CN115862850A - Modeling method and device of hepatocellular carcinoma monitoring model based on longitudinal multidimensional data - Google Patents
Modeling method and device of hepatocellular carcinoma monitoring model based on longitudinal multidimensional data Download PDFInfo
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Abstract
The invention discloses a modeling method and a device of a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data, which relate to the technical field of medical treatment, and the method comprises the following steps: acquiring longitudinal follow-up case information of a sample patient, and extracting sample evaluation information from the longitudinal follow-up case information; determining first sample characteristic information from the sample evaluation information, and constructing a first model based on the first sample characteristic information; inputting sample evaluation information of a sample patient into a first model to obtain a first prediction score output by the first model; determining second sample characteristic information from the first prediction score and the sample evaluation information of the sample patient, and constructing a second model based on the second sample characteristic information and the first prediction score; and sequentially processing the first model and the second model to construct a third model. The third model constructed based on the longitudinal multidimensional data can better monitor the hepatocellular carcinoma occurrence condition of the patient, and has a good effect when being applied to the cirrhosis crowd.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a modeling method and a device of a hepatocellular carcinoma monitoring model based on longitudinal multi-dimensional data.
Background
Hepatocellular carcinoma is the main histological subtype of liver cancer, and can account for more than 90% of primary liver cancer.
If hepatocellular carcinoma can be monitored more accurately, patients with high risk of hepatocellular carcinoma can be identified as soon as possible, so that the method is helpful for guiding clinicians to select and implement more accurate treatment measures for the patients, and intervenes and treats the patients as soon as possible, thereby obtaining better surgical effect and improving prognosis of the patients, and is one of key measures capable of remarkably improving survival rate of hepatocellular carcinoma.
Currently, there are many hepatocellular carcinoma prediction models available for monitoring hepatocellular carcinoma in clinic, however, the efficacy of these models is significantly reduced in the liver cirrhosis population. Because only single follow-up data is used and most of the data is baseline clinical data, most models lack real data for monitoring the efficiency of hepatocellular carcinoma for a long time, the extremely early occurrence condition and the progress change of the hepatocellular carcinoma cannot be correctly reflected, and clinical problems of unreasonable monitoring and management of the hepatocellular carcinoma, untimely treatment and management and the like are easily caused.
Disclosure of Invention
In view of this, the embodiments of the present invention provide a modeling method and apparatus for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data, so as to solve the problem that a hepatocellular carcinoma prediction model cannot correctly monitor hepatocellular carcinoma.
According to a first aspect, an embodiment of the present invention provides a modeling method for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data, the method including:
acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information, and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information;
determining first sample characteristic information from the sample evaluation information of the sample patient and constructing a first model based on the first sample characteristic information of the sample patient; the first model is used to predict a first prediction score that characterizes a corresponding hepatocellular carcinoma monitoring condition of a sample patient;
inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model;
determining second sample characteristic information from the first prediction score and the sample assessment information for the sample patient and constructing a second model based on the second sample characteristic information and the first prediction score for the sample patient; the second model is used for predicting a second prediction score used for characterizing a corresponding hepatocellular carcinoma monitoring condition of the sample patient;
sequentially processing the constructed first model and the constructed second model to construct a third model; the third model is used to predict a third prediction score that is used to characterize a corresponding hepatocellular carcinoma monitoring condition in the sample patient.
With reference to the first aspect, in a first implementation manner of the first aspect, the acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information, and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information specifically includes the following steps:
acquiring the longitudinal follow-up case information of the sample patient, and determining the type of the longitudinal follow-up case information;
if the longitudinal follow-up case information is determined to be unstructured type information, carrying out structuralization processing on the unstructured longitudinal follow-up case information to obtain structuralized longitudinal follow-up case information;
and screening the structured longitudinal follow-up case information, and extracting the sample evaluation information.
With reference to the first aspect, in a second embodiment of the first aspect, the first model is obtained by modeling through the following steps:
determining an initial evaluation factor corresponding to the first sample characteristic information;
adjusting the initial evaluation factor to obtain a first evaluation factor;
constructing the first model based on the first sample feature information and the first evaluation factor.
With reference to the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the inputting the sample evaluation information of the sample patient into the first model that has been constructed to obtain a first prediction score corresponding to the sample patient output by the first model specifically includes:
inputting the sample evaluation information of the sample patient into the first model, and processing the sample evaluation information based on the first sample characteristic information and the first evaluation factor corresponding to the first model to obtain the first prediction score output by the first model.
With reference to the second embodiment of the first aspect, in a fourth embodiment of the first aspect, the second model is obtained by modeling through the following steps:
grouping the sample patients to obtain sample patients of a diseased group and sample patients of a non-diseased group;
fitting the sample evaluation information of the sample patients in the diseased group and the non-diseased group and a first prediction score generated by previous follow-up based on an initial longitudinal model to obtain a first group of mean contours of the diseased group and a second group of mean contours of the non-diseased group respectively; the initial longitudinal model adopts a multivariate linear mixed effect model;
constructing the second model based on the first set of mean contours and the second set of mean contours.
