CN111341452B - Multisystem atrophy disability prediction method, model building method, device and equipment - Google Patents
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Abstract
The invention relates to a multi-system atrophy disability prediction method, a model building method, a device and equipment, and belongs to the technical field of disease prediction.
Description
Technical Field
The invention belongs to the technical field of disease prediction, and particularly relates to a multi-system atrophy disability prediction method, a model building device and equipment.
Background
Multiple system atrophy (multiple system atrophy, MSA) is a rare, progressive neurodegenerative disease characterized by a diverse combination of parkinsonism, cerebellar ataxia, and autonomic dysfunction. The average median time from onset to walking for assistance, wheelchair sitting, bed rest, and death was 3,5,8, and 9 years, respectively. Due to low morbidity, the disease has been listed in the rare disease list by country. The disease has hidden onset, rapid progress and short life, and brings great burden to patients and families thereof and even the whole society.
In the past, the prognosis of patients with multiple system atrophy is studied, and death is mainly used as a clinical outcome index to construct a death prediction model. The research shows that factors such as onset of the autonomic nerve symptoms, late onset age, frequent falling and the like are closely related to prognosis.
However, in the prior art, there is no prediction of failure in patients with multiple system atrophy. Because the disease of patients with multiple system atrophy progresses rapidly, the patients gradually lose mobility in 4-5 years of the disease course and are limited to wheelchairs, lose self-care ability of life, and seriously affect the quality of life. Therefore, it is important to build a multi-system atrophy disability prediction model, which can guide a clinician to perform individual accurate treatment on a patient, improve the life quality of the patient and improve the prognosis of the patient.
Disclosure of Invention
In order to at least solve the above problems in the prior art, the present invention provides a multi-system atrophy disability prediction method, a model building method, a device and equipment.
The technical scheme provided by the invention is as follows:
in one aspect, a method for constructing a multisystem atrophy disability prediction model includes:
acquiring basic data of a patient suffering from multiple system atrophy over a preset time period, wherein the basic data comprises: clinical index data and hematology index data;
processing the clinical index data and the hematology index data based on a preset processing rule to obtain a target data set;
and constructing a multi-system atrophy disabling prediction model by using a kernlab program package in an R program according to the target data set, a support vector machine algorithm and a preset linear kernel function.
Optionally, the processing the clinical index data and the hematology index data based on a preset processing rule to obtain a target data set includes:
scoring patients within the patient population using a rapid eye movement sleep disorder assessment scale;
a unified multisystem atrophy assessment scale is adopted to assess the movement symptoms of the patient;
based on preset orthostatic hypotension evaluation rules, whether the patient has orthostatic hypotension or not is evaluated.
Optionally, the clinical index includes: age, sex, age of onset, delay of diagnosis, course of illness, body mass index, type of diagnosis, form of first symptoms, whether there is repeated falls within a preset period of time, whether there is pyramidal signs, whether there is wheezing, whether there is severe snoring, whether there is rapid eye movement sleep disorder, the first part score of the unified multisystem atrophy assessment scale, the second part score of the unified multisystem atrophy assessment scale, the fourth part score of the unified multisystem atrophy assessment scale, the total score of the unified multisystem atrophy assessment scale, whether there is orthostatic hypotension;
hematology indices include: red blood cell count, hemoglobin, hematocrit, average red blood cell volume, average red blood cell hemoglobin content, average red blood cell hemoglobin concentration, red blood cell distribution width CV, red blood cell distribution width SD, platelet count, white blood cell count, neutrophil fraction, lymphocyte fraction, monocyte fraction, eosinophil fraction, basophil fraction, total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, aspartate aminotransferase/aspartate aminotransferase ratio, total protein, albumin, globulin, white ball fraction, glucose, urea, creatinine, serum cystatin C, uric acid, triglycerides, cholesterol, high density lipoprotein, low density lipoprotein, alkaline phosphatase, glutamyl transpeptidase, creatine kinase, lactate dehydrogenase, hydroxybutyrate dehydrogenase.
