US 12,112,841 B2Granted

Systems and methods for autoregressive recurrent neural networks for identifying actionable vital alerts

AssigneeCadence Solutions, Inc.

InventorAshwyn Sharma

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Abstract

An emerging service in the medical industry is to provide high quality remote care to patients by remotely monitoring patient vital information. In some instances, patient vitals information is collected at a much higher frequency in comparison to a traditional clinical environment. This frequent influx of patient health information can result in a considerable amount of health-related noise that health computing systems and clinicians must evaluate. The instant systems and methods leverage autoregressive recurrent neural networks and patient embeddings to predict the likelihood of needing to address certain patient information, thereby reducing the amount of health-related noise and to enable health computing systems and clinicians to use their resources more effectively.

Claims20

Dependent claims

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.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/375,006Filed 29 Sep 2023 · continuation
Priority: 17 Feb 2023
Published: US 2024/0282417 A122 Aug 2024
Granted: 8 Oct 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G16H 10/60
ICT specially adapted for the handling or processing of patient-related medical or healthcare data: for patient-specific data, e.g. for electronic…
G06N 3/0442
Recurrent networks, e.g. Hopfield networks: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks
G16H 50/20
ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or…: for…

Family

Application18/171,078Parent of this continuation · filed 17 Feb 2023
US 2024/0282417 A1Pre-grant publication · published 22 Aug 2024
US 11,817,193 B1Parent of this continuation
US 12,112,841 B2This patent

From the US filing history. Foreign family members aren’t listed.

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