With reference to the fourth embodiment of the first aspect, in the fifth embodiment of the first aspect, when the third model is applied, the second prediction score is obtained by:
when determining that a first prediction score corresponding to the first follow-up visit of the person to be evaluated exceeds a first score, acquiring longitudinal follow-up case information of the current follow-up visit and previous follow-up visits of the person to be evaluated and the first prediction score of each follow-up visit, processing the longitudinal follow-up case information, and extracting evaluation information of the person to be evaluated from the longitudinal follow-up case information;
determining second feature information of the current visit from the first prediction score corresponding to the multiple visits of the person to be evaluated and the evaluation information;
inputting the first prediction scores of the multiple visits and evaluation information into the second model, and calculating a longitudinal contour corresponding to the person to be evaluated based on the initial longitudinal model;
comparing the longitudinal contour with the first group of average contours and the second group of average contours respectively to obtain a first grouping probability of the person to be evaluated summarized as the diseased group and a second grouping probability of the person to be evaluated summarized as the non-diseased group;
and calculating the second prediction score corresponding to the person to be evaluated based on the first grouping probability.
With reference to the first aspect, in a sixth implementation manner of the first aspect, when the third model is applied, the third prediction score is obtained by:
acquiring longitudinal follow-up case information of a person to be evaluated, processing the longitudinal follow-up case information, and extracting the evaluation information of the person to be evaluated from the longitudinal follow-up case information;
determining first characteristic information of a first follow-up visit from the evaluation information of the first follow-up visit;
inputting the first feature information of the first follow-up visit to the first model to obtain the first prediction score corresponding to the first follow-up visit of the person to be evaluated output by the first model;
when determining that a first prediction score corresponding to the first follow-up visit of a person to be evaluated exceeds a first score, determining second feature information of the current follow-up visit from the first prediction score of the current follow-up visit and previous follow-up visits of the person to be evaluated and the evaluation information;
inputting the second feature information of the current visit into the second model to obtain a second prediction score output by the second model and corresponding to the current visit of the person to be evaluated, and obtaining a third prediction score of the person to be evaluated based on the first prediction score of the first visit and the second prediction score of the current visit;
and when the first prediction score corresponding to the first follow-up visit of the to-be-evaluated person is determined not to exceed the first score, obtaining a third prediction score of the to-be-evaluated person based on the first prediction score of the first follow-up visit.
In a second aspect, an embodiment of the present invention further provides a modeling apparatus for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data, the apparatus including:
the information extraction module is used for acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information;
a first construction module for determining first sample characteristic information from the sample evaluation information of the sample patient and constructing a first model based on the first sample characteristic information of the sample patient; the first model is used to predict a first prediction score that characterizes a corresponding hepatocellular carcinoma monitoring condition of a sample patient;
the prediction evaluation module is used for inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model;
a second construction module for determining second sample characteristic information from the first prediction score and the sample assessment information of the sample patient and constructing a second model based on the second sample characteristic information and the first prediction score of the sample patient; the second model is used for predicting a second prediction score used for characterizing a corresponding hepatocellular carcinoma monitoring condition of the sample patient;
the third construction module is used for carrying out sequential processing on the constructed first model and the constructed second model to construct a third model; the third model is used to predict a third prediction score that is used to characterize a corresponding hepatocellular carcinoma monitoring condition in the sample patient.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of the method for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data as described in any one of the above.
In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data as described in any one of the above.
According to the modeling method and device for the hepatocellular carcinoma monitoring model based on the longitudinal multidimensional data, provided by the invention, the sample evaluation information of a longitudinal data type containing a serological marker is extracted from the longitudinal follow-up case information of a sample patient, the first model is constructed based on the sample evaluation information, the second model is constructed based on the sample evaluation information and the first prediction score output by the first model, and finally the final third model is obtained based on the first model and the second model through sequential application.
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The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a schematic flow chart of a modeling method of a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data provided by the invention;
FIG. 2 is a schematic flow chart illustrating the process of obtaining sample evaluation information in the modeling method of hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to the present invention;
FIG. 3 is a schematic flow chart illustrating a second prediction score obtaining process in the modeling method of the hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to the present invention;
fig. 4 is a schematic flow chart illustrating a third prediction score obtaining process in the modeling method of the hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to the present invention;
FIG. 5 is a schematic structural diagram of a modeling method and apparatus for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to the present invention;
fig. 6 shows a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Hepatocellular carcinoma is the main histological subtype of liver cancer, and can account for more than 90% of primary liver cancer. After the chronic liver disease patient is treated in the hospital, the hospital can acquire longitudinal follow-up case information with time series characteristics of the patient at different time points in a follow-up mode, and timely obtain relevant feedback of the state of the patient. Some patients may have various diseases, and doctors can be helped to track and observe information of other diseases of the patients to a certain extent through a follow-up mode. The good prognosis of hepatocellular carcinoma depends in large part on early diagnosis and disease stage of hepatocellular carcinoma. The average survival time of early/very early liver cancer patients after surgical resection, ablation surgery and liver transplantation exceeds 5 years.
If hepatocellular carcinoma can be monitored more accurately, patients with high risk of hepatocellular carcinoma can be identified as soon as possible, so that the method is helpful for guiding clinicians to select and implement more accurate treatment measures for the patients, and intervenes and treats the patients as soon as possible, thereby obtaining better surgical effect and improving prognosis of the patients, and is one of key measures capable of remarkably improving survival rate of hepatocellular carcinoma.
