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Method and device for predicting disease end point events, and electronic device

A disease and endpoint technology, applied in neural learning methods, biological neural network models, medical automated diagnosis, etc., can solve problems such as low accuracy of model prediction, and achieve the effect of improving accuracy

Active Publication Date: 2019-02-12
南京医基云医疗数据研究院有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, the existing models have problems such as low prediction accuracy

Method used

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  • Method and device for predicting disease end point events, and electronic device
  • Method and device for predicting disease end point events, and electronic device
  • Method and device for predicting disease end point events, and electronic device

Examples

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Embodiment Construction

[0038] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus their repeated descriptions will be omitted.

[0039] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or mo...

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Abstract

The invention relates to a method and device for predicting disease end point events, an electronic device and a computer readable medium. The method includes steps that the information at a disease occurrence and diagnosis and treatment stage is collected as the T0 sequence point feature; the information of each review is collected as the Ti sequence point feature of the corresponding review; a deep learning neural network model is utilized to predict a disease end point event in a future time window, in a DNN model of the deep learning neural network model, corresponding to each sequence point, one of the T0 sequence point feature to the Ti sequence point feature is respectively received, and a multidimensional vector is outputted; the multi-dimensional vector outputted by the DNN modelof each sequence point is received by a sequence neural network model of the deep learning neural network model; input from the sequence neural network model is received by an output layer of the deeplearning neural network model, and the output result is generated. The method is advantaged in that prediction accuracy can be improved.

Description

technical field [0001] The present disclosure relates to the field of computer information processing, in particular, to a method, method, device, electronic equipment and computer-readable medium for predicting disease endpoint events. Background technique [0002] The disease endpoint event refers to the occurrence of events such as recurrence and death within a certain period of time after the onset of a disease after treatment. Different diseases focus on different end points. For example, in the field of tumor, the n-year survival period is more concerned, and in stroke, the risk of recurrence is more concerned. The current popular prediction methods are based on traditional machine learning methods such as artificial neural networks, decision trees, logistic regression, and SVM. [0003] The prediction of endpoint events based on machine learning methods is based on a large number of historical patient medical record data as the training set, and the patient's basic i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20
CPCG16H50/20G06N3/08G06N3/044G06N3/045Y02A90/10
Inventor 李林峰
Owner 南京医基云医疗数据研究院有限公司
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