CN117672443A - Physical examination data analysis method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure provides a physical examination data analysis method, a physical examination data analysis device, electronic equipment and a storage medium, and relates to the technical field of medical treatment and health. The method comprises the following steps: determining a plurality of features to be screened according to physical examination data of a patient with confirmed diagnosis of the target disease; determining the association degree information between each feature to be screened and the target disease; screening the features to be screened according to the association degree information to obtain at least one target feature; and performing physical examination data analysis according to the target characteristics to obtain an analysis result. The method comprises the steps of determining a plurality of characteristics to be screened from physical examination data of a patient diagnosed with a target disease, and screening the characteristics according to association degree information between the characteristics to be screened and the target disease, so as to obtain target characteristics highly related to the target disease. The method and the device realize that the object disease probability is obtained by analyzing the conventional physical examination data, the utilization rate of the physical examination data is improved, and the accuracy and the reliability of disease evaluation can be improved.
Description
Technical Field
The disclosure relates to the technical field of medical health, in particular to a physical examination data analysis method, a physical examination data analysis device, electronic equipment and a storage medium.
Background
Along with the increasing of the life pressure of people, people also face the trouble of various diseases. If many diseases can be found and therapeutic means can be adopted in time in early stage, the disease development can be effectively avoided, and the physical and economic burden of patients can be reduced. However, the symptoms of a part of the disease are not obvious at the early stage of the disease, and the part of the disease is evaluated by physical examination data in the related art.
However, since people often perform physical examination only aiming at basic physical examination indexes during physical examination, the accuracy and reliability of evaluating partial diseases through physical examination data are low.
Therefore, a method for analyzing the probability of a disease of a target disease through physical examination data is needed to improve the accuracy and reliability of physical examination data analysis.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure provides a physical examination data analysis method, a physical examination data analysis device, an electronic device and a storage medium, which at least overcome the problem that the accuracy and the reliability of the evaluation of partial diseases through physical examination data in the related technology are low to a certain extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to an aspect of the embodiments of the present disclosure, there is provided a physical examination data analysis method, including: determining a plurality of features to be screened according to physical examination data of a patient with confirmed diagnosis of the target disease; determining the association degree information between each feature to be screened and the target disease; screening the features to be screened according to the association degree information to obtain at least one target feature; and performing physical examination data analysis according to the target characteristics to obtain an analysis result.
In some embodiments of the present disclosure, determining a plurality of features to be screened from physical examination data of a patient diagnosed with a disease of interest comprises: determining an abnormal index list according to physical examination data of the patient with the confirmed diagnosis of the target disease; and determining a plurality of features to be screened according to the abnormal index list.
In some embodiments of the present disclosure, determining a plurality of features to be screened from physical examination data of a patient diagnosed with a disease of interest comprises: and determining a plurality of characteristics to be screened according to the physical examination data of the patient with the confirmed diagnosis of the target disease and a physical examination index list affecting human metabolism.
In some embodiments of the present disclosure, determining association degree information between each feature to be screened and the target disease includes: acquiring a plurality of training sample data, wherein any training sample data comprises two classification labels and corresponding physical examination data; and inputting a plurality of training sample data into a training model to obtain the association degree information between each feature to be screened and the target disease.
In some embodiments of the present disclosure, the physical examination data analysis method provided in the embodiments of the present disclosure further includes: and inputting a plurality of training sample data into the training model to obtain weight information corresponding to at least one target feature, wherein any target feature corresponds to one weight information.
In some embodiments of the present disclosure, determining association degree information between each feature to be screened and the target disease includes: determining the association degree information between each feature to be screened and the target disease based on the following formula:
wherein, hθ (x) is used for representing the result of predicting the disease probability of the target disease according to the feature to be screened, θ is used for representing the correlation degree information, x is used for representing the feature to be screened, and g is used for representing the logic function.
In some embodiments of the present disclosure, performing physical examination data analysis according to the target feature to obtain an analysis result includes: and performing physical examination data analysis based on the following formula to obtain an analysis result:
wherein P is used for representing analysis results which are used for indicating the disease probability of the target disease determined according to the target characteristics, alpha is a constant parameter, and x i For representing the ith target feature, beta i And the weight information is used for representing the weight information corresponding to the ith target feature, and n is used for representing the number of the target features.