Currently, there are many hepatocellular carcinoma prediction models for monitoring hepatocellular carcinoma in clinic, but due to the lack of multi-etiology and multi-clinical feature construction and validation queues, and the clinical trend that more and more chronic liver disease patients are complicated by complications, metabolic disorders and antiviral treatment, the application of the hepatocellular carcinoma prediction models is limited and the efficacy of the hepatocellular carcinoma prediction models in the population needs to be further evaluated. Because only single follow-up data is based on baseline clinical data, the traditional hepatocellular carcinoma prediction models such as COX or Logistic regression models ignore the characteristic that the hepatocellular carcinoma risk/recurrence risk is progressive/regressive in follow-up time, namely the characteristic of continuous change, and the efficiency of the hepatocellular carcinoma prediction models is obviously reduced in the population of patients with chronic liver diseases such as cirrhosis. Clinically, the problems of unreasonable monitoring and management of hepatocellular carcinoma, untimely treatment and management, inconsistent doctor-patient communication and the like are easily caused.
The follow-up visit refers to an observation method for a hospital to know the state of an illness of a patient and guide the recovery of the patient regularly in a communication or other mode for the patient who has been seen for a while, through the follow-up visit, the service level of the hospital before and after a doctor can be improved, meanwhile, the follow-up visit is convenient for the doctor to track and observe the patient, and the first-hand data is mastered to carry out statistical analysis and experience accumulation, so that the patient can be better served.
To solve the above problem, as shown in fig. 1, fig. 1 is a schematic flow chart of a modeling method of a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to the present embodiment. The modeling method provided by the embodiment of the invention can be used in electronic equipment. As shown in fig. 1, the modeling method includes the steps of:
and S10, acquiring longitudinal follow-up case information of the sample patient, processing the longitudinal follow-up case information, and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information. The vertical direction may characterize chronological order, i.e., timing. The longitudinal follow-up case information characterizes the demographic characteristics, clinical characteristics, laboratory examination indexes, disease history and medication history of the sample patient at different time points with time sequence characteristics in the follow-up process.
Longitudinal follow-up case information of a sample patient can be acquired through a follow-up mode. The longitudinal follow-up case information can be stored in a database of a medical health system of a hospital, the longitudinal follow-up case information can also be text information and image information fed back by a sample patient independently, and the longitudinal follow-up case information can also be obtained after a medical staff records the text information and the image information fed back by the sample patient, for example, the longitudinal follow-up case information is related information recorded on a case book. The acquisition mode of the longitudinal follow-up case information is not limited at all, and only the longitudinal follow-up case information can be acquired.
Since the longitudinal follow-up case information may be unstructured data, the unstructured data in the longitudinal follow-up case information is also structured in step S10, so that sample evaluation information of the evaluator can be extracted therefrom. It should be noted that not all the structured longitudinal follow-up case information is sample evaluation information, and the sample evaluation information is related data/features for subsequent model modeling obtained after screening from the structured longitudinal follow-up case information.
In the embodiment of the present invention, the sample evaluation information of the sample patient is clinical data thereof, including sex, age, liver index information, and the like, the liver index information can be obtained by medical examination and the like, and the liver index information includes the following information:
sample patients' alpha-fetoprotein (AFP), albumin (ALB), total Bilirubin (TBIL) and platelet count (PLT).
S20, determining first sample characteristic information from sample evaluation information of a sample patient, and constructing a first model based on the first sample characteristic information of the sample patient, wherein the first model is used for predicting a first prediction score, and the first prediction score is used for representing a hepatocellular carcinoma monitoring condition corresponding to the sample patient.
In the embodiment of the present invention, the first prediction score is obtained based on a first model, the first model adopts an maps score model, and the first model is an maps (age-large-ALBI-tables) model.
In practical application, the input data of the first model is evaluation information extracted from current follow-up case information generated by follow-up of a person to be evaluated, and the evaluation information is used for obtaining a first prediction score of the person to be evaluated.
The first sample characteristic information includes sex, age, and partial information in the liver index information of the sample patient. More specifically, the map model is calculated to obtain a corresponding first prediction score based on the sex, age, albumin, total bilirubin, and platelet count of the sample patient, i.e., the first sample characteristic information includes the age, sex, albumin, total bilirubin, and platelet count.
That is, the first model only contains three common clinical test indexes, namely Albumin (ALB), total Bilirubin (TBIL) and platelet count (PLT), except for age and gender, so that the risk prediction model and the method based on the risk prediction model can be well popularized in primary hospitals, and the aamap score can obtain better evaluation performance in patients in the active stage of liver inflammation because liver inflammation indexes are not involved.
In an embodiment of the invention, the first model is modeled by:
and a10, determining first sample characteristic information.
The determining process of the first sample feature information can be seen in detail in step S20 shown in fig. 1, which is not described herein again.
and a20, determining an initial evaluation factor corresponding to the first sample characteristic information.
and a30, adjusting the initial evaluation factor to obtain a first evaluation factor.
and a40, constructing a first model based on the first sample characteristic information and the first evaluation factor.
The final first prediction score is calculated in the following manner:
aMAP = ({ 0.06 × age +0.89 × gender (male: 1; female: 0) + 0.48[(log 10 TBIL× 0.66) + (ALB × -0.085)] -0.01 ×PLT)} + 7.4)/14.77 × 100
It is understood that 0.06, 0.89, 0.48, 0.66, -0.085, 0.01, 7.4, 14.77 and 100 in the above formula are the corresponding first assessment factors of the aMAP scoring model.
