CN115050466A

CN115050466A - Accurate diagnosis and treatment system for traumatic brain injury based on combined monitoring of multiple biomarkers

Info

Publication number: CN115050466A
Application number: CN202210534273.9A
Authority: CN
Inventors: 高嵘
Original assignee: ZHANGJIAGANG FIRST PEOPLE'S HOSPITAL
Current assignee: ZHANGJIAGANG FIRST PEOPLE'S HOSPITAL
Priority date: 2022-05-17
Filing date: 2022-05-17
Publication date: 2022-09-13

Abstract

The invention discloses an accurate diagnosis and treatment system for traumatic brain injury by combined monitoring of multiple biomarkers, which combines different specificities of TB I biomarkers, classifies the expression symptoms according to different categories and medical clinical expression data thereof, distributes corresponding weights, sends the data to a feature database for comparison after the biomarker data is extracted, obtains the related medical data of the corresponding markers through feature data matching, divides the categories into categories such as directly diagnosing related category markers and prognosis related markers according to different marker specificities and expressions, distributes different category attributes and weights for different markers, combines the categories and the weights thereof, and takes the processed maximum value or at least the categories to which the first N maximum values belong as diagnosis conclusion data and guidance data, wherein N is a natural number which is more than 1; when auxiliary data exists, the data is reclassified and the weight is distributed again. The invention can accurately and effectively diagnose and prognose the brain injury degree.

Description

Accurate diagnosis and treatment system for traumatic brain injury based on combined monitoring of multiple biomarkers

Technical Field

The invention relates to the biomedical field of brain injury monitoring and diagnosis and the like, and discloses an accurate diagnosis and treatment system for traumatic brain injury based on the practical clinical significance and measurable means of a brain injury marker in brain injury diagnosis.

Background

Traumatic Brain Injury (TBI) is the most common acquired brain injury, and about 43% of TBI survivors are also accompanied by chronic disability, have high lethality and disability rate, are nervous system diseases caused by structural damage or dysfunction of brain tissues, seriously affect the quality of life and life safety of patients, are worldwide health problems, and are one of the main causes of death and disability of contemporary adults. Currently, clinical diagnosis and evaluation of brain injury are mainly based on clinical manifestations, electroencephalogram (EEG), imaging techniques, and other means, but these examinations have certain limitations and hysteresis, which results in many patients with brain injury missing the optimal treatment time window, so many researchers have focused on finding a brain injury humoral biomarker, i.e., a brain injury marker or brain biomarker, (which is a measurable and indicative index appearing in blood or cerebrospinal fluid of patients with brain tissue injury or with a history of cerebrovascular disease), expecting to detect brain injury with a method with small injury, low risk, and easy operation, discussing the pathogenesis of brain injury, finding new treatment targets, early diagnosis, evaluating the therapeutic effect, and determining prognosis.

At present, the main means for clinically diagnosing TBI are imaging detection methods, such as Magnetic Resonance Imaging (MRI), craniocerebral CT and other detection means, and international Glasgow Coma Scale (GCS) method. However, the traditional CT and MRI imaging detection methods are poor in timeliness, only can statically represent data in a fixed time, only can achieve instantaneous and accurate monitoring, cannot dynamically or perform detection with high frequency density, have certain limitations and hysteresis, cause many brain injury patients to miss optimal treatment window periods, cannot play an effective guidance role in monitoring mild brain injuries and disease conditions, and particularly have blind areas during the course of treatment. In recent years, with the research of disease biomarkers and the progress of liquid biopsy technology, related TBI injury biomarkers gradually move to clinic, and the expression of molecular formulas of various markers is different, so that an important auxiliary means is provided for early diagnosis, dynamic monitoring and prognosis evaluation of TBI. A series of existing protein or lipid molecules are directly related to TBI prognosis as feasible biomarkers of TBI brain injury, but predictive biomarkers are more important for TBI, especially for early brain injury diagnosis and prediction, sensitivity and specificity of the markers are more advantageous than imaging detection, and damage degree can be more effectively diagnosed and predicted according to external molecular expression by combining change and rule of the biomarkers, so that the defects of an imaging detection method are made up, and new basis and clues are provided for TBI treatment.

The injury mechanism caused by TBI is complex, and secondary injury of organisms can be caused by pathophysiological processes such as neurotransmitter release, free radical generation, calcium-mediated injury, gene activation, mitochondrial dysfunction, inflammatory reaction, blood coagulation dysfunction and the like after injury, and TBI adverse fatigues are often caused. Therefore, how to diagnose TBI early, quickly and accurately plays an important role in guiding clinical treatment and judging prognosis.

