Medable Launches Cloud-Based Machine Learning Solution for Healthcare

Cerebrum is the first machine learning solution created specifically for healthcare apps

“Leveraging smartphones will bring on an era of pervasive health diagnostics”

PALO ALTO, Calif., April 04, 2017 -- MEDABLE Inc., the leading application and analytics platform for healthcare, today announced Cerebrum, the first cloud-based machine learning solution created specifically for healthcare apps.   Cerebrum leverages data gathering smartphones with a first-of-its-kind machine learning engine, resulting in health events becoming more easily predicted, such as warning an elderly relative when he is at greatest risk of a fall, or preventing an asthmatic child from triggering a life threatening episode.

Smartphones are the planet’s dominant accessory.  Cerebrum can be used with Apple HealthKit, CareKit and other mobile platforms to more effectively learn and treat digital signatures of health and disease. Medable’s analytics platform was built to address the unique concerns of the healthcare and life sciences industry, and provide artificial intelligence to the entire ecosystem.

“We are making smartphones smarter health devices,” said Michelle Longmire, MD, Medable’s CEO.  “We are on the cusp of an era of pervasive diagnostics.  Eventually smartphones will become intelligent diagnostic tools, like a physician in the palm of your hand.”  She said Cerebrum uses machine learning to mine patient reported data, smart phone digital data, and connected device data.  “We can now turn health data into deliverables,” said Dr. Longmire.  “With Cerebrum’s advanced artificial intelligence solution, we are identifying novel signatures of disease status, and creating predictive models that will make the world healthier.”

Cerebrum intelligently optimizes the network so data gets harvested efficiently, said Dr. Longmire, adding that the product is being used by beta customers at leading health research centers.  “Early adopters of Cerebrum will gain a competitive advantage,” she said.

Cerebrum provides machine learning across the ecosystem of clinical study data, including standard clinical instruments and patient reported outcomes data, meta-data from mobile devices, connected devices, and genomic and epigenomic data.  The adoption of cloud computing in the healthcare industry has been a slow process due to concerns around security, regulatory and compliance.  Healthcare providers have also spent enormous effort on data-gathering and data-preparation, but they have struggled with using the data itself.   Medable’s solutions address all of these concerns.  

Traditionally, data science has been a manual, iterative, and time intensive process, said Dr. Longmire. Cerebrum’s unique machine learning system has the ability to automate the identification of high-value predictors and provide rapid generation of novel insights.  Running hundreds or thousands of queries to discover a health pattern can be extremely time consuming for researchers. Cloud-based ML can reduce this time significantly and has the ability to extract high-quality inferences from large amounts of data with less intervention.   Health companies and researchers have struggled with return on investment (ROI) issues related to Big Data. Cerebrum can help them gain a much better understanding of their data through text classification and mining, emotion/behavior analysis, tagging, and other health-specific features. 

About Medable

Privately-held Medable provides a comprehensive, secure HIPAA-compliant cloud-based platform for the needs in healthcare.  Medable’s Cortex is the fastest path to scalable, interoperable, HIPAA compliant applications. Medable’s Axon delivers iPhone and Android clinical studies on a secure, compliant technology stack. Medable’s Cerebrum is the first cloud-based machine learning solution created specifically for healthcare apps.  Medable customers include device makers, payers, pharma, wearables, and researchers.  For more information visit:   www.medable.com

Gary Poole, PR Director
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