And S30, inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model.
Specifically, step S30 includes: the sample evaluation information of the sample patient is input into the first model, and the sample evaluation information is processed based on first sample characteristic information and a first evaluation factor corresponding to the first model, so that a first prediction score output by the first model is obtained.
And S40, determining second sample characteristic information from the first prediction score and the sample evaluation information of the sample patient, and constructing a second model based on the second sample characteristic information and the first prediction score of the sample patient, wherein the second model is used for predicting a second prediction score, and the second prediction score is also used for representing a hepatocellular carcinoma monitoring condition corresponding to the sample patient.
In the embodiment of the present invention, the second prediction score is obtained based on a second model, the second model also uses an aMAP score model, and the second model is an aMAP-2 model.
In practical application, the input data of the second model is evaluation information and a first prediction score extracted from longitudinal follow-up case information generated by the current follow-up visit and the previous follow-up visit of the person to be evaluated, and the second model is used for obtaining a second prediction score of the person to be evaluated.
The second sample characteristic information includes a part of the liver index information of the sample patient. More specifically, the aMAP-2 model is calculated to obtain a corresponding second prediction score based on the first prediction score of the sample patient and the alpha-fetoprotein in the liver index information, i.e., the second sample characteristic information comprises the first prediction score and the alpha-fetoprotein.
In an embodiment of the invention, the second model is modeled by:
and b10, grouping the sample patients to obtain sample patients of a diseased group (hepatocellular carcinoma group patients) and sample patients of a non-diseased group (non-hepatocellular carcinoma group patients).
And b20, fitting sample evaluation information of sample patients in the diseased group and the non-diseased group and a first prediction score generated during the follow-up and the previous follow-up based on the initial longitudinal model to respectively obtain a first group of mean contours of the diseased group and a second group of mean contours of the non-diseased group, wherein the initial longitudinal model adopts a multi-Linear Mixed Effect model (MLMM).
In the embodiment of the present invention, the structure of the initial longitudinal model is as follows:
wherein, when fitting the MLMM,
，/>
represents a patient who is present>
Represents a certain follow-up of the patient,XandZdifferent design matrices representing the fitted variables, E represents the expected value, and ` H `>
And &>
Fixed coefficients and random coefficients in a linear mixed effect model of fitting variables are respectively represented,MVNis a plurality ofA normal distribution. />
Evaluation using a Bayesian Markov chain Monte Carlo algorithm (Markov-ChainMonte-Carlo, MCMC), based on whether or not a value is present>
Are the model internal parameters when calculating variance and covariance.
b30, constructing a second model based on the first group of average profiles and the second group of average profiles.
The patients in the non-hepatocellular carcinoma group and the patients in the hepatocellular carcinoma group are represented by numbers 0 and 1, respectively
The production of, among other things,
and the sample patient who participates in the second model modeling enters a training library, and each time the new sample patient and the sample evaluation information thereof are acquired, the longitudinal profile of the new sample patient is calculated by using the sample evaluation information thereof and is compared with the first group of average profiles and the second group of average profiles, and the grouping prediction probability of the sample patient classified into the hepatocellular carcinoma group, namely a second prediction score (aMAP-2 score) is given for continuously training the second model. The second prediction score has a value in the range of 0-1, and the closer the second prediction score is to 1, the greater the probability that the sample patient is classified as the hepatocellular carcinoma group.
In the embodiment of the present invention, the packet prediction probability is obtained based on a marginal prediction method, that is, based on the aamap-2 score, the marginal prediction method is obtained, and the specific calculation formula is as follows:
in the formula, the first and second images are shown,
the probability is predicted for the packet(s),/>
represents a marginal prediction method>
Indicates a patient, is present>
Is in the group, i.e. the diseased group or the non-diseased group,. Based on the status of the blood vessel>
For adding a horn, for>
，/>
Is a length 2 vector, is greater than or equal to>
A priori probability weight ratio, representing the patient in the sample belonging to each group, based on the weight of the patient in the sample>
Representing parameters generated during model learning.
For each bayesian MCMC simulation, two chains of sufficient length were used (6000 iterations total, the first 3000 iterations burned out), and the convergence between the two MCMC chains was evaluated by a trajectory graph, all using an algorithm generated no-information prior.
The dynamic change in hepatocellular carcinoma risk during a patient's follow-up is demonstrated by the aMAP-2 score that will change with each follow-up, and can be expressed by the mean prediction period (MLT) as the mean time difference between the time when an hepatocellular carcinoma patient is predicted by the aMAP-2 score to be a high risk outcome and when it is clinically diagnosed. The MLT result of aMAP-2 score shows that the hepatocellular carcinoma patient can be early warned for the first time to be high risk of hepatocellular carcinoma, so that hepatocellular carcinoma monitoring is enhanced in time and intervention is performed in time.
And S50, carrying out sequential processing on the constructed first model and the constructed second model to construct a third model. Wherein the third model is used to predict a third prediction score, which is also used to characterize the corresponding hepatocellular carcinoma monitoring condition of the sample patient.