Optionally, the processing the clinical index data and the hematology index data based on a preset processing rule to obtain a target data set includes:
assigning a patient gender according to gender, male=1, female=0;
obtaining a body mass index of the patient, body mass index = square of body weight/height;
assigning a diagnosis type to the patient according to the diagnosis type, wherein parkinson's disease is main type=1, and cerebellar ataxia is main type=0; the form of the first symptoms is divided into onset of autonomic dysfunction=0; motor symptom onset = 1; recurrent falls within 3 years = 1, no =0; there is pyramidal sign = 1, no = 0; wheezing=1, no=0; there was severe snoring = 1, no = 0; there was a rapid eye movement sleep disorder = 1, no = 0; there was orthostatic hypotension=1, and there was no=0.
Optionally, the method for constructing the preset linear kernel function includes:
constructing a kernel function: k (xi, xj) =Φ (xi) ·Φ (xj);
based on the relaxation variables, creating soft intervals, and acquiring constraint conditions of a classification plane as follows: y is i (wx i +b)≥1，i＝1,2,…,N；
Acquiring a soft boundary objective function:
ξ i ≥0，i＝1,2,…,N；
y i (wx i +b)≥1-ξ i ，ξ i ≥0；
wherein C is an error penalty factor; ζ is a relaxation variable.
Optionally, the error penalty factor C of the support vector machine is selected in the range of 5-8.
Optionally, the method further comprises: the accuracy of the predictive model is evaluated based on Kappa statistics.
In yet another aspect, a method of predicting multiple system atrophy disability, comprises:
acquiring basic data of a target multisystem atrophy patient, the basic data comprising: clinical index data and hematology index data;
processing the target data based on a preset processing rule to acquire target data;
predicting the disability condition of the target multi-system atrophy patient according to the target data and a model established by the multi-system atrophy disability prediction model establishing method.
In yet another aspect, a multi-system atrophy disability prediction model construction device includes: the device comprises an acquisition module, a processing module and a construction module;
the acquisition module is used for acquiring basic data of a multi-system atrophy patient with a disease course in a preset time period, and the basic data comprises: clinical index data and hematology index data;
the processing module is used for processing the clinical index data and the hematology index data based on a preset processing rule to obtain a target data set;
the construction module is used for constructing a multi-system atrophy disabling prediction model by using a kernlab program package in an R program according to the target data set, a support vector machine algorithm and a preset linear kernel function.
In yet another aspect, a multi-system atrophy disability prediction device comprises: a processor, and a memory coupled to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the multi-system atrophy disability prediction method;
the processor is configured to invoke and execute the computer program in the memory.
The beneficial effects of the invention are as follows:
according to the multi-system atrophy disability prediction method, the model building method, the device and the equipment, clinical indexes of multi-system atrophy patients are combined with hematology indexes to serve as prediction data for the first time, feature screening is carried out on the prediction data, and a support vector machine modeling is adopted on a plurality of screened features, so that accurate prediction of the multi-system atrophy patients on disability is achieved, clinicians are guided to carry out individual accurate treatment on the patients, life quality of the patients is improved, and prognosis of the patients is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for constructing a multi-system atrophy disability prediction model according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a multi-system atrophy disability prediction method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a multi-system atrophy disability prediction model building device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a multi-system atrophy disability prediction device according to an embodiment of the present invention.
Reference numerals: 31-an acquisition module; 32-a processing module; 33-building a module; 41-a processor; 42-memory.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
The disease progression process of multisystem atrophy is quite complex, and is usually the result of the combined action of multiple factors, and the traditional regression analysis statistical method is poor in treating complex multiple factors, so that a prediction model of multisystem atrophy disability is not available at present.
Based on the method, the invention establishes a failure prediction model of multi-system atrophy based on a support vector machine technology, thereby guiding individual treatment, delaying disease progression and improving life quality.
The embodiment of the invention provides a method for constructing a multisystem atrophy disability prediction model.
Fig. 1 is a schematic flow chart of a method for constructing a multi-system atrophy disability prediction model according to an embodiment of the present invention, referring to fig. 1, the method provided by the embodiment of the present invention may include the following steps:
s11, acquiring basic data of a multi-system atrophy patient in a preset time period, wherein the basic data comprises: clinical index data and hematology index data.
S12, processing the clinical index data and the hematology index data based on a preset processing rule to obtain a target data set.
