CN110111886A - A kind of intelligent interrogation system and method based on XGBoost disease forecasting - Google Patents
A kind of intelligent interrogation system and method based on XGBoost disease forecasting Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of intelligent interrogation systems and method based on XGBoost disease forecasting, the system comprises the clients and server-side by network connection, the client includes interrogation module and display module, the server-side includes data processing module, data memory module and XGBoost disease forecasting module, through the patient symptom information input that obtains client into the more disaggregated models of trained XGBoost, export disease probabilistic forecasting value and predictive disease information, and patient can directly carry out checking relevant information in client, based on machine learning and big data technology, the self-service diagnostic service of patient may be implemented, support the intellectual analysis and prediction to magnanimity patient data, prediction has high accuracy and high-timeliness, it is easy to operate at low cost.
Description
Technical Field
The embodiment of the invention relates to the technical field of disease prediction and diagnosis, in particular to an intelligent inquiry system and method based on XGboost disease prediction.
Background
Because of the problems of medical resource shortage, uneven matching distribution and the like, common people have difficulty in seeing a doctor and are expensive to see a doctor, the intelligent inquiry product is an artificial intelligent system capable of providing self-service consultation and diagnosis service for a patient, can assist a doctor to inquire the patient, effectively reduces the time and money cost of seeing a doctor, and improves the diagnosis and treatment efficiency of the doctor. The existing intelligent inquiry products still stay at the stage of disease conjecture aiming at symptoms, can deal with a small amount of symptoms and diseases, cannot be handed down when aiming at hundreds of thousands of massive big data, and mostly analyze the symptoms based on clinical experience data or databases, so that the accuracy and timeliness of prediction are difficult to guarantee.
Disclosure of Invention
Therefore, the embodiment of the invention provides an intelligent inquiry system and method based on XGboost disease prediction, so as to solve the problem that the existing intelligent inquiry product cannot accurately analyze and predict massive patient data.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
according to a first aspect of the embodiments of the present invention, an intelligent inquiry system based on XGBoost disease prediction is provided, where the system includes a client and a server connected via a network;
the client comprises an inquiry module and a display module;
the inquiry module is used for performing inquiry and answer interaction with a patient through a simulation doctor to acquire basic information and symptom information of the patient;
the display module is used for receiving and displaying the predicted disease information and the predicted value of the disease probability sent by the server;
the server comprises a data processing module, a data storage module and an XGboost disease prediction module;
the data processing module is used for acquiring basic information and symptom information data of a patient at a client side for processing;
the data storage module is used for storing data;
the XGboost disease prediction module is used for inputting the processed patient symptom information data into a trained XGboost multi-classification model, outputting a disease probability prediction value and predicted disease information and sending the disease probability prediction value and the predicted disease information to a client.
Further, the data processing module comprises a standardization module;
the standardization module is used for carrying out 0-1 standardization processing on the acquired symptom information data.
Further, the data processing module also comprises a weight assignment module;
and the weight assignment module is used for carrying out weight assignment on the symptom information data according to the uncertainty of the symptom information.
Furthermore, the server also comprises an incremental data acquisition module and an incremental learning module;
the incremental data acquisition module is used for acquiring newly-increased case data according to a preset period, wherein the newly-increased case data comprises newly-increased disease information and newly-increased disease symptom information data corresponding to the newly-increased disease information;
and the incremental learning module is used for carrying out periodic incremental learning training on the XGboost multi-classification model by using the newly added case data.
Furthermore, the XGboost multi-classification model is based on an XGboost multi-classification algorithm, a gradient lifting method is adopted for training, the type of an iterator is a gbtree, and a loss function adopts a Mean Square Error (MSE) form.
Further, the server also comprises an associated information module;
the associated information module is used for giving out disease associated information according to the predicted disease information and sending the disease associated information to a client side for displaying, wherein the disease associated information comprises disease introduction information, a department of medical guidance, medication suggestions and professional doctor recommendation information which are associated with the predicted disease information.
Further, the client further comprises a feedback module;
the feedback module is used for receiving and processing feedback information of the patient, if the feedback problem of the patient is solved, the inquiry is finished, and if the feedback problem of the patient is not solved, the patient is prompted to directly consult a professional doctor.
Further, the basic information includes sex, age, pregnancy status, past medical history, past medication history, and medical allergy history.
According to a second aspect of the embodiments of the present invention, an intelligent inquiry method based on XGBoost disease prediction is provided, the method including:
the client-side performs question-answer interaction with the patient through a simulated doctor to acquire basic information and symptom information of the patient;
the server side obtains the basic information and symptom information data of the client patient to process;
the server side inputs the processed patient symptom information data into a trained XGboost multi-classification model, outputs a disease probability predicted value and predicted disease information and sends the disease probability predicted value and the predicted disease information to the client side;
and the client receives and displays the predicted disease information and the predicted value of the disease probability sent by the server.