In the present embodiment, the third prediction score is obtained based on a third model, which is an aamap-2 model because the third model is obtained based on sequential application of the constructed first and second models. Specifically, when the third model is actually applied, sequential application of the first model and the second model means that the first model is used to obtain a first prediction score of the person to be evaluated, when the first prediction score corresponding to the first follow-up of the person to be evaluated exceeds the first score, the second model is used to obtain a second prediction score of the person to be evaluated, and a third prediction score of the person to be evaluated is obtained according to the previous first prediction score and the second prediction score; and when the first prediction score corresponding to the first follow-up visit of the person to be evaluated does not exceed the first score, directly obtaining a third prediction score according to the first prediction score.
Therefore, the third prediction score obtained based on the third model has higher hepatocellular carcinoma monitoring precision and good hepatocellular carcinoma layered monitoring effect, and the third prediction score can guide a doctor to make a better treatment scheme for a patient.
According to the modeling method of the hepatocellular carcinoma monitoring model, sample evaluation information of which the longitudinal data type comprises a serological marker is extracted from longitudinal follow-up case information of a sample patient, a first model is constructed based on the sample evaluation information, a second model is constructed based on the sample evaluation information and a first prediction score output by the first model, and a final third model is obtained based on the first model and the second model through sequential application.
In order to solve the above-mentioned problems, as shown in fig. 2, the present embodiment provides a method for modeling an hepatocellular carcinoma monitoring model based on longitudinal multidimensional data, the method for modeling an hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention can be used in an electronic device, fig. 2 is a schematic flow chart of obtaining sample evaluation information according to the method for modeling an hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention, and as shown in fig. 2, step S10 includes the following steps:
s11, longitudinal follow-up case information of the sample patient is obtained, and the type of the longitudinal follow-up case information is determined.
And S12, determining that the longitudinal follow-up case information is unstructured type information, and carrying out structuralization processing on the unstructured longitudinal follow-up case information to obtain the structuralized longitudinal follow-up case information.
And S13, screening the structured longitudinal follow-up case information, and extracting sample evaluation information.
In step S12, unstructured data in the longitudinal follow-up case information is structured, so that sample evaluation information of the evaluator can be extracted from the unstructured data, and in step S13, sample evaluation information for subsequent model modeling obtained by screening the structured longitudinal follow-up case information is used.
In order to solve the above problems, as shown in fig. 3, a modeling method for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data is provided in this embodiment, the modeling method for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention can be used in an electronic device, fig. 3 is a schematic flow chart of a second prediction score obtaining process in the modeling method for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention, and as shown in fig. 3, the second prediction score is obtained through the following steps:
and A10, when determining that a first prediction score corresponding to the first follow-up visit of the to-be-evaluated person exceeds a first score, acquiring longitudinal follow-up case information of the to-be-evaluated person at the current follow-up visit and before the follow-up visit and the first prediction score of each follow-up visit, processing the longitudinal follow-up case information, and extracting evaluation information of the to-be-evaluated person from the longitudinal follow-up case information.
The manner of acquiring the evaluation information in step a10 is detailed in step S10 shown in fig. 1, and is not repeated herein.
And A20, determining second characteristic information from the first prediction scores corresponding to the multiple visits of the person to be evaluated and the evaluation information.
The detailed manner of determining the second characteristic information is shown in step S40 shown in fig. 1, and is not described herein again.
And A30, inputting the first prediction scores of the multiple visits and the evaluation information into a second model, and calculating the longitudinal contour corresponding to the person to be evaluated based on the initial longitudinal model.
For details, see the modeling process of the second model in step a30, which is not described herein.
And A40, comparing the longitudinal contour with the first group of average contours and the second group of average contours respectively to obtain a first grouping probability of the person to be evaluated, which is summarized as a diseased group, and a second grouping probability of the person to be evaluated, which is summarized as a non-diseased group.
Similarly, step a40 is to obtain the first grouping probability and the second grouping probability based on the marginal prediction method.
And A50, calculating a second prediction score corresponding to the person to be evaluated based on the first grouping probability.
In order to solve the above problems, as shown in fig. 4, a modeling method and a device for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data are provided in this embodiment, the modeling method for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention can be used in an electronic device, fig. 4 is a flow diagram illustrating a process for obtaining a third prediction score in the modeling method for a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention, and as shown in fig. 4, the third prediction score is obtained through the following steps:
and B10, acquiring longitudinal follow-up case information of the person to be evaluated, processing the longitudinal follow-up case information, and extracting the evaluation information of the person to be evaluated from the longitudinal follow-up case information.
The step B10 is detailed in the step S10 shown in fig. 1, and is not described herein again.
And B20, determining first characteristic information of the first follow-up visit from the evaluation information of the first follow-up visit.
And B30, inputting the first characteristic information of the first follow-up visit into the first model to obtain a first prediction score output by the first model and corresponding to the first follow-up visit of the person to be evaluated.
The step B30 is detailed in the step S30 shown in fig. 1, and is not described herein again.
And B40, when the first prediction score corresponding to the first follow-up visit of the person to be evaluated exceeds the first score, determining second feature information of the current follow-up visit from the first prediction score and the evaluation information of the current follow-up visit and the previous follow-up visit of the person to be evaluated.
The determination manners of the first characteristic information and the second characteristic information are respectively detailed in step S20 and step S40 shown in fig. 1, and are not described herein again.
