METHOD AND APPARATUS FOR PREDICTING HEALTH DATA VALUE THROUGH GENERATION OF HEALTH DATA PATTERN

• US 20170147777A1
• Filed: 11/16/2016
• Published: 05/25/2017
• Est. Priority Date: 11/25/2015
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: KIM, Seunghwan, KIM, YoungWon, HAN, Youngwoong, KIM, Minho, JUNG, Ho-Youl, CHOI, Jae Hun, LIM, Myung-eun, KIM, Dae Hee
• Application Number:
  US15/353,671
• Publication Number:
  US 20170147777A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

The inventive concept relates to a method and apparatus for predicting health data values through the generation of a health data pattern. The inventive concept provides a health data value prediction method and apparatus. The health data value prediction method and apparatus may select health data values and important health characteristics associated with the health data values from big data on a plurality of pieces of time-series health information. The health data value prediction method and apparatus may form a health data value prediction model that has repetitively learned the pattern, and accurately predict a user'"'"'s health data value through the prediction model.

Claims

1. A method of predicting a health data value of an apparatus through generalization of a health data pattern, the method comprising:

performing learning of a prediction model for a health data value by using a pattern of a plurality of pieces of health data; and

generating a prediction model by verifying performance of the prediction model by determining a prediction model and,

wherein the prediction model is learned to output a generalized prediction result of health data.

2. The method of claim 1, further comprising:

selecting a health data value and health characteristic related to a specific disease from the health data and normalizing the selected health data value and the selected health characteristic.

3. The method of claim 2, further comprising:

dividing the normalized health data into a training data group and a verification data group; and

generating a pattern from the normalized health data of the divided training data group and the verification data group.

4. The method of claim 3, wherein the performing of the learning of the prediction model comprises:

performing the learning of the generated prediction model by using the training data group; and

verifying performance of the learned prediction model by using the verification data group.

5. The method of claim 1, further comprising:

performing preprocessing that comprises selecting a health data value and health characteristic related to a specific disease from user'"'"'s personal health data, and normalizing the selected health data value and health characteristic;

generating a pattern from the normalized personal health data; and

applying a prediction model to the generated pattern to extract a result of prediction on the user'"'"'s health data value.

6. The method of claim 1, wherein the prediction model is generated by applying of a machine learning technique that comprises deep network learning, machine learning, support vector machine (SVM), a neural network or the like.

7. The method of claim 1, wherein the prediction model predicts a future health data value from past time-series personal health data, wherein the future health data value is predicted by recovering of a damaged portion of the past time-series health data.

8. An apparatus for predicting a health data value through generalization of a health data pattern, the apparatus comprising:

a prediction model learning unit configured to perform learning of a prediction model for a health data value by using a pattern of a plurality of pieces of health data; and

a prediction model generation unit configured to generate the prediction model by verifying the prediction model to determining performance of the prediction model,

wherein the prediction model is learned to output a generalized prediction result of health data.

9. The apparatus of claim 8, further comprising:

a first preprocessing unit configured to select a health data value and health characteristic related to a specific disease from the health data and normalize the selected health data value and health characteristic.

10. The apparatus of claim 9, further comprising:

a training/verification data selection unit configured to divide the normalized health data into a training data group and a verification data group; and

a first pattern generating unit configured to generate a pattern from the health data of the training data group and the verification data group.

11. The apparatus of claim 10, wherein the prediction model learning unit configured perform the learning of the generated prediction model by using the training data group, and

the prediction model generation unit configured to verify performance of the learned prediction model by using the verification data group.

12. The apparatus of claim 8, further comprising:

a second preprocessing unit configured to select a health data value and health characteristic related to a specific disease from user'"'"'s personal health data, and normalize the selected health data value and health characteristic;

a second pattern generation unit configured to generate a pattern from the normalized personal health data; and

a health data value prediction unit configured extract a result of prediction on the user'"'"'s health data value by applying a prediction model to the generated pattern.

13. The apparatus of claim 8, wherein the prediction model is generated by applying of a machine learning technique that comprises deep network learning, machine learning, support vector machine (SVM), a neural network or the like.

14. The apparatus of claim 8, wherein the prediction model predicts a future health data value from past time-series personal health data, wherein the future health data value is predicted by recovering of a damaged portion of the past time-series health data.