Healthcare – PyTorch

HippoScreen Improves AI Performance by 2.4x with oneAPI Tools

PyTorch Foundation — Tue, 21 Feb 2023 05:19:00 +0000

The Taiwan-based neurotechnology startup used tools and frameworks in the Intel® oneAPI Base and AI Analytics Toolkits to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system. As a result, HippoScreen is able to broaden the system’s applications to a wider range of psychiatric conditions and diseases.

Extracting value from siloed healthcare data using federated learning with Azure Machine Learning

PyTorch Foundation — Fri, 30 Dec 2022 05:23:00 +0000

Sensitive information such as healthcare data is often siloed within health organization boundaries. This has posed a challenge to machine learning models used by the health and life sciences industry that require data for training purposes. To improve patient care and accelerate health industry progression, the Microsoft Health & Life Sciences AI group used a federated learning setup to train their biomedical natural language processing service, Text Analytics for Health,

Crayon boosts speed, accuracy of healthcare auditing process using Azure Machine Learning and PyTorch

PyTorch Foundation — Tue, 28 Jun 2022 05:32:00 +0000

Healthcare providers need to be able to verify that they’re maintaining the highest operating safety and efficacy standards. Those standards are set by a national accreditation organization whose surveyors, often healthcare professionals themselves, regularly visit facilities and document situations that might need to be corrected or brought back in line with the latest rules and policies. That assessment and accreditation process generates a huge amount of data, and even the most experienced

ChemicalX: A Deep Learning Library for Drug Pair Scoring

PyTorch Foundation — Thu, 10 Feb 2022 05:42:00 +0000

In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scoring models accessible to machine learning researchers and practitioners in a streamlined this http URL design of ChemicalX reuses existing high level model training utilities, geometric deep learning, and deep chemistry layers from the PyTorch ecosystem.