And B50, inputting the second characteristic information of the current follow-up visit into the second model, obtaining a second prediction score which is output by the second model and corresponds to the current follow-up visit of the person to be evaluated, and obtaining a third prediction score of the person to be evaluated based on the first prediction score of the first follow-up visit and the second prediction score of the current follow-up visit.
And B60, when the first prediction score corresponding to the first follow-up visit of the person to be evaluated is determined not to exceed the first score, obtaining a third prediction score of the person to be evaluated based on the first prediction score corresponding to the first follow-up visit.
The modeling method and apparatus for hepatocellular carcinoma monitoring model based on longitudinal multidimensional data provided by the present invention are described below, and the modeling method and apparatus for hepatocellular carcinoma monitoring model based on longitudinal multidimensional data described below and the modeling method for hepatocellular carcinoma monitoring model based on longitudinal multidimensional data described above can be referred to correspondingly.
As shown in fig. 5, in the present embodiment, a device for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data is provided, the method for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention can be applied to an electronic device, fig. 5 is a schematic structural diagram of the device for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to an embodiment of the present invention, and as shown in fig. 5, the device includes:
the information extraction module 10 is configured to acquire longitudinal follow-up case information of a sample patient, process the longitudinal follow-up case information, and extract sample evaluation information of the sample patient from the longitudinal follow-up case information. The longitudinal follow-up case information is information recording the demographic characteristics, clinical characteristics, laboratory examination indexes, disease history, medication history and the like of the sample patient.
Longitudinal follow-up case information of a sample patient can be acquired through a follow-up mode. The longitudinal follow-up case information can be stored in a database of a medical health system of a hospital, the longitudinal follow-up case information can also be text information and image information fed back by a sample patient independently, and the longitudinal follow-up case information can also be obtained after a medical staff records the text information and the image information fed back by the sample patient, for example, the longitudinal follow-up case information is related information recorded on a case book. The acquisition mode of the longitudinal follow-up case information is not limited at all, and only the longitudinal follow-up case information can be acquired.
Since the longitudinal follow-up case information may be unstructured data, the unstructured data in the longitudinal follow-up case information is also structured in the first building module 20, so that the sample evaluation information of the evaluator can be extracted from the unstructured data. It should be noted that not all the structured longitudinal follow-up case information is sample evaluation information, and the sample evaluation information is related data/features for subsequent model modeling obtained after screening from the structured longitudinal follow-up case information.
In the embodiment of the present invention, the sample evaluation information of the sample patient is clinical data thereof, including sex, age, liver index information, and the like, the liver index information may be obtained by medical examination, and the liver index information includes alpha-fetoprotein (AFP), albumin (ALB), total Bilirubin (TBIL), and platelet count (PLT) of the sample patient.
The first constructing module 20 is configured to determine first sample characteristic information from the sample evaluation information of the sample patient, and construct a first model based on the first sample characteristic information of the sample patient, where the first model is used to predict a first prediction score, and the first prediction score is used to characterize a corresponding hepatocellular carcinoma monitoring condition of the sample patient.
In the embodiment of the present invention, the first prediction score is obtained based on a first model, the first model adopts an maps score model, and the first model is an maps model.
The first sample characteristic information comprises sex, age and partial information training in the liver index information of the sample patient. More specifically, the map model is calculated to obtain a corresponding first prediction score based on the sex, age, albumin, total bilirubin, and platelet count of the sample patient, i.e., the first sample characteristic information includes sex, age, albumin, total bilirubin, and platelet count.
That is, the first model only includes three common clinical test indexes, namely Albumin (ALB), total Bilirubin (TBIL) and platelet count (PLT), except for age and gender, so that the risk prediction model and the device based on the risk prediction model can be well popularized in primary hospitals, and the aamap score can obtain better evaluation performance in patients in the active stage of liver inflammation because no liver inflammation index is involved.
And the prediction evaluation module 30 is configured to input the sample evaluation information of the sample patient into the constructed first model, so as to obtain a first prediction score corresponding to the sample patient output by the first model.
And a second constructing module 40, configured to determine second sample characteristic information from the first prediction score and the sample evaluation information of the sample patient, and construct a second model based on the second sample characteristic information and the first prediction score of the sample patient, where the second model is used to predict a second prediction score, and the second prediction score is also used to characterize a corresponding hepatocellular carcinoma monitoring condition of the sample patient.
In the embodiment of the invention, the second prediction score is obtained based on a second model, the second model also adopts an aMAP score model, and the second model is an aMAP-2 model.
The second sample characteristic information includes a part of the liver index information of the sample patient. More specifically, the aamap-2 model calculates a corresponding second prediction score based on the first prediction score of the sample patient and the alpha-fetoprotein in the liver index information, that is, the second sample characteristic information includes the first prediction score of the valley and the alpha-fetoprotein.
And a third constructing module 50, configured to perform sequential processing on the constructed first model and the constructed second model to construct a third model. Wherein the third model is used to predict a third prediction score, which is also used to characterize the corresponding hepatocellular carcinoma monitoring condition of the sample patient.
