CN-117281470-B - Alzheimer's disease prediction method, system, equipment and medium

CN117281470BCN 117281470 BCN117281470 BCN 117281470BCN-117281470-B

Abstract

The invention relates to the technical field of medical data processing, and discloses a method, a system, equipment and a medium for predicting Alzheimer's disease, wherein the method comprises the steps of obtaining spectrum data of a target group in a non-invasive mode; training to obtain a Alzheimer disease prediction model based on the spectrum data, wherein the target population comprises a healthy population and a population suffering from Alzheimer disease. According to the invention, the sample of the target group is obtained noninvasively by a non-invasive mode, the advantages of rapid detection and analysis speed of all elements are utilized, qualitative or quantitative analysis of chemical components on the surface of the material is realized by measuring the emission spectrum of microplasma, the spectrum data of the target group is accurately and rapidly obtained, and the early screening of a person to be detected can be effectively assisted by the Alzheimer disease prediction model obtained based on the spectrum data training, so that early Alzheimer disease patients can be intervened as early as possible, and the method has a wide application prospect.

Inventors

ZENG QIANG
ZHANG SHAOFENG
YANG LEI
Li Yaju
YE FEI
QIAN DONGBIN
CHENG WEI
JIN XIAODONG
LIU XUEQI
CHEN LIANGWEN
MA XINWEN

Assignees

中国科学院近代物理研究所

Dates

Publication Date: 20260505
Application Date: 20230914

Claims (5)

1. The method for constructing the Alzheimer disease prediction model is characterized by comprising the following steps of: obtaining spectral data of a target group in a non-invasive mode; Training to obtain an Alzheimer disease prediction model based on the spectrum data; The target population includes a healthy population and a population with alzheimer's disease; Wherein, the obtaining the spectrum data of the target group through a non-invasive mode comprises the following steps: collecting a fecal sample of a target population; based on the excrement sample, obtaining spectrum data of a target group by utilizing a laser-induced breakdown spectroscopy technology; The training to obtain the Alzheimer's disease prediction model based on the spectrum data comprises the following steps: preprocessing the spectrum data, wherein the preprocessing comprises abnormal data processing and spectrum area normalization processing; Training to obtain a Alzheimer disease prediction model based on the pretreated spectrum data; wherein, the normalization processing of the spectrum area is carried out on the spectrum data, specifically: Carrying out spectral area normalization processing on the spectral data through a normalization formula; Wherein, the normalization formula is: , in the normalization formula(s), Represents the spectral data after the normalization of the spectral area, Representing original spectral data of an ith row and a jth column in a spectral matrix of m rows and n columns; The training to obtain the Alzheimer's disease prediction model based on the spectrum data comprises the following steps: mapping the optical data through a kernel function; Obtaining a linear boundary based on the mapped spectrum data to train and obtain an Alzheimer disease prediction model; Wherein the expression of the kernel function is: , in the expression of the kernel function, The kernel function is represented by a function of the kernel, Representing the spectral vector of the i-th row, Representing the spectral vector of the j-th column, Representing the mapping; wherein the expression of the linear boundary is: , , , in the expression of the linear boundary(s), Representing the vector to be measured, b representing the intercept, Corresponding to the lagrangian multiplier for the row vector, The lagrangian multiplier corresponding to the column vector, A label vector representing the i-th row, Representing the label vector for the j-th column, X represents the overall spectral matrix, Representing the spectral vector of the i-th row, Representing the j-th column spectral vector.
2. The method for constructing a predictive model for alzheimer's disease according to claim 1, wherein said performing abnormal data processing on said spectral data comprises: obtaining a spectrum difference value according to the spectrums of the healthy population and the population suffering from Alzheimer's disease; determining an abnormal spectrum peak according to the spectrum difference value; And carrying out spectral line identification belonging to abnormal spectral peaks and filtering abnormal data.
3. A system for constructing a predictive model for alzheimer's disease, comprising: A data receiving module configured to receive spectral data of a target population obtained by a non-invasive manner; The model training module is configured to train to obtain an Alzheimer disease prediction model based on the spectrum data received by the data receiving module; wherein the target population comprises a healthy population and a population with alzheimer's disease; Wherein, the obtaining the spectrum data of the target group through a non-invasive mode comprises the following steps: collecting a fecal sample of a target population; based on the excrement sample, obtaining spectrum data of a target group by utilizing a laser-induced breakdown spectroscopy technology; The training to obtain the Alzheimer's disease prediction model based on the spectrum data comprises the following steps: preprocessing the spectrum data, wherein the preprocessing comprises abnormal data processing and spectrum area normalization processing; Training to obtain a Alzheimer disease prediction model based on the pretreated spectrum data; wherein, the normalization processing of the spectrum area is carried out on the spectrum data, specifically: Carrying out spectral area normalization processing on the spectral data through a normalization formula; Wherein, the normalization formula is: , in the normalization formula(s), Represents the spectral data after the normalization of the spectral area, Representing original spectral data of an ith row and a jth column in a spectral matrix of m rows and n columns; The training to obtain the Alzheimer's disease prediction model based on the spectrum data comprises the following steps: mapping the optical data through a kernel function; Obtaining a linear boundary based on the mapped spectrum data to train and obtain an Alzheimer disease prediction model; Wherein the expression of the kernel function is: , in the expression of the kernel function, The kernel function is represented by a function of the kernel, Representing the spectral vector of the i-th row, Representing the spectral vector of the j-th column, Representing the mapping; wherein the expression of the linear boundary is: , , , in the expression of the linear boundary(s), Representing the vector to be measured, b representing the intercept, Corresponding to the lagrangian multiplier for the row vector, The lagrangian multiplier corresponding to the column vector, A label vector representing the i-th row, Representing the label vector for the j-th column, X represents the overall spectral matrix, Representing the spectral vector of the i-th row, Representing the j-th column spectral vector.
4. An early screening system for alzheimer's disease, comprising: The data receiving module is configured to receive spectrum data of a to-be-detected person obtained in a non-invasive mode; The prediction module is configured to obtain an Alzheimer's disease prediction result of a person to be tested according to the spectrum data of the person to be tested, which is received by the data receiving module, through the Alzheimer's disease prediction model obtained by the method for constructing the Alzheimer's disease prediction model according to claim 1 or 2; Wherein the subject is a healthy subject, a patient suspected of having an asymptomatic phase or a pre-dementia phase of Alzheimer's disease, or a patient to be excluded from Alzheimer's disease.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor performs the steps of: Receiving spectral data of a to-be-detected person obtained in a non-invasive manner; according to the received spectral data of the testee, obtaining an Alzheimer's disease prediction result of the testee through the Alzheimer's disease prediction model obtained by the construction method of the Alzheimer's disease prediction model according to claim 1 or 2; Wherein the subject is a healthy subject, a patient suspected of having an asymptomatic phase or a pre-dementia phase of Alzheimer's disease, or a patient to be excluded from Alzheimer's disease.

