US 12,112,841 B2
Systems and methods for autoregressive recurrent neural networks for identifying actionable vital alerts
Ashwyn Sharma, Seattle, WA (US)
Assigned to CADENCE SOLUTIONS, INC., New York, NY (US)
Filed by CADENCE SOLUTIONS, INC., New York, NY (US)
Filed on Sep. 29, 2023, as Appl. No. 18/375,006.
Application 18/375,006 is a continuation of application No. 18/171,078, filed on Feb. 17, 2023, granted, now 11,817,193.
Prior Publication US 2024/0282417 A1, Aug. 22, 2024
Int. Cl. G06N 3/0442 (2023.01); G16H 10/60 (2018.01)
CPC G16H 10/60 (2018.01) [G06N 3/0442 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a server comprising one or more processors; and
a non-transitory memory, in communication with the server, storing instructions that when executed by the one or more processors, cause the one or more processors to implement a method comprising:
receiving one or more features as input at one or more encoders, wherein the one or more features are associated with patient data;
converting, by the one or more encoders, the one or more features into one or more latent embeddings, wherein each of the one or more encoders converts a corresponding feature of the one or more features to generate an embedding for the corresponding feature;
concatenating the one or more latent embeddings into a singular patient embedding;
receiving the singular patient embedding at at least one decoder; and
reconstructing, by the at least one decoder, the one or more features based on the singular patient embedding;
wherein the one or more reconstructed features are used by a neural network for generating a prediction of triggering an actionable alert corresponding to the one or more features associated with the patient data.