When constructing the multisystem atrophy disability prediction model, basic data of a patient suffering from multiple atrophy with a disease course in a preset time period can be collected first, wherein the basic data can include: clinical index data and hematology index data.
For example, the preset time period may be 3 years, and basic data (baseline clinical data) of a multi-system atrophy patient with a disease course within 3 years at the time of a visit in a certain hospital or a certain area is acquired. Clinical index data may include: age, sex, date of onset, date of first diagnosis, delay of diagnosis, course of illness, body mass index, type of diagnosis, form of first symptoms, whether there is recurrent fall within 3 years, whether there is pyramidal signs, wheezing, whether there is severe snoring, whether there is rapid eye movement sleep disturbance, score of first part of unified multisystem atrophy assessment scale, score of second part of unified multisystem atrophy assessment scale, score of fourth part of unified multisystem atrophy assessment scale, total score of unified multisystem atrophy assessment scale, whether there is orthostatic hypotension
And when judging whether the rapid eye movement sleep behavior disorder (Rapid eye movement sleep behavior disorder, RBD) exists, evaluating by using a rapid eye movement sleep behavior disorder evaluation scale, wherein the score is greater than or equal to 5 and represents that the RBD exists. Patients were assessed for motor symptoms using the unified multisystemic atrophy assessment scale (Unified Rating MSA Scale, UMSARS). For assessing whether the patient has orthostatic hypotension, the patient can rest on the examination bed for 10 minutes, then the blood pressure in the lying position is measured and recorded, then the patient is kept in a standing position from the examination bed, the standing position blood pressure after 1,3,5 and 10 minutes is measured and recorded, and if the systolic pressure of any one standing position of the patient is reduced by more than 30mmHg than that of the lying position or the diastolic pressure is reduced by more than 15mmHg than that of the lying position, the orthostatic hypotension is defined as orthostatic hypotension (Orthostatic hypotension, OH).
When the hematology index data is acquired, the fasting hematology sample of the patient in the treatment period can be acquired for detection: the detection index is blood routine and liver and kidney functions.
A total of 167 patients with multiple system atrophy with a disease course of less than 3 years were enrolled after the baseline assessment, i.e., disabled wheelchair patients, were excluded.
167 patients with multiple system atrophy were subjected to follow-up assessment, either face-to-face assessment once per year or telephone assessment, to observe whether they were incapacitated and restricted to wheelchairs, and to record the time to begin using wheelchairs. Patient disability is limited to wheelchair definition as disabled. We have disability as an ending indicator. According to the follow-up result, we take the course of the disease as a limit, the time of 4 years is marked as 0 when disabling occurs within 4 years, the time of no disabling occurs within 4 years is marked as 1, and meanwhile, the data of the course of the disease within 4 years but no disabling occurs are removed. Data were finally obtained for 137 patients with multiple system atrophy, all conforming to the likely multiple system atrophy according to the 2008 second edition diagnostic criteria.
Pretreatment and format conversion of data from 137 patients with multiple system atrophy: sex (male=1, female=0); body Mass Index (BMI) =square of body weight/height (international unit kg/m 2); the diagnosis types are divided into parkinson's disease as main type (MSA with predominantly parkinsonian features, MSA-P) =1 and cerebellar ataxia as main type (MSA with predominantly cerebellar ataxia, MSA-C) =0; the forms of the first symptoms are classified into autonomic dysfunction onset=0 (including dysuria and orthostatic hypotension) and motor symptom onset=1 (including cerebellar ataxia and parkinsonism); recurrent falls within 3 years = 1, no =0; there is pyramidal sign = 1, no = 0; wheezing=1, no=0; there was severe snoring = 1, no = 0; with rbd=1, no=0; there was orthostatic hypotension=1, no=0; the other indexes are all numerical data.