Description

Alzheimer's disease prediction method, system, equipment and medium Technical Field The invention relates to the technical field of medical data processing, in particular to a method, a system, equipment and a medium for predicting Alzheimer's disease. Background In 2020, 5500 thousands of people have developed dementia worldwide, and the number of patients has increased at a rate of doubling over 20 years. As one of the main categories of dementia, alzheimer's Disease (AD) belongs to a neurodegenerative disease, without obvious pathological tissues. So far, there is no effective radical cure measure for AD, and early diagnosis and timely intervention are the only effective measures for delaying disease progression. AD is a continuous process comprising three phases, asymptomatic phase (preclinical AD), pre-dementia phase (mild cognitive impairment due to AD, MCI), dementia phase (dementia due to AD). Before 15-20 years of clinical symptoms, pathophysiological changes in AD have begun. Early prediction of AD can lead to a continuous advance in the diagnostic phase, competing for time for timely intervention. At present, the main diagnosis mode of Alzheimer's disease is also a behavior cognition test, and has hysteresis and uncertainty. Detection of biomarkers allows for early accurate diagnosis thereof, but detection of biomarkers is expensive and requires invasive human sampling, which is prone to injury. Therefore, there is a need in the medical field to develop noninvasive and rapid auxiliary diagnostic techniques for early screening of diseases such as Alzheimer's disease. Disclosure of Invention The invention provides a method, a system, equipment and a medium for predicting Alzheimer's disease, which are used for solving the defects of hysteresis, uncertainty and high detection cost in the detection of Alzheimer's disease in the prior art and realizing noninvasive and rapid assistance of a doctor in early screening Cha Aer of Alzheimer's disease. The invention provides a construction method of an Alzheimer disease prediction model, which comprises the following steps: obtaining spectral data of a target group in a non-invasive mode; Training to obtain an Alzheimer disease prediction model based on the spectrum data; The target population includes a healthy population and a population with alzheimer's disease. According to the method for constructing the Alzheimer's disease prediction model provided by the invention, the spectrum data of the target group is obtained in a non-invasive mode, and the method comprises the following steps: collecting a fecal sample of a target population; Based on the excrement sample, the spectral data of the target group are obtained by utilizing a laser-induced breakdown spectroscopy technology. According to the method for constructing the Alzheimer's disease prediction model provided by the invention, the Alzheimer's disease prediction model is obtained by training based on spectral data, and the method comprises the following steps: preprocessing the spectrum data, wherein the preprocessing comprises abnormal data processing and spectrum area normalization processing; based on the pretreated spectrum data, training to obtain the Alzheimer disease prediction model. According to the method for constructing the Alzheimer's disease prediction model, the abnormal data processing is carried out on the spectrum data, and the method comprises the following steps: obtaining a spectrum difference value according to the spectrums of the healthy population and the population suffering from Alzheimer's disease; determining an abnormal spectrum peak according to the spectrum difference value; And carrying out spectral line identification belonging to abnormal spectral peaks and filtering abnormal data. According to the method for constructing the Alzheimer's disease prediction model, the spectrum area normalization processing is carried out on the spectrum data, and the method specifically comprises the following steps: Carrying out spectral area normalization processing on the spectral data through a normalization formula; Wherein, the normalization formula is: in the normalization formula(s), Representing the spectral data after the normalization of the spectral area, x ij represents the original spectral data of the ith row and the jth column in the spectral matrix of m rows and n columns. According to the method for constructing the Alzheimer's disease prediction model provided by the invention, the Alzheimer's disease prediction model is obtained by training based on spectral data, and the method comprises the following steps: mapping the optical data through a kernel function; Based on the mapped spectrum data, a linear boundary is obtained to train and obtain the Alzheimer's disease prediction model. According to the method for constructing the Alzheimer disease prediction model provided by the invention, the expression of the kernel function is as follows: In the expression