Aetna Inc.

Ryan T Berns

Faisal Khan

Yue Xiao

Radhika G Athalye

Sanjeev Bollam

Gaurav Sharma

Hualiang Li

Jingran Li

William R Jones

Jamal Timsah

Sarah Wohlman

Naiqian Zhi

Rajiv Bhan

Alex Xu

Sumeet Kumar

Alan Leggitt

Qin Li

A system and method are disclosed for detecting chronic health conditions based on data collected by a wearable device such as an activity tracker or a smart watch. Deep learning algorithms are configured to process the monitored parameter data collected by the wearable device as well as additional embedding data obtained from health records corresponding to a user account registered to the wearable device. In some examples, the input vector can also include embedding data related to social determinants data and/or demographic data. The output of the deep learning algorithms provides predictions that represent probabilities that the user of the wearable device has an underlying health condition. If any underlying health condition is detected, then the user can be notified directly, via the wearable device or an associated application or technology, or indirectly, via a primary care provider associated with the user.

Machine learning framework for detection of chronic health conditions

Embodiments of the disclosure provide a system and method of allocating a resource based on myriad input data. In some embodiments, the myriad input data include membership information, claims data, transactional data, etc. The myriad input data are sorted and organized in a meaningful association relationship before applied to a resource allocation modeling algorithm. The resource allocation modeling algorithm provides estimated resource necessary for the application chosen. For example, an insurance company may use membership information, claims data, transactional data, etc., to estimate how much reserves or funds it should hold to cover future claims within a certain timeframe.

Resource allocation

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Southbury, CT, United States of America

Ryan T Berns