CN111175480A - Method for calculating gender and age by blood biochemical indexes - Google Patents
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Abstract
The invention relates to the technical field of blood detection, and discloses a method for calculating gender and age by blood biochemical indicators. The blood index is a measurement object, the comprehensiveness of measurement is increased, meanwhile, the blood index contains multi-group chemical metabolites, the individual aging condition can be comprehensively reflected, the aging level is explained, the blood biochemical index is calculated through a DNN model, the whole process is more convenient, the blood biochemical index can be transmitted into the DNN model, the test result can be obtained, various parameters and model structures in the calculation model are verified for multiple times, the accuracy of the calculation result is guaranteed, meanwhile, the selected blood biochemical index is the most common index in clinical use and physical examination mechanisms, the acquisition difficulty is small, the cost is low, the explanation degree is high, and the result obtained after the sample is calculated is more comprehensized.
Description
Technical Field
The invention relates to the technical field of blood detection, in particular to a method for calculating gender and age by blood biochemical indexes.
Background
Aging is an inevitable life stage of an individual, and is a continuous, dynamic and slow-occurring stage after the biological development and maturity, in the stage, the individual has hypofunction, and physiological structural components are gradually degenerated, and simultaneously a series of degenerative diseases such as senile hyperosteogeny, Alzheimer's disease (senile dementia), Parkinson's syndrome and the like are accompanied. The decline in organ function, changes in cellular and molecular levels, accompanied by aging progressively affect health, and thus aging is closely associated with disease and death. In recent years, research on aging of individuals has also become a popular research area in life sciences. The individual aging varies greatly, and the aging level and the aging speed of each system and organ of the same individual also vary, so that the calendar age is not the most reliable index of the reaction life, and the biological age is more accurate.
Large longitudinal programs like MARK-AGE have begun to study the relationship between the changes in various biomarkers during aging and actual AGE, and methylation-related markers, transcriptome-related metabolites, telomere length, immune cell number and response effect, etc. can be used as one of the criteria for measuring the AGE of an individual. The research team of the university of Edinburgh predicts the age of an individual according to the DNA methylation level in blood, and then compares the age with the real age of the individual, and the result shows that the methylation prediction age is 21 per thousand higher than that of the old aged over five years old in actual age; telomeres are ubiquitous at the end of chromosomes, so that chromosomes are protected from being degraded, chromosomes are prevented from being fused with one another, once the telomeres are exhausted, the chromosomes cannot normally divide, and the updating of cells is also finished, so that the length of the telomeres strongly points to the size of cell division potential, the shorter the telomeres, the lower the cell regeneration capacity, the longer the telomeres, the higher the cell regeneration capacity and the remaining division times are.
However, most of the indexes researched by the prior art lack the overall description of all organs or systems, and only the aging aspect is explained for a certain system or a certain layer of an individual; meanwhile, the indexes have high measurement difficulty and high cost; most importantly, the indexes obtained by the research are mostly selected from individuals with pathological characteristics and are not universal for all individuals, blood detection is the most common and simplest detection in the medical and health industries, and the blood indexes are diverse in individuals under natural conditions and sensitive to different physiological conditions (inflammation or drunkenness conditions and the like), so the blood detection is widely used clinically. Research shows that after an individual walks into the elderly, the number of red blood cells in blood is reduced by about 10 to 20 percent compared with that in young and strong years, and the hematocrit and the hemoglobin are both reduced; the white blood cells are also reduced along with the increase of the age, wherein the lymphocyte reduction is the most obvious, so that the immunity of the old is generally reduced, and the probability of infection, inflammation and tumor is increased; the blood indicators of the gradual decrease of the amount of albumin, the remarkable increase of the total amount of blood fat, the increase of the content of triglyceride, the increase of the content of cholesterol and the like are proved to be the markers of the aging of individuals.
