Methods for determining disease risk combining downsampling of class-imbalanced sets with survival analysis

A survival analysis, downsampling technique used in the field of processing electronic data to determine disease risk

Pending Publication Date: 2021-08-17
SOMALOGIC OPERATING CO INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In this case, specificity will be maximized at the expense of sensitivity, which can be a problem when the goal is to identify as many individuals as possible at risk of developing the condition or event

Method used

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  • Methods for determining disease risk combining downsampling of class-imbalanced sets with survival analysis
  • Methods for determining disease risk combining downsampling of class-imbalanced sets with survival analysis
  • Methods for determining disease risk combining downsampling of class-imbalanced sets with survival analysis

Examples

Experimental program
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Embodiment 1

[0074] This example provides a description of the combination of downsampling and a Cox proportional hazards elastic net regression model to evaluate the prediction of a myocardial infarction (MI) event up to 4 years after the initial blood draw, which can be found in figure 2 Completed within the exemplary data risk analysis platform.

[0075] The purpose of this example is at least twofold: 1) to select and identify features predictive of both minority and majority classes, and 2) to derive estimated effect sizes such that minority class risk is well predicted. As a comparison, the predictive power of the logistic regression elastic net model (with and without downsampling) and that of the Cox elastic net model without downsampling were examined.

[0076] Materials and methods - Datasets

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PUM

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Abstract

A method for downsampling class-imbalanced sets with survival analysis comprising: acquiring a class-imbalanced data set, wherein the class-imbalanced data set comprises biological data from a plurality of subjects, wherein the biological data of each subject includes an observation, a time value, and a plurality of clinical measurements, and wherein the biological data is categorized as being part of a majority data class or a minority data class, wherein the majority data class has a greater number of observations than the minority data class; downsampling the class-imbalanced data set, wherein the downsampling results in the majority data class having an equivalent or substantially equivalent number of observations as the minority data class; and performing cross-validation on the downsampled data set with a survival analysis to generate a survival model, wherein the observation comprises an event or no event at a specific time value.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 773,028, filed November 29, 2018, and U.S. Provisional Patent Application No. 62 / 783,733, filed December 21, 2018, which are incorporated by reference in their entirety into this article. technical field [0003] The present disclosure relates generally to the field of disease risk determination, and more particularly, to systems and methods for processing electronic data to determine disease risk. Background technique [0004] Methods for identifying biomarkers associated with the risk of various disease-related conditions or events (e.g., cardiovascular events, diabetes diagnoses, various cancer types, etc.) have improved, mainly due to the discovery of high-throughput techniques , such as gene sequencing, transcriptomics, proteomics, and metabolomics. However, these techniques also complicate matters by providing high-dimensional data r...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/117A61B5/00
CPCA61B5/103A61B5/117A61B5/05G16H50/30G16H50/20A61B5/7275A61B5/7264A61B5/021A61B5/14546A61B5/4866A61B5/4872A61B5/7221G09B23/00G16H50/50
Inventor Y·夏甲G·达塔L·亚历山大M·欣特伯格
Owner SOMALOGIC OPERATING CO INC
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