At present, the diagnostic value of the protein is more definite, and the protein comprises S100B protein in serum, hypersensitive C reactive protein, neuron-specific enolase, marrow basic protein, Ubiquitin carboxyl terminal hydrolase L1(Ubiquitin carboxyl terminal hydrolases L1, UCH-L1), Neurofilament protein (Neurofament, NF), Glial Fibrillary Acidic Protein (GFAP), nerve-specific enolase (NSE), Brain-derived neurotrophic factor (BDNF), alpha II spectrin, microtubule-associated protein 2(MAP2), interleukin-6 (IL-6) and the like. Among them, S100B protein, hypersensitive C reactive protein in serum (hsCRP), neuron-specific enolase NSE in serum (in brain tissue and spinal cord cells), myelin basic protein MBP, MicroRNA (MicroRNA, miRNA), and the like. New brain injury markers are reported in succession, but currently, the number of the markers which can be conventionally used in clinical practice is very small, on one hand, because the research is in an experimental stage, the examination of large-sample clinical experiments is lacked, the reasons of brain injury are many and complicated, meanwhile, the clinical performance of each index is different, and the specificity of a single index is not high; on the other hand, the method cannot be well applied due to the limitation of clinical detection means and the like.

Disclosure of Invention

In order to overcome the defects, the invention aims to provide a system for accurately and effectively diagnosing and prognosing the degree of brain injury, and monitoring multiple biomarkers in a traumatic brain injury precise diagnosis and treatment manner by combining the multiple biomarkers for providing a new strategy for diagnosis and prognosis of related diseases.

In order to achieve the above purposes, the invention adopts the technical scheme that: a multiple biomarker combined monitoring traumatic brain injury accurate diagnosis and treatment system is disclosed, wherein different TBI biomarker specificities are combined, expression symptoms are classified according to different categories and medical clinical expression data thereof, corresponding weights are distributed, after biomarker data are extracted, the data are sent to a characteristic database for comparison, relevant medical data of corresponding markers are obtained through characteristic data matching, the categories are divided into categories such as directly diagnosing relevant category markers and prognosis relevant marker categories according to different marker specificities and expressions, different category attributes and weights are distributed for different markers, when data processing is carried out, the processed maximum value or at least the categories of the first N maximum values are taken as diagnosis conclusion data and prognosis guidance data for taking out values corresponding to relative reasonable maximum value correlation algorithms in a data community, n is a natural number > 1; where auxiliary data or other available parametric data sources are available, they are reclassified and assigned weights.

Preferably, the auxiliary data comprises medical history reference data, CT data or MRI detection data, routine medical diagnostic data.

Preferably, the data processing includes specific feature extraction and data preprocessing, the specific feature extraction is mainly embodied in a feature database and is used for completing feature matching comparison analysis on input TBI biomarker data (the input form of the marker data can be pictures and direct data entry forms, and can also be other forms), and the data preprocessing and data analysis are realized by a computer or an algorithm server; the TBI biomarker comprises neurofibrillary protein NF and S100B protein.

Preferably, the specific feature extraction and data preprocessing include establishing a corresponding medical feature database, assigning different categories and weights to the expression of each type of marker, and dynamically calculating and extracting the data expression of the marker according to all the categories of the biomarker adopted by the system to realize the specific feature extraction and data preprocessing.

Preferably, the marker classification is divided into a plurality of levels of degree analysis, including marker classification data, concentration gradient classification data, feature molecular formula expression weight assignment, temporal parameter data, potential marker processing data, other data classification and weight assignment; the probability of a certain condition or the direct directionality of the diagnosis is characterized according to the weights and the gradient table. The classification of markers is to facilitate accurate diagnosis, and because of their specificity, their molecular expression should often correspond to one or several conditions (or brain damage degree and long-term response and prognosis performance), it is necessary to classify markers. It can be limited to 6 number grades, or divided into three grades such as light, medium and heavy according to actual application, or divided into 10 degrees according to different applications.

The invention has the further improvement that a mode of taking the monitoring data of the current round as the main and taking the monitoring data of the previous round as the auxiliary is adopted, and a mode of dynamic data analysis is adopted, wherein the dynamic data comprises historical data and instant data. Providing scientific and effective monitoring data for prognosis, and obtaining more accurate analysis results and medical reference data.