The embodiment of the invention has the following advantages:
the intelligent inquiry system and the method based on XGboost disease prediction provided by the embodiment of the invention comprise a client and a server, wherein the client comprises an inquiry module and a display module, the server comprises a data processing module, a data storage module and an XGboost disease prediction module, patient symptom information acquired by the client is input into a trained XGboost multi-classification model, a disease probability prediction value and predicted disease information are output, and a patient can directly check related information at the client, the intelligent inquiry system can realize self-service diagnosis service of the patient based on machine learning and big data technology, support intelligent analysis and timely prediction of massive patient data, has high accuracy and high timeliness in prediction, is simple and convenient to operate and low in cost, can provide accurate disease diagnosis service for the patient, and can also provide auxiliary diagnosis and auxiliary treatment for a doctor, the misdiagnosis rate is reduced, the occurrence of medical accidents is reduced, and the healthy development of the medical industry is promoted.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a schematic structural diagram of an intelligent inquiry system based on XGBoost disease prediction according to embodiment 1 of the present invention;
fig. 2 is a schematic flow chart of an intelligent inquiry method based on XGBoost disease prediction according to embodiment 2 of the present invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. 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.
Example 1
As shown in fig. 1, the present embodiment provides an intelligent inquiry system based on XGBoost disease prediction, which includes a client 100 and a server 200 connected via a network.
The client 100 includes an interrogation module 110 and a display module 120.
The inquiry module 110 is used for acquiring basic information and symptom information of the patient by simulating the inquiry and answer interaction between the doctor and the patient. The basic information includes information of sex, age, pregnancy status, past medical history, past medication history, and drug allergy history.
The display module 120 is configured to receive and display the predicted disease information and the predicted disease probability value sent by the server 200.
The server 200 includes a data processing module 210, a data storage module 220, and an XGBoost disease prediction module 230.
The data processing module 210 is used for acquiring the basic information and symptom information data of the patient at the client 100 for processing.
Further, the data processing module 210 includes a normalization module and a weight assignment module;
and the standardization module is used for carrying out 0-1 standardization processing on the acquired symptom information data. The symptom information obtained by the client 100 through the simulated question and answer is text data, which needs to be subjected to 0-1 standardization processing, and the text information is mapped into the [0,1] interval, for example, if a patient has a certain symptom, the value is 1, and if the patient does not have the certain symptom, the value is 0.
Further, the data processing module 210 further includes a weight assignment module, and the weight assignment module is configured to perform weight assignment on the symptom information data according to the uncertainty of the symptom information. The value 1 or 0 can be assigned for the case of the above-mentioned symptom being determined or not, while for the case of uncertain symptoms, for example, in the following simulation scenario:
the doctor asks the patient: do you ask you for abdominal pain? "
The patient may respond to three situations: a is not painful; b, a bit of pain; c pain.
Then with the above dialog example, the a and C options are clearly the cases of 0 and 1 described earlier (a is not painful, maps to 0 state, C is painful, maps to 1 state), but the B option is somewhat painful, how did this be judged? In the prior art, a single disease and a symptom are judged, and when the condition is not judged, the uncertain symptom of the option B can be given an initial random weight value by a professional doctor according to the uncertainty of the symptom information, the weight is mapped in a [0,1] interval, for example, the weight is set to be mapped to 0.3, some uncertain conditions of a patient can be reasonably assigned and quantified through weight assignment, and the finally output disease prediction result is more accurate.
The data storage module 220 is used for storing data.
The XGBoost disease prediction module 230 is configured to input the processed patient symptom information data into a trained XGBoost multi-classification model, output a disease probability prediction value and predicted disease information, and send the disease probability prediction value and predicted disease information to the client 100.
The XGboost multi-classification model is based on an XGboost multi-classification algorithm, a gradient lifting method is adopted for training, the type of an iterator is gbtree, and a loss function is in a Mean Square Error (MSE) form.
The XGboost classification algorithm is a lifting tree model, and integrates a plurality of CART regression tree models to form a strong classifier. The algorithm idea is to continuously add trees, continuously perform feature splitting to grow a tree, and each time a tree is added, actually learn a new function to fit the residual error predicted last time.
Assuming that k trees are obtained after training, the score of a sample is predicted, that is, according to the feature of the sample, a corresponding leaf node is fallen in each tree, each leaf node corresponds to a score, and finally, the score corresponding to each tree is only added up to be the predicted value of the sample, that is, the linear sum of a series of classification regression trees, and an optional example can be written as:
wherein,to classify a set of regression trees, fkIs in a function spaceThe k-th regression tree function therein, and Wq(x)Weight of leaf node q under the Single Tree model, RTFor the leaf weights of the tree, q represents the nodes of the tree and T represents the number of leaves on the tree.