The final acquisition of the target data set includes: the clinical indexes include: age, sex, age of onset, delay of diagnosis, course of illness, body Mass Index (BMI), type of diagnosis, first symptom form, whether repeatedly falls within 3 years, whether there is pyramidal signs, wheezing, whether there is severe snoring, whether there is rapid eye movement sleep disorder (RBD), unified multisystemic atrophy assessment scale (Unified Rating MSA Scale, UMSARS) first score, UMSARS second score, UMSARS fourth score, UMSARS total score, whether there is orthostatic hypotension; hematology indices include: red blood cell count, hemoglobin, hematocrit, average red blood cell volume, average red blood cell hemoglobin content, average red blood cell hemoglobin concentration, red blood cell distribution width CV, red blood cell distribution width SD, platelet count, white blood cell count, neutrophil fraction, lymphocyte fraction, monocyte fraction, eosinophil fraction, basophil fraction, total bilirubin, direct bilirubin, indirect bilirubin, alanine Aminotransferase (ALT), aspartate Aminotransferase (AST), AST/ALT ratio, total protein, albumin, globulin, white ball fraction, glucose, urea, creatine, serum cystatin C, uric acid, triglycerides, cholesterol, high density lipoprotein, low density lipoprotein, alkaline phosphatase, glutamyltranspeptidase, creatine kinase, lactate dehydrogenase, hydroxybutyrate dehydrogenase.
For example, taking patient 1 as an example, sex men=1, age 49, onset age 47, diagnosis delay 1.5 years, course 2, body mass index 23.6, diagnosis type MSA-P type=1, first symptom motion symptom=1, repeated fall over 3 years=1, cone beam sign=1, no wheezing=0, no severe snoring=0, rapid eye sleep disorder=1, umsare first part score 10, umsare second part score 12, umsare fourth part score 2, umsare total score 24, upstanding hypotension=1, red blood cell count=4.76, hemoglobin=138, red blood cell pressure=0.41, mean red blood cell volume=86.3, mean red blood cell hemoglobin content=29, mean red blood cell hemoglobin concentration=336, red blood cell distribution width CV 12.7, red blood cell distribution width sd=40.5, platelet count=199, white blood cell=6.1, neutral cell score=6.6.36, leaf area=6.36, leaf area=3.4.02, absolute value of 1, absolute value of 1.4.3, absolute value of 1, value of total bilirubin, value of 1.3.4.3 absolute value of 1, value of total leaf area of 1.4.4.4.5 absolute value of 1, value of total leaf area of 1, value of total blood glucose Total protein=69.2, albumin=40.8, globulin=28.4, white ball ratio=1.44, glucose=5.38, urea=4.88, creatinine=67.1, serum cystatin c=0.94, uric acid=424, triglyceride=2.1, cholesterol=4.81, high density lipoprotein=1, low density lipoprotein=2.89, alkaline phosphatase=82, glutamyl transpeptidase=16, creatine kinase=82, lactate dehydrogenase=173, hydroxybutyrate dehydrogenase=125. Disability=0 occurs at the 3 rd year of the course according to follow-up.
S13, supporting a vector machine algorithm and a preset linear kernel function according to the target data set, the preset linear kernel function and the support vector machine algorithm, and constructing a multi-system atrophy incapacitation prediction model by using a kernlab program package in the R program.
After the target data set is obtained, a linear inseparable support vector machine algorithm is preferentially used, and a kernel function is utilized: k (x) i ,x j )＝φ(x i )·φ(x j ) A predictive model of multisystem atrophy disability was constructed using the kernlab package in the R program.
In this application, the classification plane constraint may be:
y i (wx i +b)≥1，i＝1,2,…,N。
in order to balance generalization ability and error classification, the method is characterized by min1/2 II 2 A penalty term is introduced:
converting the objective function into:
where C is the error penalty factor, also called cost value, representing the degree of penalty to the error-divided sample points. The algorithm attempts to minimize the total cost rather than finding the maximum separation. I.e.
y i (wx i +b)≥1-ξ i ，ξi≥0。
The target data set is input into a support vector machine model, and all the effective samples are randomly divided into a training set and a testing set through the system. For example, 137 samples are randomly divided into a training set and a test set by system sampling; 80% (109) of them are training sets and 20% (28) are test sets. It should be noted that the data are only listed here and are not limiting.
And constructing a multisystem atrophy incapacitation prediction model according to a support vector machine algorithm, the linear kernel function and the target data set.
The error penalty factor C of the support vector machine may be selected from the range of 5-20, for example, it is considered to select specific point values between 5,6,7,8, …,17,18,19,20 and the above values, and the preferred penalty factor is 5-8, which are not described in detail herein.