As a further improvement of the invention, the biomarker data is combined with relative characterization data of conventional examination symptoms and relative characterization data of medical history symptoms to analyze pathological characteristics, and then the diagnosis result and prognostic analysis guidance data about the brain injury degree are obtained through data analysis of a computer or a computer server after feature database matching and weight assignment. By the aid of other effective external data, more effective data analysis can be formed on the brain injury degree and prognosis evaluation more effectively and reliably.

Preferably, the content of the relative characterization data of the routine examination condition is a reference index for a routine diagnosis that is medically available at the time of brain injury detection. Including the detection to patient's eyes, the unusual sensitive and pupil size of light is different, breathing frequency degree of urgency, both hands perception is unusual, crooked mouth angle, conventional detection data such as image data, because the classification through the detection is more with embodying, be the data that can't express with accurate figure for some, like degree of urgency, when judging, can be handled the degree of urgency for a numerical value, can be with breathing 5 ~ 15 certainly will slowly, 20 ~ 30 definitions are moderate, 30 ~ 40 location are processing modes such as urgency, here, this application is not listed in a list.

Preferably, the relative characterization data of the medical history condition comprises influence detection data such as CT, injury passing, patient consciousness expression and self description data.

Preferably, the input form of the TBI biomarker data comprises a picture, direct entry.

Preferably, the biomarkers are classified according to application scenarios and complexity of operability: the serum related biomarkers are applied to non-complex equipment and are convenient for sampling scenes, and the markers need to be extracted and analyzed; the exosome biomarker adopts an application scene of a non-invasive detection method and is used for simply detecting and continuously measuring the neurogenic egg biomarker; the neurogenic egg biomarker is used for detecting related biomarkers existing in hydrocephalus liquid such as brains, spinal cords and the like and neurons during operation, preoperative or precise diagnosis.

The system for accurately diagnosing and treating traumatic brain injury by jointly monitoring multiple biomarkers has the advantages that different TBI biomarkers are combined, expression diseases are classified according to different categories and medical clinical expression data, corresponding weights are distributed, and data processing (realized by a computer or a server) is performed through the multiple-weight marker data, so that more accurate and effective data on injury degrees and categories are obtained, and accurate diagnosis and prognosis evaluation are realized.

Drawings

FIG. 1 is a schematic diagram of the application of the combined monitoring of multiple biomarkers in the accurate diagnosis and prognosis evaluation of traumatic brain injury;

FIG. 2 is a schematic diagram of the extraction of specific features of various biomarkers and the data preprocessing in this embodiment;

FIG. 3 is a diagram illustrating an implementation of dynamic weights according to this embodiment;

FIG. 4 is a graph of the time correlation of diagnostic and prognostic data for different biomarkers and over a continuous period of time for the present example;

FIG. 5 is a schematic diagram of the integrated process of the present embodiment combining the general examination data, the medical history data and the marker data.

Detailed Description

The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, and the scope of the present invention can be clearly and clearly defined.

Referring to fig. 1-5, in the embodiment, a combination monitoring of multiple biomarkers is described in relation to the implementation mechanism involved in the precision diagnosis and treatment system for traumatic brain injury:

1) application of multiple biomarker combined monitoring in accurate diagnosis and prognosis evaluation of traumatic brain injury

According to different TBI biomarker specificities, the expression symptoms are classified according to different categories and medical clinical expression data thereof, corresponding weights are distributed, and data processing (realized by a computer or a server) is performed through multiple weight marker data, so that more accurate and effective data on damage degree and category are obtained, accurate diagnosis and prognosis evaluation are realized, and the implementation mechanism is shown in FIG. 1.

In fig. 1, TBI _ N-valued different brain injury biomarkers on the shift table, such as biomarkers of neurofibrillary protein NF, S100B protein, after extracting biomarker data, the data are sent to a feature database for comparison, related medical data corresponding to the markers are obtained through feature data matching, and are classified into categories such as directly diagnosis related marker categories and prognosis related marker categories according to different specificity and expression of the markers, different category attributes and weights are assigned to different markers, and when data processing is performed, the category and the weight thereof are combined, and the processed maximum value or at least the category to which the first N (e.g., N ═ 3) maximum values belong is taken as diagnosis conclusion data, prognosis guidance data, and the like. Where auxiliary data or other available parametric data sources are available, they are reclassified and assigned weights. Wherein the auxiliary data comprises medical history reference data, CT or MRI detection data, and conventional medical diagnosis data. The data processing mainly comprises specific feature extraction and data preprocessing, the specific feature extraction is mainly embodied in a feature database and used for completing feature matching comparison analysis of input TBI biomarker data, and the data preprocessing and data analysis are realized by a computer or an algorithm server.

