EarlySign’s AI and Machine Learning

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EarlySign’s AI and Machine Learning

“Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”
Atul Butte, UCSF


Medial EarlySign is developing a state-of-the-art, AI-based machine learning toolset for handling large-scale medical databases with groundbreaking efficiency. This toolset features a proprietary database, especially designed for medical data analytics.

EarlySign's technology

Delivering Critical Insight One Hundred Times Faster 

Our technology helps us to quickly identify and effectively create a medical hypothesis, and test it numerous times against the existing data, with a machine learning-based, algorithmic engine that runs up to one thousand times faster than standard databases.  We are able to store the data in an exceptionally condensed and efficient way, to provide fast access to expansive medical databases, as well as rapidly scan and index the data. EarlySign’s ability to perform a large number of rapid test cycles results in a model of substantial quality and dramatically reduces the time to reach one.


Our cognitive algorithms consist of proprietary, machine learning tools developed internally, as well as conventional algorithms that our team has “deconstructed” and rewritten to optimize system performance.  The unique combination of algorithms and storage format enable us to work with much bigger data, resulting in highly accurate cognitive insight delivered at considerably higher speeds than standard databases.


Built from the Ground Up to Deliver Groundbreaking AI-based Solutions

The solutions proposed with EarlySign’s AI-based machine learning technology reflects our in-depth understanding of medical data, as well as the sheer scope of medical data at our disposal. We have accumulated substantial experience in handling electronic medical data, with the capacity to stabilize it, compare the data from different sources, and handle complex elements.


EarlySign’s team comprises researchers, doctors and data scientists, many of whom are experts in both the medical and the algorithmic world. This synergy fosters valuable insight and creatively steers the team to tackle gripping, clinical and technological challenges