The objective function formulated in the training process is:
obj(θ)＝L(θ)+Ω(θ)；
wherein:and representing a training error, and adopting a mean square error, namely an error between a real value and a predicted value.A term of the regularization is represented,t represents the number of leaves on the tree, and gamma represents a control parameter for the number of leaf nodes. WjRepresents the square of the weight modulo of the leaf node j, i.e., L2 regularization, and λ represents the L2 regularization term parameter to prevent overfitting.
As a supplementary description of the CART regression tree, the CART regression tree is a binary tree, and features are continuously split, for example, a current tree node is split based on the jth feature value, a sample with the feature value smaller than s is divided into a left sub-tree, and a sample with the feature value larger than s is divided into a right sub-tree:
R1(j，s)＝{x|x(j)≤s}and R2(j，s)＝{x|x(j)＞s}；
the CART regression tree essentially divides the sample space in the feature dimension, and the optimization of the space division is an NP-Hard problem, so that a heuristic method is used in the decision tree model. The objective function generated by a typical CART regression tree is:
therefore, in order to solve the optimal segmentation feature j and the optimal segmentation point s, the method is converted into an objective function:
therefore, only through traversing all the segmentation points of all the features, the optimal segmentation feature and segmentation point can be found, and finally a regression tree is obtained.
In the embodiment, firstly, an XGboost multi-classification model is constructed, the input and the output of the model are determined, standardized data of patient symptom information are used as the input of the model, and a disease probability prediction value is finally output, because the final disease type is more than 2, a multi-classification machine learning model based on the XGboost is constructed, when the initial model parameters are set, the learning rate is set to be 0.1, the maximum depth of a tree is 6, the minimum loss function reduction value required by node splitting is 0.1, the iteration times are 100 times, the L2 regular lambda parameter is 1, the number of parallel multi-threads is 4, and other parameters are default values.
After the model is built, a training set built by using 0-1 standardized original case data is used for training the XGboost multi-classification model, the original case data comprises a plurality of disease information and symptom information corresponding to the disease information, 4920 case data are used as the original case data in the embodiment, the diagnosis record of each patient is one data, 132 symptoms are in total, 41 diseases are in total, the number of data pieces of each disease is 120, 0-1 standardized processing is carried out on the original case data to obtain a sample data set, the sample data set can be cut into the training set and a testing set according to the proportion of 7:3, and random seeds are set as fixed values in the cutting process so as to facilitate parameter comparison.
And after the training is finished, testing the trained XGboost multi-classification model by using a test set, and outputting disease probability values corresponding to various diseases respectively. And optimizing the XGboost multi-classification model according to user requirements to determine optimal model parameters. And adjusting the model parameters according to specific user requirements to obtain probability distribution meeting the requirements. And finally, performing threshold selection on the disease probability value and outputting a disease probability predicted value. And sequencing the output disease probability values in an order from large to small, selecting a threshold value, such as a maximum value, outputting a disease probability predicted value, and finishing the training and testing of the XGboost multi-classification model.
After the test is finished, indexes such as accuracy, recall rate and F1-Score can be adopted to evaluate the XGboost multi-classification model, after the evaluation is finished, the model is packaged and deployed to a server, and an interface is provided for calling.
Further, the server 200 further includes an incremental data obtaining module 240 and an incremental learning module 250.
The incremental data obtaining module 240 is configured to obtain newly added case data according to a preset period, where the newly added case data includes newly added disease information and newly added disease symptom information data corresponding to the newly added disease information.
The incremental learning module 250 is configured to perform periodic incremental learning training on the XGBoost multi-classification model using the newly added case data. In order to enable the model to better control the existing epidemic diseases, a sample set can be updated in a fixed period (such as one day, three days or one week) based on newly-added case data and a disease verification result of a professional doctor, and supervised learning training of the XGboost model is performed again to obtain a prediction model with more comprehensive data and better timeliness.
Further, the server 200 further includes a related information module 260, where the related information module 260 is configured to provide disease related information according to the predicted disease information, and send the disease related information to the client 100 for display, where the disease related information includes disease introduction information, a department for medical guidance, medication advice, and professional doctor recommendation information related to the predicted disease information. The user is helped to obtain the disease prediction result and also obtain the targeted basic diagnosis suggestion.
Further, the client 100 further includes a feedback module 130, and the feedback module 130 is configured to receive and process feedback information of the patient, end the inquiry if the patient feedback problem is solved, and prompt the patient to directly consult a professional doctor if the patient feedback problem is not solved.