2) Extraction of multiple biological mark specific characteristics and data preprocessing

In an application system for accurately diagnosing traumatic brain injury and performing prognosis evaluation on multiple biomarkers in a combined monitoring manner, a corresponding medical feature database is established firstly, then different categories and weights are distributed for expression of various markers, and finally data expression of the markers is dynamically calculated and extracted according to all categories of the biomarkers adopted by the system, so that specific feature extraction and data preprocessing are realized.

The specific feature extraction and data preprocessing flow related to the system is shown in fig. 2, wherein the data such as the category and the weight thereof are matched from the feature database according to the basic data and the molecular expression specificity of each biomarker (the specific molecular expression specificity of the marker is pre-recorded in the feature database), the matching category is extracted by the medical expression specificity of the marker itself and the medical indexes and the weights thereof matched or corresponding to the feature database, so that the specificity of the marker and the medical clinical expression thereof are effectively and organically combined, and parameter data are provided for the real-time processing, diagnosis and prognosis guidance of the subsequent data. The TBI biomarker input form may be a picture and direct data entry form as well as other forms.

In addition, when the feature database is matched, the weight is dynamic, namely the weight is normalized according to the total number of the selected markers and the types of the selected markers, so that effective data evaluation can be realized in the use process of the feature data of the biomarkers with different numbers and types.

The markers are classified for the convenience of accurate diagnosis, and due to the specificity of the markers, the molecular expression of the markers usually corresponds to one or more symptoms (or brain injury degree and long-term response and prognosis performance), so that the markers need to be classified; degree gradient analysis, mainly aiming at the degree of injury or illness, making a rough dictionary classification, generally speaking, the more the gradient is, the more accurate the gradient is, but because of the difference of individuals (patients), the gradient classification is generally divided into 6 grades, and finally, the probability of a certain disease condition or the direct diagnosis directivity is represented according to the weight and the gradient table; the weight assignment refers to dividing the marker into two different weights according to the specificity of the marker, wherein the two weights respectively correspond to the damage weight and the prognosis weight (mainly classifying and assigning according to the specificity of the marker), and when the weight assignment is carried out, the weight assignment is carried out by parameters in a characteristic database and necessary detection categories; the setting of the time parameters is mainly based on prognosis and timeliness diagnosis, and because the response action time of the marker has a certain relation with the disease characteristics and the extraction time of the marker data, the corresponding time parameters are allocated to the input marker data; the data processing of the potential marker mainly follows that the initial value may be 0 or a fixed reference value, such as 0.05, and then a new weight ratio is obtained through data iterative analysis in the comparison analysis of the actual diagnosis result (which is also beneficial to the detection and experiment of the novel potential marker); other data classification and weight assignment give a certain proportion of weight relationship to the diagnosis direction according to the medical history (such as patient description, history medical history, imaging detection results, routine examination results and the like), thereby being beneficial to data synthesis and analysis after multi-marker combined monitoring and obtaining more definite and effective diagnosis result prognosis analysis.

In the implementation process of the application system for accurate diagnosis and prognosis evaluation of traumatic brain injury, as shown in fig. 3, in the combined monitoring of multiple biomarkers, due to the introduction of new biomarkers (or potential biomarkers that have not been completely verified and reported at present), the feature database should be kept updated first, and in practical applications, since the types (e.g., the biomarkers existing in serum, the biomarkers existing in spinal cord, and the biomarkers existing in body fluid) and the number of the adopted biomarkers are different, the lateral weighted points of the molecular expression of the reference biomarkers are different from the purpose, and thus the weights represented by the markers are different for accurate diagnosis and the prognosis analysis of the corresponding disease. In order to solve the problem, different test contents and the used reference markers can be dynamically weight-distributed in a dynamic weight adjustment mode, so that accurate diagnosis and effective prognosis evaluation analysis of brain injury are realized. This is illustrated by way of example in FIG. 3:

the original system adopts NF and Tau as biomarkers for judging the mild and severe conditions of the diseases of sa and sb (sa and sb respectively represent two types of brain diseases), and priority _ sa is respectively given according to the accuracy or positive correlation of the biomarkers on the two types of diseases according to the historical data or literature reference data clinically verified by the biomarkers, wherein the priority _ sb has two weights aiming at different diseases; when a new marker for BBB damage detection such as S100B is added to the system on the basis of the original system for new testing, the weights for two different diseases of sa and sb are changed, and the weights are assigned again mainly according to the specificity of different biomarkers and clinical or reported data (verified) of the biomarkers, so that a new dynamic weight adjustment system for the marker data is obtained, wherein the system is dynamically adjusted from the weights p10 and p20 to p12 and p22. The dynamic weight adjustment presupposes that the feature database is added with the biomarker feature data to be used.

