinda-ML is to provide a reproducible and easy-to-use system for assessing the likelihood that a suspected drug is the cause of an adverse reaction. The pipeline is built with scikit-learn and handles common challenges in clinical data, such as missing values and class imbalance (using SMOTE).

Features

  • End-to-End Pipeline: From raw data to a trained model.
  • Feature Engineering: Automatically calculates features like patient BMI, drug administration duration, and more.
  • Class Imbalance Handling: Uses SMOTE to create a balanced dataset for training.
  • Hyperparameter Tuning: Leverages RandomizedSearchCV to find the best model configuration.
  • Experiment Tracking: Integrated with MLflow to log parameters, metrics, and models.

Installation

Install Medilinda-ML directly from PyPI:

pip install medilinda-ml

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

medilinda_ml-0.1.4.tar.gz (10.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

medilinda_ml-0.1.4-py3-none-any.whl (11.5 kB view details)

Uploaded Python 3

File details

Details for the file medilinda_ml-0.1.4.tar.gz.

File metadata

  • Download URL: medilinda_ml-0.1.4.tar.gz
  • Upload date:
  • Size: 10.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for medilinda_ml-0.1.4.tar.gz
Algorithm Hash digest
SHA256 849c89d98e3dd6f70bf0faf0c3033fca35967d24da507a8d07359d0bd83cdc14
MD5 0001cb778d8172983960f3403979c448
BLAKE2b-256 398df69f02875a4caa57f21feacf50562a6f4608a97b4e4256084d38d0d2e3ee

See more details on using hashes here.

Provenance

The following attestation bundles were made for medilinda_ml-0.1.4.tar.gz:

Publisher: python-publish.yml on kraigochieng/medilinda

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file medilinda_ml-0.1.4-py3-none-any.whl.

File metadata

  • Download URL: medilinda_ml-0.1.4-py3-none-any.whl
  • Upload date:
  • Size: 11.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for medilinda_ml-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 2cb99cbb56edd295cdd1b51344e5efddaa498532b1e99a2c35b6240369eaf0d9
MD5 21acbf0f2c8610a7ad72c968ba4c342c
BLAKE2b-256 c6701ee876b70760378a258527086e0c44bbe5a0416c13bad7cd77c8c02ba445

See more details on using hashes here.

Provenance

The following attestation bundles were made for medilinda_ml-0.1.4-py3-none-any.whl:

Publisher: python-publish.yml on kraigochieng/medilinda

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page