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Personalized adverse drug reaction prediction method, system and device and medium

A technology of adverse reactions and prediction methods, applied in the field of biomedicine, can solve the problems of low efficiency of model calculation performance, time and space complexity, and achieve the effect of shortening the clinical trial cycle of new drugs and good practical application value

Active Publication Date: 2020-10-30
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the inventors found that the drawback of this fully individualized modeling strategy is that its model computational performance is not efficient in terms of time and space complexity, especially when the number of patients with ADRs or the histories associated with ADRs is large

Method used

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  • Personalized adverse drug reaction prediction method, system and device and medium
  • Personalized adverse drug reaction prediction method, system and device and medium
  • Personalized adverse drug reaction prediction method, system and device and medium

Examples

Experimental program
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Effect test

Embodiment 1

[0086] This embodiment proposes a multi-task learning model (KEMULA) based on a multi-kernel function to replace the traditional "one size fits all" and "completely individualized" learning methods. More specifically, the KEMULA model learns a constrained personalized ADRs ranking function to compute and rank each patient's ADRs occurrence risk score by assuming a shared function of the model. This function is called personalized ADRs ranking function (personalized ADR ranking function, or personADRank). Personal ADRank is a linear combination of multiple scoring functions that calculates a patient's risk of developing associated ADRs. The KEMULA model also incorporates Laplacian regularization to ensure that the personADRank function of similar patients is trained with close variable information, which improves the model's causality (true positives) of associations between a given patient and the corresponding ADRs. The schematic diagram of personADRank is as follows figure...

Embodiment 2

[0271] A personalized adverse drug reaction prediction system, the prediction system comprising:

[0272] Clinical data acquisition module: acquire clinical data of subjects;

[0273] Prediction model building block: Based on the KEMULA prediction model to predict the subject, get the subject's personalized adverse drug reaction results.

[0274] Wherein, the clinical data of the subject at least includes the information of the subject taking small molecule drugs, biotechnology drugs and the subject's medical condition (referred to as indications in the present invention).

[0275] The KEMULA prediction model includes a personalized ADR ranking function, which is specifically a linear combination of several personalized scoring functions that calculate the risk of a patient's occurrence of related ADRs.

[0276] The scoring function includes at least a small molecule drug function, a biotechnology drug function and a disease characteristic function.

[0277] Wherein, the fun...

Embodiment 3

[0295] An electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are run by the processor, each operation in the method of Embodiment 1 is completed. For brevity, here No longer.

[0296] Described electronic device can be mobile terminal and non-mobile terminal, and non-mobile terminal comprises desktop computer, and mobile terminal comprises smart phone (Smart Phone, such as Android mobile phone, IOS mobile phone etc.), smart glasses, smart watch, smart bracelet, tablet computer , laptops, personal digital assistants and other mobile Internet devices that can communicate wirelessly.

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Abstract

The invention provides a personalized adverse drug reaction prediction method, system and device and a medium, and belongs to the technical field of biomedicine. The invention provides a multi-task learning model (KEMULA) based on multi-kernel function learning so as to replace traditional learning methods of universal application and complete individuation. More specifically, the model calculatesand ranks the risks of ADR development of patients by learning a constrained personalized ADR ranking function by assuming a sharing function of the model. This function is referred to as a personalized ADR ranking function, which is a linear combination of several scoring functions that calculate the risk of developing related ADR of patients. In addition, the model is also combined with Laplacian regularization to ensure that variable information trained by personADRank functions of similar patients is close, so that the causal relationship (true positive) of the model to the association between a given patient and the corresponding ADR can be improved. Therefore, the method has good practical application value.

Description

technical field [0001] The invention belongs to the technical field of biomedicine, and in particular relates to a personalized drug adverse reaction prediction method, system, equipment and medium. Background technique [0002] The information disclosed in this background section is only intended to increase the understanding of the general background of the present invention, and should not be taken as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. [0003] Adverse drug reactions (ADRs) are described by the World Health Organization as "toxic and unexpected drug reactions that generally occur within the dose range normally used in humans". ADRs pose a serious challenge to public health security. In the United States alone, more than 21,000 ADRs were reported in 2004, and it is estimated that 6-7% of hospitalized patients experience adverse drug reactions each year. Additionally, drug-r...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H70/40
CPCG16H70/40
Inventor 杨帆薛付忠江冰薛浩
Owner SHANDONG UNIV
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