Machine Learning Algorithms Drive Health Care Transformation

With a doctorate in anthropological sciences, Brian Richmond, Ph.D. serves as a senior data scientist at Aura Health in San Francisco, California. Dr. Brian Richmond, who has built the company’s first data infrastructure and published articles and blogs, utilizes machine learning models to improve and optimize product offering and performance.

A recent analytical study published in the New England Journal of Medicine brought focus to the potential of machine learning in transforming the healthcare industry. Machine learning algorithms can distinguish chromatic complex patterns and anticipate responses to specific treatments. In cases of rare and puzzling ailments, all available knowledge resources are corralled, with the patient’s case adding vital information to an expanding database.

One significant contribution of machine learning involves curated data that informs patient-provider interactions and enables evaluations and recommendations backed by the latest evidence-based methodologies. Machine learning can aid physicians in identifying health conditions quicker and more accurately by developing models that can recommend tests and health inquiries based on the patient’s data collected over time. Machine learning models can also sort through patients and automatically identify subpopulations who are eligible for clinical trials or new treatment procedures.

At Aura Health, Brian Richmond and his team use Python and R to build machine learning models that drive Aura’s mobile app by learning people’s preferences and delivering meditations and other content to help people feel less stressed or anxious, sleep better, and be happier and more focused.