Syntegra applies state-of-the-art machine learning models to create validated, synthetic data derivatives of health care data that match all of the statistical properties of the underlying data, but guaranteed to contain none of the original data, and cleared of privacy issues. Healthcare systems and data aggregators are forced to impose ever greater barriers to clinical data access. Syntegra is unique in developing a very large, open-access data platform of high quality, patient-level real-world data, trained using modern machine learning methods on health system information. Users can leverage our platform to conduct analytics, building predictive models, generating synthetic control groups for clinical trials and more. Since training is done on data at rest, and only deep learning model parameters are sent from the Syntegra API to the Syntegra servers, there is no risk of identifiable personal information leaving our secure platform. To satisfy health system security teams and end-users, numerous methods of validation, leak-testing and benchmarking are applied to the data derivatives before they pierce the health system’s firewall. Syntegra’s solution is scalable, and usable across many types of data, from images to financial information to sensor streams, will be securely added to our data lake over time. The company is led by a team of extraordinary serial entrepreneurs and university faculty with deep knowledge in medicine and data science.
Target Customer Segments
Digital Health Provider; Ambulatory Practice; Hospital / Health System; Health Plan; Life Sciences
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