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A method for comprehensive information extraction of medical time series data

A time-series data and comprehensive information technology, applied in medical informatics, computer-aided medical procedures, informatics, etc., can solve the problem of limited ability to capture information and interact, and achieve good analysis performance

Active Publication Date: 2022-06-28
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

These methods have relatively limited ability to capture information interaction

Method used

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  • A method for comprehensive information extraction of medical time series data
  • A method for comprehensive information extraction of medical time series data
  • A method for comprehensive information extraction of medical time series data

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Embodiment Construction

[0031] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0032] see attached figure 1 , the first embodiment implements a method for comprehensive information extraction of medical time series data according to the present invention, and the appendix figure 1 It is a flow chart of the method of Embodiment 1, comprising the following steps:

[0033] S1 obtains the medical time series feature matrix where t∈{1,2,...,T t } is the index of the time step, x t ∈R |C| represents the eigenvector corresponding to the time step t, and |C| represents the length of the eigenvector;

[0034] For example, the x t It can be a vector composed of the original medical feature values ​​of the t-th time step (such as 1 hour), such as blood glucose (Glucose), PH value, lactate value (Lactate), keto-acid value (Keto-acid), low pressure value, High pressure value, plasma HCO3 concentration, etc.; it can also be...

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Abstract

The invention relates to a method for extracting comprehensive information of medical time series data, which belongs to the technical field of artificial intelligence data processing. It includes the following steps: Obtain the medical time series feature matrix and perform dynamic modeling on X to obtain cumulative representation Interact the output of each time step with the output of the last time step Obtain the interactive results of each time step For each time step The interaction with the last time step assigns attention weights by summing all time steps by multiply-adding with the last time step. The overall representation after the interaction with the last time step will be fully modeled with concatenation for X. This invention works well with Model the interaction between time steps to more effectively learn the dynamically changing health status of patients, and these changes vary from person to person. The attention mechanism is used to distinguish the importance of different interactions, which can provide The ability to more fully represent patient EMR data, leading to better analytical performance and fine-grained medical analysis results.

Description

technical field [0001] The invention relates to a data processing method, in particular to a comprehensive information extraction method for medical time series data, and belongs to the technical field of artificial intelligence data processing. Background technique [0002] Healthcare analytics aims to improve patient management by analyzing various healthcare data through a data-driven approach to aid healthcare decisions and provide personalized treatment recommendations. With the progress of health informatization and the development of big data, international scientific researchers have paid more and more attention to and participated in the preservation and mining of medical big data. Electronic health records (EHR) are used for One of the important data sources for healthcare analytics. Electronic health records contain various forms of information, such as demographic information (such as age, gender, height, admission and discharge time, death, etc.), dynamic medic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H50/50G16H50/30G06N3/04G06N3/08
CPCG16H50/50G16H50/30G06N3/084G06N3/048G06N3/044
Inventor 蔡庆鹏郑凯平王伟姚畅张美慧
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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