Healcisio is a partnership of clinicians and data scientists united by our appreciation of the complexities of healthcare and our shared passion for improving it.

Our mission is to provide improved actionable data on an individual basis through deeper insight into physiological systems, health, and disease. We accomplish this through the application of multivariate, multiscale, and multimodal signal processing and machine learning.

Our quantified biological methods concentrate on paradigms that consider holistic strategies built on a foundation of interconnected parallel biopsychosocial processes.

The core of our expertise spans signal processing, machine learning, big data analytics, control systems design, and clinical outcomes research with founding members hailing from MIT, Harvard, Georgia Tech, UC Berkeley, and UC San Diego. Our team has extensively published in both clinical and machine learning journals including Critical Care Medicine, CHEST, Annals of Emergency Medicine, Journal of Applied Physiology, and Nature Digital Medicine, among others. Our publication record spans several areas of machine learning including supervised and unsupervised learning, deep learning and reinforcement learning, global optimization techniques, time series analysis, dynamical systems modeling and optimal control.