In an embodiment of the invention, the third prediction score is obtained based on a third model, which is an aamap-2 model, since the third model is obtained based on a constructed third risk prediction model and a constructed second model applied sequentially. Specifically, when the third model is actually applied, sequential application of the first model and the second model means that the first model is used to obtain a first prediction score of the person to be evaluated, when the first prediction score corresponding to the first follow-up of the person to be evaluated exceeds the first score, the second model is used to obtain a second prediction score of the person to be evaluated, and a third prediction score of the person to be evaluated is obtained according to the previous first prediction score and the second prediction score; and when the first prediction score corresponding to the person to be evaluated does not exceed the first score, directly obtaining a third prediction score according to the first prediction score.
Therefore, the third prediction score obtained based on the third model has higher hepatocellular carcinoma monitoring precision and good hepatocellular carcinoma hierarchical monitoring effect, and the third prediction score can guide a doctor to make a better treatment scheme for a patient.
The modeling device of the hepatocellular carcinoma monitoring model based on the longitudinal multidimensional data extracts sample evaluation information of which the longitudinal data type comprises serological markers from longitudinal follow-up case information of a sample patient, constructs a first model based on the sample evaluation information, constructs a second model based on the sample evaluation information and a first prediction score output by the first model, and finally obtains a final third model based on the first model and the second model by sequential application.
Fig. 6 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 6: a processor (processor) 410, a communication Interface 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic commands in the memory 430 to perform a method of modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data, the method comprising:
acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information, and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information;
determining first sample characteristic information from the sample evaluation information of the sample patient and constructing a first model based on the first sample characteristic information of the sample patient; the first model is used to predict a first prediction score that characterizes a corresponding hepatocellular carcinoma monitoring condition of a sample patient;
inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model;
determining second sample characteristic information from the sample patient's first prediction score and the sample assessment information, and constructing a second model based on the second sample characteristic information and the first prediction score of the sample patient; the second model is used for predicting a second prediction score, and the second prediction score is used for representing a hepatocellular carcinoma monitoring condition corresponding to the sample patient;
sequentially processing the constructed first model and the constructed second model to construct a third model; the third model is used to predict a third prediction score that is used to characterize a corresponding hepatocellular carcinoma monitoring condition in the sample patient.
In addition, the logic commands in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic commands are sold or used as a separate medium. Based on such understanding, the technical solution of the present invention may be essentially or partially contributed to by the prior art, or may be embodied in a form of a software medium, which is stored in a storage medium and includes a plurality of commands for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention further provides a computer program medium, which includes a computer program, the computer program being stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is capable of executing the method for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data provided by the above methods, the method including:
acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information, and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information;
determining first sample characteristic information from the sample evaluation information of the sample patient and constructing a first model based on the first sample characteristic information of the sample patient; the first model is used for predicting a first prediction score, and the first prediction score is used for representing a hepatocellular carcinoma monitoring condition corresponding to a sample patient;
inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model;
determining second sample characteristic information from the sample patient's first prediction score and the sample assessment information, and constructing a second model based on the second sample characteristic information and the first prediction score of the sample patient; the second model is used for predicting a second prediction score, and the second prediction score is used for representing a hepatocellular carcinoma monitoring condition corresponding to the sample patient;
sequentially processing the constructed first model and the constructed second model to construct a third model; the third model is used to predict a third prediction score that is used to characterize a corresponding hepatocellular carcinoma monitoring condition in the sample patient.
In still another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements a method for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data provided by the above methods, the method comprising:
acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information, and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information;
determining first sample characteristic information from the sample evaluation information of the sample patient and constructing a first model based on the first sample characteristic information of the sample patient; the first model is used to predict a first prediction score that characterizes a corresponding hepatocellular carcinoma monitoring condition of a sample patient;
inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model;
determining second sample characteristic information from the sample patient's first prediction score and the sample assessment information, and constructing a second model based on the second sample characteristic information and the first prediction score of the sample patient; the second model is used for predicting a second prediction score used for characterizing a corresponding hepatocellular carcinoma monitoring condition of the sample patient;
sequentially processing the constructed first model and the constructed second model to construct a third model; the third model is used to predict a third prediction score that is used to characterize a corresponding hepatocellular carcinoma monitoring condition in the sample patient.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software medium which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes commands for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A modeling method of a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data is characterized by comprising the following steps:
acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information, and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information;
determining first sample characteristic information from the sample evaluation information of the sample patient and constructing a first model based on the first sample characteristic information of the sample patient; the first model is used to predict a first prediction score that characterizes a corresponding hepatocellular carcinoma monitoring condition of a sample patient;
inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model;
determining second sample characteristic information from the first prediction score and the sample assessment information for the sample patient and constructing a second model based on the second sample characteristic information and the first prediction score for the sample patient; the second model is used for predicting a second prediction score, and the second prediction score is used for representing a hepatocellular carcinoma monitoring condition corresponding to the sample patient;
sequentially processing the constructed first model and the constructed second model to construct a third model; the third model is used to predict a third prediction score that is used to characterize a corresponding hepatocellular carcinoma monitoring condition in the sample patient.
2. The modeling method of hepatocellular carcinoma monitoring model based on longitudinal multidimensional data as claimed in claim 1, wherein the method comprises the following steps:
acquiring the longitudinal follow-up case information of the sample patient, and determining the type of the longitudinal follow-up case information;
if the longitudinal follow-up case information is determined to be unstructured, carrying out structuring processing on the unstructured longitudinal follow-up case information to obtain the structured longitudinal follow-up case information;
and screening the structured longitudinal follow-up case information, and extracting the sample evaluation information.
