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Brain disease progression prediction method and system based on weakly supervised multi-task matrix completion

A matrix completion and multi-task technology, applied in the field of artificial intelligence and machine learning, can solve the problems of not considering features and sample noise, etc., and achieve good prediction accuracy

Active Publication Date: 2022-07-15
NANJING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, most of the existing papers only consider the selection of common feature subsets related to all tasks when performing feature selection, and do not consider the unique features of each task, nor consider the noise of the samples

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  • Brain disease progression prediction method and system based on weakly supervised multi-task matrix completion
  • Brain disease progression prediction method and system based on weakly supervised multi-task matrix completion
  • Brain disease progression prediction method and system based on weakly supervised multi-task matrix completion

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Embodiment Construction

[0069] Below in conjunction with the accompanying drawings and specific embodiments, the present invention will be further clarified. It should be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention. Modifications in the form of valence all fall within the scope defined by the appended claims of the present application.

[0070] A brain disease process prediction method based on weakly supervised multi-task matrix completion, such as figure 2 shown, including the following steps:

[0071] Step 1: Preprocessing multiple modal data such as magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) measurements measured by multiple subjects at baseline, Specifically include the following steps:

[0072] Step 1-1, Magnetic Resonance Imaging (MRI) using anterior commissure (AC) – posterior commissure (PC) correction, intensity inhomogeneity correction, skull ...

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Abstract

The invention discloses a brain disease process prediction method and system based on weakly supervised multi-task matrix completion, including a data acquisition unit, an offline processing unit, and a process prediction unit connected in sequence. The measured multiple modal data are preprocessed; the multi-task transductive matrix completion model is used to model the disease state prediction at multiple time points as a multi-task regression problem; the task-share features and task- specific features, using these two features to further improve the prediction accuracy of the score matrix, so as to complete the prediction of the disease process.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and machine learning, in particular to a brain disease process prediction method based on a weakly supervised multi-task matrix completion model. Background technique [0002] Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by damage to neurons and their connections, resulting in progressive memory loss and cognitive decline, and ultimately death. Recent studies suggest that there are approximately 26.6 million AD patients worldwide and that by 2050, 1 in 85 people will be affected by AD. Accurately predicting the disease process of AD can timely and effectively target patients according to the predicted results, which can greatly delay and improve the disease, which is of great significance for the clinical diagnosis and prognosis of AD. [0003] Many clinical / cognitive measures are designed to assess the cognitive status of patients and serve as import...

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Application Information

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IPC IPC(8): G16H50/50
CPCG16H50/50
Inventor 王凌胜陈蕾查思明李平
Owner NANJING UNIV OF POSTS & TELECOMM