To verify the accuracy of the failure prediction model evaluation, kappa statistics can be used. The value range of Kappa is [0,1], the higher the value of the coefficient is, the higher the classification accuracy realized by the representative model is, and when the value of Kappa is 0.6 or above, the predicted value and the true value of the representative model have better consistency.
The kappa statistic calculation method using Cohen in the present invention can be expressed as:
pr (a) represents the true agreement between the model predicted value and the true value, and Pr (e) represents the agreement between the model predicted value and the expected value.
After the prediction model result is obtained, the test set is used for verification, the prediction result and the actual situation are compared to obtain a confusion matrix between the prediction result and the actual situation, the confusion matrix is shown in the following table 1, wherein 8 is the number of sample cases in which the incapacitation occurs and is predicted to be incapacitation in the actual 4 years, 2 is the number of sample cases in which the incapacitation occurs and is predicted to not occur in the actual 4 years, and 4 is the number of sample cases in which the incapacitation does not occur and is predicted to occur in the actual 4 years; 14 is the number of samples for which no disablement has actually occurred within 4 years and is predicted to have not occurred.
TABLE 1
In table 1, 0 represents that disablement occurred within 4 years, and 1 represents that disablement did not occur within 4 years.
Therefore, the sensitivity of the disabling prediction model constructed in this embodiment is: 87.5%, specificity is: 66.7%, the Kappa coefficient is 0.6,0.6, the value range is [0.6,1], and therefore, the accuracy of the model is good.
According to the construction method of the multisystem atrophy disability prediction model based on the support vector machine, feature screening is carried out according to clinical features of multisystem atrophy patients and combined with hematology indexes, and the support vector machine modeling is adopted for the screened multiple features, so that accurate prediction of the multisystem atrophy patients on disability is realized, and a clinician is guided to carry out individual accurate treatment on the patients, so that prognosis of the patients is improved, and life quality of the patients is improved.
According to the method for constructing the multisystem atrophy disability prediction model, provided by the embodiment of the invention, clinical characteristics of a multisystem atrophy patient are combined with hematological indexes to serve as prediction data for the first time, the prediction data are subjected to characteristic screening, and a support vector machine modeling is adopted for a plurality of screened characteristics, so that accurate prediction of the multisystem atrophy patient disability is realized, and a clinician is guided to conduct individualized accurate treatment on the patient, so that the prognosis situation of the patient is improved, and the quality of life of the patient is improved.
Based on a general inventive concept, the embodiment of the invention also provides a multi-system atrophy disability prediction method.
Fig. 2 is a flow chart of a multi-system atrophy disability prediction method provided by an embodiment of the present invention, referring to fig. 2, the prediction method provided by the embodiment of the present invention may include the following steps:
s21, acquiring basic data of a target multisystem atrophy patient, wherein the basic data comprises the following steps: clinical index data and hematology index data.
S22, processing the target data based on a preset processing rule to obtain the target data.
S23, predicting the disability condition of the target multi-system atrophy patient according to the target data and the model established by the multi-system atrophy disability prediction model establishing method.
In a specific prediction process, the specific acquisition process and processing process may be described in the above embodiment by acquiring the basic data of the target patient, which is not described herein. And inputting the acquired target data into the constructed prediction model, so as to acquire the incapacitation prediction condition of the target patient.
Based on a general inventive concept, the embodiment of the invention also provides a multi-system atrophy incapacitation prediction model construction device.
Fig. 3 is a schematic structural diagram of a multi-system atrophy disability prediction model building device according to an embodiment of the present invention, referring to fig. 3, the multi-system atrophy disability prediction model building device according to an embodiment of the present invention includes: an acquisition module 31, a processing module 32 and a construction module 33.
An obtaining module 31, configured to obtain basic data of a patient suffering from multiple system atrophy in a preset period of time, where the basic data includes: clinical index data and hematology index data;
a processing module 32, configured to process the clinical index data and the hematology index data based on a preset processing rule, and obtain a target data set;
the construction module 33 is configured to construct a prediction model of multiple system atrophy disability using a kernlab package in the R procedure according to the target data set, the support vector machine algorithm, and the preset linear kernel function.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
According to the multi-system atrophy disability prediction model construction device provided by the embodiment of the invention, clinical indexes of a multi-system atrophy patient are combined with hematological indexes to serve as prediction data for the first time, feature screening is carried out on the prediction data, and a support vector machine modeling is adopted on a plurality of screened features, so that accurate prediction of the multi-system atrophy patient disability is realized, and a clinician is guided to carry out individualized accurate treatment on the patient, so that prognosis of the patient is improved, and life quality of the patient is improved.