3. The modeling method of hepatocellular carcinoma monitoring model based on longitudinal multidimensional data as claimed in claim 1, characterized in that the first model is obtained by modeling through the following steps:
determining an initial evaluation factor corresponding to the first sample characteristic information;
adjusting the initial evaluation factor to obtain a first evaluation factor;
constructing the first model based on the first sample feature information and the first evaluation factor.
4. The method as claimed in claim 3, wherein the step of inputting the sample evaluation information of the sample patient into the first model to obtain the first prediction score corresponding to the sample patient output by the first model comprises:
inputting the sample evaluation information of the sample patient into the first model, and processing the sample evaluation information based on the first sample characteristic information and the first evaluation factor corresponding to the first model to obtain the first prediction score output by the first model.
5. The modeling method of hepatocellular carcinoma monitoring model based on longitudinal multidimensional data as claimed in claim 3, wherein the second model is obtained by modeling through the following steps:
grouping the sample patients to obtain sample patients of an affected group and sample patients of a non-affected group;
fitting the sample evaluation information of the sample patients in the diseased group and the non-diseased group and a first prediction score generated by previous follow-up based on an initial longitudinal model to obtain a first group of mean contours of the diseased group and a second group of mean contours of the non-diseased group respectively; the initial longitudinal model adopts a multivariate linear mixed effect model;
constructing the second model based on the first set of mean profiles and the second set of mean profiles.
6. The modeling method of hepatocellular carcinoma monitoring model based on longitudinal multidimensional data as claimed in claim 5, wherein when said third model is applied, said second prediction score is obtained by the following steps:
when determining that a first prediction score corresponding to the first follow-up visit of a person to be evaluated exceeds a first score, acquiring longitudinal follow-up case information of the current follow-up visit and previous follow-up visits of the person to be evaluated and a first prediction score of each follow-up visit, processing the longitudinal follow-up case information, and extracting evaluation information of the person to be evaluated from the longitudinal follow-up case information;
determining second characteristic information from the first prediction scores and evaluation information corresponding to the multiple visits of the person to be evaluated;
inputting the first prediction scores of the multiple visits and the evaluation information into the second model, and calculating a longitudinal contour corresponding to the person to be evaluated based on the initial longitudinal model;
comparing the longitudinal contour with the first group of average contours and the second group of average contours respectively to obtain a first grouping probability of the person to be evaluated summarized as the diseased group and a second grouping probability of the person to be evaluated summarized as the non-diseased group;
and calculating the second prediction score corresponding to the person to be evaluated based on the first grouping probability.
7. The method as claimed in claim 1, wherein the third prediction score is obtained by the following steps when the third model is applied:
acquiring longitudinal follow-up case information of a person to be evaluated, processing the longitudinal follow-up case information, and extracting the evaluation information of the person to be evaluated from the longitudinal follow-up case information;
determining first characteristic information of a first follow-up visit from the evaluation information of the first follow-up visit;
inputting the first feature information of the first follow-up visit to the first model to obtain the first prediction score output by the first model and corresponding to the first follow-up visit of the person to be evaluated;
when determining that a first prediction score corresponding to the first follow-up visit of a person to be evaluated exceeds a first score, determining second feature information of the current follow-up visit from the first prediction score of the current follow-up visit and previous follow-up visits of the person to be evaluated and the evaluation information;
inputting the second feature information of the current visit to the second model, obtaining a second prediction score output by the second model and corresponding to the current visit of the person to be evaluated, and obtaining a third prediction score of the person to be evaluated based on the first prediction score of the first visit and the second prediction score of the current visit;
and when the first prediction score corresponding to the first follow-up visit of the to-be-evaluated person is determined not to exceed the first score, obtaining a third prediction score of the to-be-evaluated person based on the first prediction score of the first follow-up visit.
8. A modeling apparatus for hepatocellular carcinoma monitoring model based on longitudinal multidimensional data, the apparatus comprising:
the information extraction module is used for acquiring longitudinal follow-up case information of a sample patient, processing the longitudinal follow-up case information and extracting sample evaluation information of the sample patient from the longitudinal follow-up case information;
a first construction module for determining first sample characteristic information from the sample evaluation information of the sample patient and constructing a first model based on the first sample characteristic information of the sample patient; the first model is used to predict a first prediction score that characterizes a corresponding hepatocellular carcinoma monitoring condition of a sample patient;
the prediction evaluation module is used for inputting the sample evaluation information of the sample patient into the constructed first model to obtain a first prediction score corresponding to the sample patient output by the first model;
a second construction module for determining second sample characteristic information from the first prediction score and the sample assessment information of the sample patient and constructing a second model based on the second sample characteristic information and the first prediction score of the sample patient; the second model is used for predicting a second prediction score used for characterizing a corresponding hepatocellular carcinoma monitoring condition of the sample patient;
the third construction module is used for carrying out sequential processing on the constructed first model and the constructed second model to construct a third model; the third model is used to predict a third prediction score that is used to characterize a corresponding hepatocellular carcinoma monitoring condition in the sample patient.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for modeling hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to any of claims 1 to 7.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for modeling a hepatocellular carcinoma monitoring model based on longitudinal multidimensional data according to any of claims 1 to 7.
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