In some embodiments of the present disclosure, when the target disease is hyperuricemia, the target characteristic comprises at least one of a gender characteristic, a blood uric acid characteristic, a body mass index characteristic, a ratio of serum aspartate aminotransferase to alanine aminotransferase characteristic, a triglyceride characteristic, a total cholesterol characteristic, an average hemoglobin characteristic.
According to another aspect of the present disclosure, there is provided a physical examination data analysis device comprising: the to-be-screened characteristic determining module is used for determining a plurality of to-be-screened characteristics according to physical examination data of the patient with the confirmed diagnosis of the target disease; the association degree information determining module is used for determining association degree information between each feature to be screened and the target disease; the target feature determining module is used for screening the features to be screened according to the association degree information to obtain at least one target feature; and the physical examination data analysis module is used for carrying out physical examination data analysis according to the target characteristics to obtain an analysis result.
In some embodiments of the present disclosure, the feature to be screened determining module is configured to determine an abnormal index list according to physical examination data of a patient diagnosed with a target disease; and determining a plurality of features to be screened according to the abnormal index list.
In some embodiments of the present disclosure, the feature to be screened determining module is configured to determine a plurality of features to be screened according to physical examination data of the patient diagnosed with the target disease and a physical examination index list affecting metabolism of the human body.
In some embodiments of the present disclosure, the association degree information determining module is configured to obtain a plurality of training sample data, where any training sample data includes a two-class label and corresponding physical examination data; and inputting a plurality of training sample data into a training model to obtain the association degree information between each feature to be screened and the target disease.
In some embodiments of the present disclosure, the physical examination data analysis device provided in the embodiments of the present disclosure further includes: the weight information determining module is used for inputting a plurality of training sample data into the training model to obtain weight information corresponding to at least one target feature, wherein any one target feature corresponds to one weight information.
In some embodiments of the present disclosure, the association degree information determining module is configured to determine association degree information between each feature to be screened and the target disease based on the following formula:
wherein, hθ (x) is used for representing the result of predicting the disease probability of the target disease according to the feature to be screened, θ is used for representing the correlation degree information, x is used for representing the feature to be screened, and g is used for representing the logic function.
In some embodiments of the present disclosure, the physical examination data analysis module is configured to perform physical examination data analysis based on the following formula, to obtain an analysis result:
wherein P is used for representing analysis results which are used for indicating the disease probability of the target disease determined according to the target characteristics, alpha is a constant parameter, and x i For representing the ith target feature, beta i And the weight information is used for representing the weight information corresponding to the ith target feature, and n is used for representing the number of the target features.
In some embodiments of the present disclosure, when the target disease is hyperuricemia, the target characteristic comprises at least one of a gender characteristic, a blood uric acid characteristic, a body mass index characteristic, a ratio of serum aspartate aminotransferase to alanine aminotransferase characteristic, a triglyceride characteristic, a total cholesterol characteristic, an average hemoglobin characteristic.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the physical examination data analysis method described above via execution of the executable instructions.
According to still another aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described physical examination data analysis method.
According to another aspect of the present disclosure, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the physical examination data analysis method provided in the various alternative ways in any of the embodiments of the present disclosure.
According to the technical scheme provided by the embodiment of the disclosure, the characteristics to be screened are determined from the physical examination data of the patient with the confirmed diagnosis of the target disease, and the characteristics are screened according to the association degree information between the characteristics to be screened and the target disease, so that the target characteristics highly related to the target disease are obtained. The embodiment of the disclosure realizes that the object disease probability is obtained by analyzing the conventional physical examination data, improves the utilization rate of the physical examination data, and can improve the accuracy and reliability of disease evaluation.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 shows a schematic diagram of a system architecture in an embodiment of the present disclosure;
FIG. 2 illustrates a flow chart of a method of analyzing physical examination data in an embodiment of the disclosure;
FIG. 3 illustrates a flow chart of a method of analyzing physical examination data in an embodiment of the disclosure;
FIG. 4 shows a schematic representation of an AUC in an embodiment of the disclosure;
FIG. 5 is a schematic diagram of a physical examination data analysis device according to an embodiment of the present disclosure;
FIG. 6 shows a block diagram of an electronic device in an embodiment of the disclosure;
fig. 7 shows a schematic diagram of a computer-readable storage medium in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
FIG. 1 illustrates an exemplary system architecture diagram to which physical examination data analysis in embodiments of the present disclosure may be applied. As shown in fig. 1, the system architecture may include a terminal device 101, a network 102, and a server 103.