The intelligent inquiry system is based on machine learning and big data technology, can realize self-service diagnosis service of patients, supports intelligent analysis and timely prediction of massive patient data, has high accuracy and high timeliness in prediction, is simple and convenient to operate, is low in cost, can provide accurate disease diagnosis service for the patients, can also provide auxiliary diagnosis and auxiliary treatment for doctors, reduces misdiagnosis rate, reduces occurrence of medical accidents, and promotes healthy development of medical industry.
Example 2
Correspondingly to embodiment 1, this embodiment proposes an intelligent inquiry method based on XGBoost disease prediction, which includes:
s100, the client 100 performs question-answer interaction with a patient through a simulation doctor to acquire basic information and symptom information of the patient;
s200, the server 200 acquires the basic information and symptom information data of the patient of the client 100 to process;
s300, the server 200 inputs the processed patient symptom information data into a trained XGboost multi-classification model, outputs a disease probability predicted value and predicted disease information and sends the disease probability predicted value and the predicted disease information to the client 100;
s400, the client 100 receives and displays the predicted disease information and the predicted disease probability value sent by the server 200.
The specific processes of each step in the intelligent inquiry method based on XGBoost disease prediction provided in the embodiment of the present invention have been described in detail in the above embodiment 1, and therefore, redundant description is not repeated here.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (9)
1. An intelligent inquiry system based on XGboost disease prediction is characterized by comprising a client and a server which are connected through a network;
the client comprises an inquiry module and a display module;
the inquiry module is used for performing inquiry and answer interaction with a patient through a simulation doctor to acquire basic information and symptom information of the patient;
the display module is used for receiving and displaying the predicted disease information and the predicted value of the disease probability sent by the server;
the server comprises a data processing module, a data storage module and an XGboost disease prediction module;
the data processing module is used for acquiring basic information and symptom information data of a patient at a client side for processing;
the data storage module is used for storing data;
the XGboost disease prediction module is used for inputting the processed patient symptom information data into a trained XGboost multi-classification model, outputting a disease probability prediction value and predicted disease information and sending the disease probability prediction value and the predicted disease information to a client.
2. The XGboost disease prediction-based intelligent interrogation system of claim 1, wherein the data processing module comprises a normalization module;
the standardization module is used for carrying out 0-1 standardization processing on the acquired symptom information data.
3. The XGboost disease prediction-based intelligent inquiry system according to claim 2, wherein said data processing module further comprises a weight assignment module;
and the weight assignment module is used for carrying out weight assignment on the symptom information data according to the uncertainty of the symptom information.
4. The XGboost disease prediction-based intelligent inquiry system according to claim 1, wherein the server further comprises an incremental data acquisition module and an incremental learning module;
the incremental data acquisition module is used for acquiring newly-increased case data according to a preset period, wherein the newly-increased case data comprises newly-increased disease information and newly-increased disease symptom information data corresponding to the newly-increased disease information;
and the incremental learning module is used for carrying out periodic incremental learning training on the XGboost multi-classification model by using the newly added case data.
5. The XGboost disease prediction-based intelligent inquiry system according to claim 1, wherein the XGboost multi-classification model is based on an XGboost multi-classification algorithm and is trained by a gradient boosting method, the iterator type is gbtree, and the loss function is in a Mean Square Error (MSE) form.
6. The XGboost disease prediction-based intelligent inquiry system according to claim 1, wherein the server further comprises an association information module;
the associated information module is used for giving out disease associated information according to the predicted disease information and sending the disease associated information to a client side for displaying, wherein the disease associated information comprises disease introduction information, a department of medical guidance, medication suggestions and professional doctor recommendation information which are associated with the predicted disease information.
7. The XGboost disease prediction-based intelligent interrogation system of claim 1, wherein the client further comprises a feedback module;
the feedback module is used for receiving and processing feedback information of the patient, if the feedback problem of the patient is solved, the inquiry is finished, and if the feedback problem of the patient is not solved, the patient is prompted to directly consult a professional doctor.
8. The system of claim 1, wherein the basic information includes gender, age, pregnancy status, past medical history, and medical allergy history.
9. An intelligent inquiry method based on XGboost disease prediction is characterized by comprising the following steps:
the client-side performs question-answer interaction with the patient through a simulated doctor to acquire basic information and symptom information of the patient;
the server side obtains the basic information and symptom information data of the client patient to process;
the server side inputs the processed patient symptom information data into a trained XGboost multi-classification model, outputs a disease probability predicted value and predicted disease information and sends the disease probability predicted value and the predicted disease information to the client side;
and the client receives and displays the predicted disease information and the predicted value of the disease probability sent by the server.
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