Based on one general inventive concept, the embodiment of the invention also provides a multi-system atrophy disability prediction device.
Fig. 4 is a schematic structural diagram of a multi-system atrophy disabling prediction device according to an embodiment of the present invention, referring to fig. 4, the multi-system atrophy disabling prediction device according to an embodiment of the present invention includes: a processor 41, and a memory 42 connected to the processor.
The memory 42 is used for storing a computer program for at least the multisystem atrophy disability prediction method according to any of the above embodiments;
the processor 41 is used to call and execute a computer program in memory.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (6)
1. A method of constructing a multisystem atrophy disability prediction model, comprising:
acquiring basic data of a patient suffering from multiple system atrophy over a preset time period, wherein the basic data comprises: clinical index data and hematology index data; wherein, clinical indexes include: age, sex, age of onset, delay of diagnosis, course of illness, body mass index, type of diagnosis, form of first symptoms, whether there is repeated falls within a preset period of time, whether there is pyramidal signs, whether there is wheezing, whether there is severe snoring, whether there is rapid eye movement sleep disorder, the first part score of the unified multisystem atrophy assessment scale, the second part score of the unified multisystem atrophy assessment scale, the fourth part score of the unified multisystem atrophy assessment scale, the total score of the unified multisystem atrophy assessment scale, whether there is orthostatic hypotension; hematology indices include: red blood cell count, hemoglobin, hematocrit, average red blood cell volume, average red blood cell hemoglobin content, average red blood cell hemoglobin concentration, red blood cell distribution width CV, red blood cell distribution width SD, platelet count, white blood cell count, neutrophil fraction, lymphocyte fraction, monocyte fraction, eosinophil fraction, basophil fraction, total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, aspartate aminotransferase, alanine aminotransferase/aspartate aminotransferase ratio, total protein, albumin, globulin, white ball fraction, glucose, urea, creatine, serum cystatin C, uric acid, triglycerides, cholesterol, high density lipoprotein, low density lipoprotein, alkaline phosphatase, glutamyl transpeptidase, creatine kinase, lactate dehydrogenase, hydroxybutyrate dehydrogenase;
processing the clinical index data and the hematology index data based on a preset processing rule to obtain a target data set, including: scoring the patient using a rapid eye movement sleep behavioral disorder assessment scale; a unified multisystem atrophy assessment scale is adopted to assess the movement symptoms of the patient; based on a preset orthostatic hypotension evaluation rule, evaluating whether the patient has orthostatic hypotension; the method specifically comprises the following steps: assigning a patient gender according to gender, male=1, female=0; obtaining a body mass index of the patient, body mass index = square of body weight/height; assigning a diagnosis type to the patient according to the diagnosis type, wherein parkinson's disease is main type=1, and cerebellar ataxia is main type=0; the form of the first symptoms is divided into onset of autonomic dysfunction=0; motor symptom onset = 1; recurrent falls within 3 years = 1, no =0; there is pyramidal sign = 1, no = 0; wheezing=1, no=0; there was severe snoring = 1, no = 0; there was a rapid eye movement sleep disorder = 1, no = 0; there was orthostatic hypotension=1, no=0;
according to the target data set, a support vector machine algorithm and a preset linear kernel function, a kernlab program package in an R program is used for constructing a multi-system atrophy disabling prediction model; the construction method of the preset linear kernel function comprises the following steps:
constructing a kernel function: k (xi, xj) =Φ (xi) ·Φ (xj);
based on the relaxation variables, creating soft intervals, and acquiring constraint conditions of a classification plane as follows: y is i (wx i +b)≥1，i＝1,2,…,N；
Acquiring a soft boundary objective function:
y i (wx i +b)≥1-ξ i ，ξ i ≥0；
wherein C is an error penalty factor; ζ is a relaxation variable.