3) Application for dynamically and effectively monitoring data and evaluating prognosis in real time

The dynamic real-time nature of monitoring is where the biomarkers are most sensitive and prominent, unlike conventional imaging. In the dynamic real-time effective data monitoring and application process, the invention adopts a mode of taking the current round of monitoring data as a main part and taking historical (previous round of monitoring) data as an auxiliary part and a mode of analyzing dynamic data (combining historical data and instant data), particularly provides scientific and effective monitoring data for prognosis and obtains more accurate analysis results and medical reference data.

The implementation mechanism is shown in fig. 4, because the expressions of different biomarkers have direct correlation with time, when accurate diagnosis and prognosis analysis are performed, comprehensive judgment is performed according to the characteristics of the biomarkers and time parameters, so that accurate and effective diagnosis and analysis data can be obtained, and accurate and effective analysis results can be obtained according to the concentration quantity relationship of the biomarkers and the probability, the direct ratio and the like of mapped diseases. Especially in early diagnosis and prognosis analysis, the biomarker and continuous data measurement directly influence the data analysis result, so that under the condition of condition, the continuous measurement with stronger real-time effect is carried out as far as possible, and the analysis is carried out according to the variation of the measured data, thereby providing scientific data support for the prognosis evaluation in the early diagnosis and treatment process, effectively controlling the deterioration of the disease condition and reducing the risk.

4) Data comprehensive processing method combining relative characterization data of general examination symptoms, relative characterization data of medical history symptoms and relative characterization data of marker medical history symptoms

The invention not only can be combined with a method for monitoring multiple biomarkers in a combined manner to carry out accurate diagnosis and prognosis evaluation on brain injury, but also can be combined with relative characterization data of conventional examination diseases to provide more accurate and effective reference data for diagnosis and prognosis. The method can be developed, for example, diagnosis and prognosis data prediction by combining relative characterization data of the disease history are adopted, so that pathological characteristics can be analyzed more accurately and effectively, and more accurate and effective analysis results and data prediction are given. In summary, with the assistance of other effective external data, a more effective data analysis can be formed for the brain injury degree and prognosis evaluation more effectively and reliably, and the implementation mechanism is shown in fig. 5.

The relative characteristic data of the conventional examination symptoms comprise the characteristics of detection of eyes of a patient, abnormal sensitivity to light, different sizes of pupils, breathing frequency and urgency degree, abnormal perception of both hands, facial distortion and the like; the imaging data and medical history include CT and other influence detection data, injury process, patient consciousness expression, self description and the like; the diagnosis result about the brain injury degree, prognostic analysis guide data and the like are obtained by combining biomarker data, relative characterization data of routine examination diseases, imaging data, relative characterization data of medical history diseases and the like, matching through a characteristic database, distributing weights, and analyzing data through a computer or a calculation server.

The diagnosis method and the prognosis evaluation application system based on the combined monitoring of the multiple biomarkers in the accurate diagnosis and treatment process of the traumatic brain injury are not limited to accurate diagnosis and prognosis of the traumatic brain injury, and can be generally used for diagnosis and prognosis analysis of most brain diseases.

The research and implementation described in the present invention can also be used for clinical and routine tests by classifying biomarkers, such as by extracting relevant biomarkers from serum relative to the obtained serum data, and the biomarkers are classified according to different application scenarios and the complexity of operability: serum class-related biomarkers, exosome biomarkers, neuroglobin biomarkers. The serum related biomarkers are applied to non-complex equipment and convenient sampling scenes, the markers need to be extracted and analyzed, and the exosome biomarkers adopt the application scenes of a non-invasive detection method and are used for simple detection and continuous measurement of the neurogenic egg biomarkers; the neurogenic egg biomarker is used for detecting related biomarkers existing in hydrocephalus liquid such as brains, spinal cords and the like and neurons during operation, preoperative or precise diagnosis. Biomarkers closely related to the prognosis of TBI patients, such as D-dimer, thrombospondin-1 (TSP-1) and plasma signal peptide-CUB-epidermal growth factor domain-containing protein 1(signal peptide-CUB-EGF domain-associating protein 1, SCUBE1), serum Myelin Basic Protein (MBP), P-Tau concentration in blood and P-Tau/T-Tau ratio, and potential markers such as GFAP; or a series of biomarkers such as Neurofibrillarin (NF) extracted from spinal cord or hydrocephalus, neurons, alphaII spectrin and the like; the method for analyzing and applying the data of the classified markers can be suitable for different occasions and human groups, particularly the combination of the biomarkers existing in serum and body fluid is more beneficial to the continuous monitoring of the data, the effective prognosis evaluation is realized, and the effects of long-term prevention, simplicity and convenience in monitoring can be realized.

