Developing health information feature abstractions from intra-individual temporal variance heteroskedasticity

A technique of heteroskedasticity and variance, applied in informatics, medical informatics, health index calculation, etc., can solve problems such as limitation and non-existence

Active Publication Date: 2016-10-12
KYNDRYL INC
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

While existing technologies (several mobile applications and web-based portals) help capture and store relevant da

Method used

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  • Developing health information feature abstractions from intra-individual temporal variance heteroskedasticity
  • Developing health information feature abstractions from intra-individual temporal variance heteroskedasticity
  • Developing health information feature abstractions from intra-individual temporal variance heteroskedasticity

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Embodiment Construction

[0013] The present invention can be a system, method and / or computer program product. A computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to perform various aspects of the present invention.

[0014] A computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access ...

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Abstract

A method, system, and/or computer program product automatically abstracts and se1ects an optimal set of variance-related features that are indicative of an individual outcome and persona1ized plan selection in health care. An abstracted set of candidate variance-related patient features, which comprise temporally heteroskedastic features, is generated. Each patient feature from the abstracted set of candidate variance-related patient features is optimized by identifying a time period in which variances and heteroskedasticity of each patient feature are maximized, where the optimizing creates an optimal abstracted set of variance-related patient features from the time period in which the variances and heteroskedasticity of each patient feature are maximized. The optimal abstracted set of variance-related patient features is then used for a current patient to predict a particular outcome and/or to create a personalized health care treatment plan.

Description

technical field [0001] This disclosure relates to the field of computers, and more particularly to the use of computers to analyze data. More specifically, the present disclosure relates to extracting and selecting an optimal set of variance-related features associated with healthcare patients. Background technique [0002] Disease self-management programs and intervention / care plan monitoring programs are not able to systematically utilize patient-generated information, especially those that require skilled interpretation of the temporal context of the measurements (examples include, but are not limited to, patient weight over time, Cholesterol levels, blood sugar levels, etc.) While existing technologies (several mobile applications and web-based portals) help capture and store relevant data, their ability to determine the appropriate measure most sensitive to that individual is limited or non-existent. This is because these techniques do not take into account the specif...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16H50/30G16H50/50G16H50/70
Inventor S·瑞玛克斯南P·莫维尔于珂M·阿库什维奇S·夏尔马薛沛芸
Owner KYNDRYL INC
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