The existing method for calculating the age of the individual only aims at a certain organ or tissue of the individual, lacks the overall description and explanation of the individual aging, cannot comprehensively reflect the aging condition of the individual, and the blood index relates to various omic metabolites, so that the aging condition of the individual can be more comprehensively described by taking the blood index as the method for calculating the age of the individual;
the method for measuring aging and individual age by using methylation-related markers, transcriptome-related metabolites, telomere length, immune cell number and response effect has the advantages of difficult acquisition of indexes to be detected, high cost, more common clinical use of blood detection and easier acquisition of indexes.
Disclosure of Invention
Aiming at the defects of the background technology, the invention provides the method for calculating the sex and the age of the blood biochemical index, which has the advantages of small difficulty in obtaining a sample, low cost and high data accuracy and solves the problems in the background technology.
The invention provides the following technical scheme: a method for calculating gender and age by blood biochemical indicators comprises the steps of blood biochemical indicator data collection, data preprocessing and model establishment, wherein after a sample is collected, the sample is subjected to data preprocessing and then is calculated by a DNN (digital noise network) model to obtain a result, and the gender and age of the blood biochemical indicators are calculated by the following method:
the first step, collecting raw data, and collecting 92062 samples in total, wherein each sample contains age, sex and 19 blood biochemical indicators, and the selected indicators are commonly found in blood routine and blood biochemical indicator detection reports of hospitals and physical examination institutions;
secondly, preprocessing data, namely removing samples with missing data and samples with obvious error outliers (outlear), obtaining 26754 total samples for training and testing two models, then carrying out standardization processing on 19 blood biochemical indexes, and mapping the numerical values of all indexes in the range of 0, 1;
and thirdly, establishing and evaluating a model, wherein 19 items of blood biochemical indexes of the sample to be detected are directly transmitted into the model when the model is used for calculation, and the calculated age and sex are obtained after DNN model calculation.
Preferably, the 19 biochemical indicators of blood include albumin, glucose, urea, cholesterol, total protein, serum sodium, creatinine, hemoglobin, total bilirubin, triglycerides, high density lipoprotein cholesterol, low density lipoprotein cholesterol, serum calcium, serum potassium, hematocrit, mean red blood cell hemoglobin concentration, mean red blood cell volume, platelet count, and red blood cell count.
Preferably, the age calculation uses a DNN regression algorithm and the gender calculation uses a DNN classification algorithm.
Preferably, the DNN model is mainly composed of an input layer, a hidden layer, and an output layer, each layer has a plurality of neurons, the input layer neurons correspond to the number of input variables, and the output layer neurons correspond to the number of output result variables.
The invention has the following beneficial effects:
the method for calculating the sex and the age of the blood biochemical index increases the comprehensiveness of measurement by taking the blood index as a measurement object, meanwhile, the blood index contains multi-group chemical metabolites, can comprehensively reflect the individual aging condition and explain the aging level, and calculates the blood biochemical index through a DNN model, so that the whole process is more convenient, the blood biochemical index can be transmitted into the DNN model to obtain a test result, and various parameters and model structures in the calculation model are verified for multiple times to ensure the accuracy of the calculation result.
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FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a schematic structural diagram of a deep neural network DNN algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, a method for calculating gender and age of blood biochemical indicators includes collecting blood biochemical indicator data, preprocessing the data, building a model, preprocessing the collected sample, dividing the calculation method into two parts of gender calculation and age calculation, each part includes training a calculation model and using a model, training us of the model uses DNN algorithm, which is verified to be significantly higher than other machine learning algorithms (k-nearest neighbor algorithm, random forest, linear regression, support vector machine, etc.) in accuracy and interpretability of the result, hidden layer and neurons are introduced into the DNN algorithm, the expression capability of the model is enhanced, and the characteristic of DNN in the aspect of automatically scaling neurons also enriches the development direction of the model to the maximum extent, and then the result is obtained after DNN model calculation, the blood biochemical index is used for calculating the sex and the age according to the following methods:
the first step, collecting raw data, and collecting 92062 samples in total, wherein each sample contains age, sex and 19 blood biochemical indicators, and the selected indicators are commonly found in blood routine and blood biochemical indicator detection reports of hospitals and physical examination institutions;
secondly, preprocessing data, namely after removing samples with missing data and samples with obvious wrong outliers (outlear), obtaining 26754 total samples for training and testing two models, then carrying out standardization processing on 19 blood biochemical indexes, mapping numerical values of all indexes in a range of 0 and 1, avoiding the problem that the model convergence is slow due to different iteration speeds of the model in different dimensions, and improving the convergence speed of the model;
and thirdly, establishing and evaluating a model, wherein 19 items of blood biochemical indexes of the sample to be detected are directly transmitted into the model when the model is used for calculation, and the calculated age and sex are obtained after DNN model calculation.
