Method and device for establishing model for early risk assessment of tachycardia events

A technology for tachycardia and risk assessment, applied in blood characterization devices, biological neural network models, computer components, etc., to achieve real-time assessment and optimal performance

Active Publication Date: 2021-02-12
BEIHANG UNIV
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  • Application Information

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Problems solved by technology

But so far, few studies have attempted to establish a personalized prediction model for early tachycardia events, a

Method used

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  • Method and device for establishing model for early risk assessment of tachycardia events
  • Method and device for establishing model for early risk assessment of tachycardia events
  • Method and device for establishing model for early risk assessment of tachycardia events

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Embodiment Construction

[0066] The following will combine Figure 1-14 The present invention will be described in detail.

[0067] The individualized early warning and risk real-time assessment model and device based on continuous monitoring of vital signs and electronic health records for the development of diseases / outcomes proposed by the present invention are mainly used for early prediction of the probability / risk of tachycardia in patients. It is easy and convenient to obtain recorded information, and it can be developed and applied to different application scenarios (intensive care / general ward). After training and verification of large sample data sets, the model can be evaluated in real time in the early stage of clinical risk, and fully automatic individualized and accurate evaluation of cardiac events. Rapid risk, prompting medical staff to pay attention to and intervene in potential high-risk patients as soon as possible. The present invention utilizes open-source data sets to accumulate...

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Abstract

The invention discloses a method and device for establishing a model for early risk assessment of tachycardia events. According to the method, through easily-obtained continuous monitoring vital signsand electronic health record information, real-time assessment and early warning of the tachycardia event risk of an inpatient are realized by utilizing advanced artificial intelligence deep learningand unsupervised learning methods; therefore, doctors are assisted in treating and intervening patients as soon as possible, and the workload of medical workers is reduced. The device mainly comprises the following execution steps: 1) a data set construction module; 2) a data processing module; and 3) a model construction and evaluation module. Through verification of different scene data sets, the method has high prediction performance, can be used to predict the occurrence of tachycardia events 0-6 hoursearlier only based on clinically easily obtained information, and is suitable for different scenes and medical resource configuration mechanisms.

Description

technical field [0001] The invention belongs to the technical field of medical information decision-making, and in particular relates to a method and device for establishing a universal individualized tachycardia event early risk assessment model based on deep learning and unsupervised learning. Background technique [0002] Tachycardia is a cardiac arrhythmia defined as a resting heart rate in adults exceeding 100 beats per minute. Tachycardia is usually divided into sinus tachycardia, atrial fibrillation (AF), atrial flutter, ventricular tachycardia (VT) and ventricular fibrillation (VF). Spontaneous VT is the main cause of sudden cardiac death (SCD). According to statistics, 533,000 to 606,000 patients worldwide suffer from the disease every year, and the mortality rate is as high as 15 to 20%. AF is one of the important risk factors leading to stroke, congestive heart failure and premature death, and patients with AF for the first time have a higher risk of death. In a...

Claims

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Application Information

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IPC IPC(8): G16H50/30G16H50/70G16H10/60G06N3/04G06K9/62A61B5/145A61B5/0205
CPCG16H50/30G16H50/70G16H10/60G06N3/049A61B5/0205A61B5/14542G06N3/045G06F18/23213
Inventor 李德玉刘晓莉张政波欧阳真超
Owner BEIHANG UNIV
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