Vital sign data processing method, system and device and computer readable medium

Abstract

The invention relates to a data processing technology. The invention discloses a vital sign data processing method, system and device and a computer readable medium. The vital sign data processing method comprises the following steps: a, preprocessing vital sign data; b, learning random potential variables in the vital sign data, and constraining the variables as Gaussian distribution; c, learningfeature representation of vital sign data in each subset through two full-connection network layers of a neural network according to a specific task, and performing fusion learning on the learned features of each sub-task and random potential variable features; d, training the model, verifying the model by using the verification set every 30 iterations, and reserving the optimal model; and e, testing the stored optimal model by using the test set, and finally obtaining an evaluation result of each task. According to the method, the vital sign data of a healthy body can be subjected to accurate ability evaluation and risk evaluation, and the evaluation accuracy is greatly improved.

Description

technical field [0001] The present invention relates to a technique for analyzing and processing medical data by using a neural network and deep learning technology in the field, and in particular, relates to a vital sign data processing method, system, device, and computer-readable medium. Background technique [0002] Vital sign data mainly includes indicators of a healthy body such as blood pressure, blood sugar concentration, and blood oxygen level. According to medical research, through mining and analysis of some vital sign data, one can directly or indirectly know the current physical state of the healthy body and Evaluate some capabilities or possible future risks. Therefore, accurate modeling analysis and evaluation of vital sign data are of great importance in today's healthcare field. [0003] With the rapid development of artificial intelligence (especially deep learning), a series of new intersecting fields have emerged. Among them, the intersecting field of me...

AI Technical Summary

Problems solved by technology

[0005] However, these traditional methods mentioned above perform well in the face of a single classification task, but their performance is often unsatisfactory when dealing with multiple classification tasks at the same time.

[0006] This is because traditional machine learning methods ignore the interdependence and correlation between vital sign data features

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