17764468. MRTHOD, DEVICE FOR DISEASE PREDICTION, ELECTRONIC DEVICE AND COMPUTER-READABLE STORAGE MEDIUM simplified abstract (BOE TECHNOLOGY GROUP CO., LTD.)

MRTHOD, DEVICE FOR DISEASE PREDICTION, ELECTRONIC DEVICE AND COMPUTER-READABLE STORAGE MEDIUM

Organization Name

BOE TECHNOLOGY GROUP CO., LTD.

Inventor(s)

Zhenzhong Zhang of Beijing (CN)

MRTHOD, DEVICE FOR DISEASE PREDICTION, ELECTRONIC DEVICE AND COMPUTER-READABLE STORAGE MEDIUM - A simplified explanation of the abstract

This abstract first appeared for US patent application 17764468 titled 'MRTHOD, DEVICE FOR DISEASE PREDICTION, ELECTRONIC DEVICE AND COMPUTER-READABLE STORAGE MEDIUM

Simplified Explanation

The abstract describes a method and device for disease prediction using a risk prediction model with linear and non-linear sub-models.

• Method for predicting disease:

   * Acquiring first and second features of a target object
   * Inputting features into risk prediction model with linear and non-linear sub-models
   * Processing first feature through linear sub-model to obtain first risk score
   * Processing second feature through non-linear sub-model to obtain second risk score
   * Calculating disease risk based on first and second risk scores

• • Potential Applications:**

- Healthcare industry for early disease detection and prevention - Personalized medicine for tailored treatment plans - Public health initiatives for population-level disease risk assessment

• • Problems Solved:**

- Early detection of diseases before symptoms manifest - Tailored treatment plans based on individual risk factors - Improved public health strategies for disease prevention

• • Benefits:**

- Improved accuracy in disease prediction - Early intervention for better treatment outcomes - Cost-effective healthcare management through targeted interventions

Original Abstract Submitted

A method and a device for disease prediction, an electronic device and a computer-readable storage medium are provided. A method for predicting disease comprising the steps of: respectively acquiring a first feature and a second feature of a target object; inputting the first feature and the second feature into a risk prediction model, wherein the risk prediction model comprises a linear sub-model and a non-linear sub-model obtained by joint training; processing the first feature through the linear sub-model to obtain a first risk score; processing the second feature through the non-linear sub-model to obtain a second risk score; calculating a disease risk of the target subject based on the first risk score and the second risk score.