Wherein the terminal device 101 may determine a plurality of features to be screened according to physical examination data of the patient diagnosed with the target disease. The terminal device 101 may then send the plurality of features to be screened to the server 103. The server 103 may determine association degree information between each feature to be screened and the target disease, and screen the feature to be screened according to the association degree information to obtain at least one target feature. Finally, the server 103 may perform physical examination data analysis according to the target feature, to obtain an analysis result. In addition, the server 103 may also transmit the analysis result to the terminal apparatus 101.
The medium used by the network 102 to provide a communication link between the terminal device 101 and the server 103 may be a wired network or a wireless network.
Alternatively, the wireless network or wired network described above uses standard communication techniques and/or protocols. The network is typically the Internet, but may be any network including, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wired or wireless network, private network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including HyperText Mark-up Language (HTML), extensible markup Language (Extensible MarkupLanguage, XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as secure sockets layer (Secure Socket Layer, SSL), transport layer security (Transport Layer Security, TLS), virtual private network (Virtual Private Network, VPN), internet security protocol (Internet Protocol Security, IPSec), etc. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above.
The terminal device 101 may be a variety of electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, smart speakers, smart watches, wearable devices, augmented reality devices, virtual reality devices, and the like.
The server 103 may be a server providing various services, such as a background management server providing support for devices operated by the user with the terminal apparatus 101. The background management server can analyze and process the received data such as the request and the like, and feed back the processing result to the terminal equipment.
Optionally, the server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like.
Those skilled in the art will appreciate that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative, and that any number of terminal devices, networks, and servers may be provided as desired. The embodiments of the present disclosure are not limited in this regard.
The present exemplary embodiment will be described in detail below with reference to the accompanying drawings and examples.
Under the system architecture, the embodiment of the disclosure provides a physical examination data analysis method, which can be executed by any electronic device with computing processing capability.
In some embodiments, the physical examination data analysis method provided in the embodiments of the present disclosure may be executed by the terminal device of the above system architecture; in other embodiments, the physical examination data analysis method provided in the embodiments of the present disclosure may be performed by a server in the system architecture described above; in other embodiments, the physical examination data analysis method provided in the embodiments of the present disclosure may be implemented by the terminal device and the server in the system architecture in an interactive manner.
Fig. 2 shows a flowchart of a physical examination data analysis method according to an embodiment of the present disclosure, and as shown in fig. 2, the physical examination data analysis method provided in the embodiment of the present disclosure includes the following steps S202 to S208.
S202, determining a plurality of characteristics to be screened according to physical examination data of a patient with confirmed diagnosis of the target disease.
The disclosed embodiments are not limited to the target disease, which may be diabetes, hyperuricemia, polycythemia kidney disease, etc., by way of example.
The embodiment of the disclosure also does not limit the content of the physical examination data, and illustratively, the physical examination data may include a plurality of physical examination indexes, and the physical examination indexes may be determined based on application scenes or experience. For example, the physical examination index may include at least one of gender, age, blood uric acid, body mass index, ratio of serum aspartic acid aminotransferase to alanine aminotransferase, triglycerides, total cholesterol, mean hemoglobin, high density lipoprotein cholesterol, serum creatinine, fasting blood glucose, low density lipoprotein cholesterol, diastolic blood pressure, urea nitrogen, uric acid, blood uric anhydride, urine protein, glomerular filtration rate, blood urea, blood uric acid nitrogen, serum calcium, serum phosphorus, serum sodium, serum potassium.