The above embodiments are merely illustrative of the technical concept and features of the present invention, and the present invention is not limited thereto, and any equivalent changes or modifications made according to the spirit of the present invention should be included in the scope of the present invention.

Claims

1. A system for accurately diagnosing and treating traumatic brain injury by jointly monitoring multiple biomarkers is characterized in that: combining different specificities of TBI biomarkers, classifying the expression symptoms according to different classes and medical clinical expression data thereof, distributing corresponding weights, sending the data to a feature database for comparison after extracting the biomarker data, obtaining relevant medical data of corresponding markers through feature data matching, dividing the data into classes such as directly diagnosing relevant classes of markers and prognostically relevant markers according to different specificity and expression of the markers, distributing different class attributes and weights to the different markers, combining the classes and weights thereof when processing the data, and taking the processed maximum value or the classes of at least the first N maximum values as diagnosis conclusion data and prognosis guidance data, wherein N is a natural number greater than 1; when auxiliary data is available as a source of available parameter data, the data is reclassified and assigned with weights.

2. The system for the accurate diagnosis and treatment of traumatic brain injury by combined monitoring of multiple biomarkers according to claim 1, wherein: the auxiliary data includes medical history reference data, CT data or MRI detection data, and conventional medical diagnosis data.

3. The system for the accurate diagnosis and treatment of traumatic brain injury based on the combined monitoring of multiple biomarkers according to claim 1, wherein: the data processing comprises specific feature extraction and data preprocessing, the specific feature extraction is mainly embodied in a feature database and is used for completing feature matching comparison analysis of the input TBI biomarker data, and the data preprocessing and the data analysis are realized by a computer or an algorithm server.

4. The system for the accurate diagnosis and treatment of traumatic brain injury based on the combined monitoring of multiple biomarkers according to claim 3, wherein: the specific feature extraction and data preprocessing comprises the steps of firstly establishing a corresponding medical feature database, then distributing different categories and weights for the expression of various markers, and finally dynamically calculating and extracting the data expression of the markers according to all the categories of the biomarkers adopted by the system to realize the specific feature extraction and data preprocessing.

5. The system for the accurate diagnosis and treatment of traumatic brain injury based on the combined monitoring of multiple biomarkers according to claim 1, wherein: the degree analysis of the classification and division of the marker into a plurality of grades comprises marker classification data, concentration gradient classification data, feature molecular formula expression weight assignment, time parameter data, potential marker processing data and weight assignment; the probability of a certain condition or the direct directionality of the diagnosis is characterized according to the weights and the gradient table.

6. The system for the accurate diagnosis and treatment of traumatic brain injury based on the combined monitoring of multiple biomarkers according to claim 1, wherein: the relative characterization data of the biomarker disease history need to be combined with the relative characterization data of the general examination disease and the relative characterization data of the disease history to analyze pathological characteristics, and then the diagnosis result and prognostic analysis guide data about the brain injury degree are obtained through data analysis of a computer or a calculation server after feature database matching and weight distribution.

7. The system for the accurate diagnosis and treatment of traumatic brain injury based on the combined monitoring of multiple biomarkers according to claim 6, wherein: the content of the relative characterization data of the routine examination condition is a reference index for routine diagnosis that is medically available at the time of brain injury detection.

8. The system for the accurate diagnosis and treatment of traumatic brain injury based on the combined monitoring of multiple biomarkers according to claim 6, wherein: the relative characterization data of the medical history symptoms refer to the relative characterization data corresponding to symptoms in the corresponding system, and comprise influence detection data such as CT (computed tomography) and the like, injury passing, consciousness expression of patients and self description data.

9. The system for the accurate diagnosis and treatment of traumatic brain injury based on the combined monitoring of multiple biomarkers according to claim 1, wherein: the biomarkers are classified according to different application scenarios and complexity of operability: serum-related biomarkers, exosome biomarkers, neurogenic protein biomarkers.