Wherein the 19 biochemical indicators of blood include albumin, glucose, urea, cholesterol, total protein, serum sodium, creatinine, hemoglobin, total bilirubin, triglycerides, high density lipoprotein cholesterol, low density lipoprotein cholesterol, serum calcium, serum potassium, hematocrit, mean red blood cell hemoglobin concentration, mean red blood cell volume, platelet count, and red blood cell count.
The age calculation uses a DNN regression algorithm, and the gender calculation uses a DNN classification algorithm, so that the age and the gender in the blood sample can be distinguished conveniently, and the test result is more accurate.
The DNN model mainly comprises an input layer, a hidden layer and an output layer, wherein a plurality of neurons are arranged in each layer, the number of neurons in the input layer corresponds to the number of input variables, and the number of neurons in the output layer corresponds to the number of variables in output results, so that in the measurement of a sample, the measurement is obtained through the neuron analysis of the input layer.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A method for calculating sex and age by blood biochemical indexes is characterized in that: the method comprises the steps of blood biochemical index data collection, data pretreatment and model establishment, wherein the sample is subjected to data pretreatment after being collected, and then a result is obtained after DNN model calculation, and the gender and the age of the blood biochemical index are calculated according to the following methods:
the first step, collecting raw data, and collecting 92062 samples in total, wherein each sample contains age, sex and 19 blood biochemical indicators, and the selected indicators are commonly found in blood routine and blood biochemical indicator detection reports of hospitals and physical examination institutions;
secondly, preprocessing data, namely removing samples with missing data and samples with obvious error outliers (outlear), obtaining 26754 total samples for training and testing two models, then carrying out standardization processing on 19 blood biochemical indexes, and mapping the numerical values of all indexes in the range of 0, 1;
and thirdly, establishing and evaluating a model, wherein 19 items of blood biochemical indexes of the sample to be detected are directly transmitted into the model when the model is used for calculation, and the calculated age and sex are obtained after DNN model calculation.
2. The method of claim 1, wherein the method comprises the steps of: the 19 biochemical indicators of blood comprise albumin, glucose, urea, cholesterol, total protein, serum sodium, creatinine, hemoglobin, total bilirubin, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, serum calcium, serum potassium, hematocrit, mean red blood cell hemoglobin concentration, mean red blood cell volume, platelet count and red blood cell count.
3. The method of claim 1, wherein the method comprises the steps of: the age calculation uses the DNN regression algorithm and the gender calculation uses the DNN classification algorithm.
4. The method of claim 1, wherein the method comprises the steps of: the DNN model mainly comprises an input layer, a hidden layer and an output layer, wherein a plurality of neurons are arranged in each layer, the neurons of the input layer correspond to the number of input variables, and the neurons of the output layer correspond to the number of output result variables.
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| CN112462073A (en) * | 2020-11-03 | 2021-03-09 | 佛山市第一人民医院（中山大学附属佛山医院） | Data processing method and system for hematology index |
| CN112568872A (en) * | 2020-12-30 | 2021-03-30 | 深圳大学 | Brain age fusion prediction method based on MRI (magnetic resonance imaging) image and blood biochemical indexes |
| CN113380327A (en) * | 2021-03-15 | 2021-09-10 | 浙江大学 | Human biological age prediction and human aging degree evaluation method based on whole peripheral blood transcriptome |
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