2. The method according to claim 1, characterized in that:
the error penalty factor C for the support vector machine is selected in the range 5-8.
3. The method as recited in claim 1, further comprising: the accuracy of the predictive model is evaluated based on Kappa statistics.
4. A method of predicting multiple system atrophy disability, comprising:
acquiring basic data of a target multisystem atrophy patient, the basic data comprising: clinical index data and hematology index data;
processing the target data based on a preset processing rule to acquire target data;
predicting the disability condition of the target multisystem atrophy patient according to the target data and a model established by the method for constructing the multisystem atrophy disability prediction model according to any one of claims 1-3.
5. A multi-system atrophy disability prediction model construction device, comprising: the device comprises an acquisition module, a processing module and a construction module;
the acquisition module is used for acquiring basic data of a multi-system atrophy patient with a disease course in a preset time period, and the basic data comprises: clinical index data and hematology index data; wherein, clinical indexes include: age, sex, age of onset, delay of diagnosis, course of illness, body mass index, type of diagnosis, form of first symptoms, whether there is repeated falls within a preset period of time, whether there is pyramidal signs, whether there is wheezing, whether there is severe snoring, whether there is rapid eye movement sleep disorder, the first part score of the unified multisystem atrophy assessment scale, the second part score of the unified multisystem atrophy assessment scale, the fourth part score of the unified multisystem atrophy assessment scale, the total score of the unified multisystem atrophy assessment scale, whether there is orthostatic hypotension; hematology indices include: red blood cell count, hemoglobin, hematocrit, average red blood cell volume, average red blood cell hemoglobin content, average red blood cell hemoglobin concentration, red blood cell distribution width CV, red blood cell distribution width SD, platelet count, white blood cell count, neutrophil fraction, lymphocyte fraction, monocyte fraction, eosinophil fraction, basophil fraction, total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, aspartate aminotransferase, alanine aminotransferase/aspartate aminotransferase ratio, total protein, albumin, globulin, white ball fraction, glucose, urea, creatine, serum cystatin C, uric acid, triglycerides, cholesterol, high density lipoprotein, low density lipoprotein, alkaline phosphatase, glutamyl transpeptidase, creatine kinase, lactate dehydrogenase, hydroxybutyrate dehydrogenase;
the processing module is used for processing the clinical index data and the hematology index data based on a preset processing rule to obtain a target data set; the method is particularly used for scoring the patient by adopting a rapid eye movement sleep behavior disorder assessment scale; a unified multisystem atrophy assessment scale is adopted to assess the movement symptoms of the patient; based on a preset orthostatic hypotension evaluation rule, evaluating whether the patient has orthostatic hypotension; the method is particularly used for: assigning a patient gender according to gender, male=1, female=0; obtaining a body mass index of the patient, body mass index = square of body weight/height; assigning a diagnosis type to the patient according to the diagnosis type, wherein parkinson's disease is main type=1, and cerebellar ataxia is main type=0; the form of the first symptoms is divided into onset of autonomic dysfunction=0; motor symptom onset = 1; recurrent falls within 3 years = 1, no =0; there is pyramidal sign = 1, no = 0; wheezing=1, no=0; there was severe snoring = 1, no = 0; there was a rapid eye movement sleep disorder = 1, no = 0; there was orthostatic hypotension=1, no=0;
the construction module is used for constructing a multi-system atrophy disabling prediction model by using a kernlab program package in an R program according to the target data set, a support vector machine algorithm and a preset linear kernel function; the construction method of the preset linear kernel function comprises the following steps:
constructing a kernel function: k (xi, xj) =Φ (xi) ·Φ (xj);
based on the relaxation variables, creating soft intervals, and acquiring constraint conditions of a classification plane as follows: y is i (wx i +b)≥1，i＝1,2,…,N；
Acquiring a soft boundary objective function:
y i (wx i +b)≥1-ξ i ，ξ i ≥0；
wherein C is an error penalty factor; ζ is a relaxation variable.
6. A multiple system atrophy disability prediction device, comprising: a processor, and a memory coupled to the processor;
the memory is used for storing a computer program at least for executing the multisystem atrophy disability prediction method according to claim 4;
the processor is configured to invoke and execute the computer program in the memory.
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