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System and method to maintain health using personal digital phenotypes

A data and data flow technology, applied in the system field, can solve problems such as unsatisfactory disease classification

Pending Publication Date: 2021-04-09
THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

There are still many unanswered questions: Why does the same ablation method work for some patients but not for others despite repeated attempts? Which mechanisms of rhythm disturbances are similar or different between individuals, and can the phenotypes of these rhythm disturbances be identified? The current disease classification is not ideal for this purpose, as pulmonary vein isolation fails in 35% to 50% of cases of "uncomplicated" paroxysmal AF between 1 and 2 years, but in "advanced" cases between 1 and 2 years Responds in 40%-50% of persistent AF cases
Currently, there are few (if any) systems in the art to achieve these goals

Method used

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  • System and method to maintain health using personal digital phenotypes
  • System and method to maintain health using personal digital phenotypes
  • System and method to maintain health using personal digital phenotypes

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Embodiment Construction

[0070] For the purposes of this disclosure, the following definitions apply:

[0071] "Associative learning" means the process of linking input data to measurable physiological or clinical outcomes. Associative learning can be iterative, enabling associations to be modified ("learned") based on patterns of change between inputs and measured outputs (physiological or clinical endpoints).

[0072] By "biological signal" is meant a signal produced by the body and which may reflect one or more body systems. For example, heart rate reflects cardiac function, autonomic tone, and other factors. See also abiotic signal.

[0073] "Biometric signal" means a signal that provides a measure of a human characteristic. Biometric identifiers can be physical or behavioral. Physiological biometrics include, but are not limited to, DNA, fingerprints or palm prints, buccal swabs, tissue or urine samples, retinal images, facial recognition, hand or foot geometry, individual irises, or identifi...

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Abstract

A system and method for identifying and treating a disease in a patient collects one or more data streams from sensors configured to detect biological signals generated within a patient's tissue over time. Patient data elements including one or more of demographic, clinical, laboratory, pathology, chemical, image, historical, genetic, and activity data for the patient is collected and processed with the data streams to generate a personalized digital phenotype (PDP). The PDP is compared to a digital taxonomy comprising prior data to classify the patient into one or more quantitative disease classifications to guide personalized intervention for treating the patient.

Description

[0001] related application [0002] This application claims the benefit of priority to U.S. Provisional Application No. 62 / 664,833, filed April 30, 2018, which is hereby incorporated by reference in its entirety. [0003] government rights [0004] This invention was made with Government support under Grant Numbers HL83359 and HL103800 awarded by the National Institutes of Health (NIH). The government has certain rights in this invention. technical field [0005] The present invention relates generally to personalized therapy for disease and health maintenance, and more particularly to a system and method for using data collected for a specific person (which can be referenced to a disease based on population data) digital taxonomy) to define digital phenotypes of disease, thereby providing identification of key abnormalities for personalized therapy. Background technique [0006] There is a growing recognition that medical therapies are often too general and can be improv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H20/00G16H50/20G16H10/60G16H50/70
CPCG16H50/20G16H10/60G16H50/70G16H20/00A61B5/361A61B5/363A61B5/0004A61B5/024A61B5/0816A61B5/4836A61B5/7246A61B5/7264A61B2562/0204A61B2562/0219A61B2562/0238A61B2562/0271
Inventor 桑吉夫·M·纳拉扬马哈茂德·侯赛尼
Owner THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV
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