In an exemplary embodiment, each physical examination index included in the physical examination data may be used as a feature to be screened. Alternatively, physical examination data of each patient whose target disease is diagnosed may be analyzed to screen an abnormal index list in which the results show abnormality, and the abnormal index list in which each result shows abnormality is used as the feature to be screened. In this case, determining a plurality of features to be screened based on physical examination data of the patient diagnosed with the disease of interest, comprising: determining an abnormal index list according to physical examination data of the patient with the confirmed diagnosis of the target disease; and determining a plurality of features to be screened according to the abnormal index list.
In one possible embodiment, the physical examination data may be analyzed by a statistical multiple regression analysis method to screen for physical examination indicators in which the results show abnormalities.
It should be noted that, the embodiment of the disclosure can collect and count physical examination data of people in all ages, and improves the rationality of data collection in social demographic characteristics. In addition, the embodiment of the disclosure eliminates the statistical index of non-medical examination, non-diet, non-physical environment, such as education level, when collecting physical examination data, wherein the non-physical environment comprises but is not limited to living environment, geographical environment and the like. Therefore, the embodiment of the disclosure can improve the accuracy and reliability of the features to be screened.
In some embodiments, determining a plurality of features to be screened from physical examination data of a patient diagnosed with a disease of interest comprises: and determining a plurality of characteristics to be screened according to the physical examination data of the patient with the confirmed diagnosis of the target disease and a physical examination index list affecting human metabolism.
In one possible implementation, the physical examination index list affecting the metabolism of the human body can be screened based on the literature, so as to obtain the physical examination index list affecting the metabolism of the human body.
It should be noted that, the physical examination index list may be determined according to the type of the target disease, so that the feature factor directly related to the target disease or indirectly related to the first level is used as the feature to be screened. For example, if the target disease is hyperuricemia, since hyperuricemia is mainly related to metabolism, all factors related to human metabolism and all characteristic factors related to purine diets can be taken as characteristics to be screened.
For example, after the physical examination index list affecting the human body is obtained, each physical examination index in the physical examination data of the patient with confirmed diagnosis of the target disease and each physical examination index in the physical examination index list affecting the human body metabolism may be de-overlapped, so as to obtain each feature to be screened.
S204, determining the association degree information between each feature to be screened and the target disease.
In some embodiments, determining association degree information between each feature to be screened and the target disease comprises: acquiring a plurality of training sample data, wherein any training sample data comprises two classification labels and corresponding physical examination data; and inputting a plurality of training sample data into a training model to obtain the association degree information between each feature to be screened and the target disease.
In an exemplary embodiment, any of the training sample data may be physical examination data of any of the subjects, and the plurality of training sample data includes physical examination data of subjects suffering from the target disease and physical examination data of subjects not suffering from the target disease.
In an exemplary embodiment, after a plurality of training sample data are acquired, each training sample data may be subjected to data preprocessing. Illustratively, the data preprocessing may include at least one of format unification, data cleansing, data sorting, etc.
For example, because the collected training sample data may not be organized, the format of the individual training sample data may not be uniform, e.g., the format of the training sample data may include, for example, pictures, pdfs, etc. The training sample data needs to be uniformly identified and processed into structured data.
Illustratively, after the structured data corresponding to each training sample data is obtained, the structured data may be cleaned to remove outliers and to remove or complement missing values. In addition, after the structured data is cleaned, the value range conversion, namely the data normalization, can be performed. For example, the description of the sex as "woman" in the data source may be "woman", "1", "0", etc., and may be unified at this time, for example, the sex as "woman" may be uniformly expressed as "1". Meanwhile, the data is normalized, for example, the height units can be unified into centimeters.
After a plurality of training sample data are obtained, the data preprocessing is performed on each training sample data, so that the accuracy of the subsequent model training can be improved, and the accuracy and the reliability of physical examination data analysis can be improved.
In some embodiments, each training sample data may be labeled with a classification tag that may characterize whether the corresponding training sample data is from a subject with the target disease. And then, inputting the data of each training sample into a two-class logistic regression model to perform iterative training of the model, and finally obtaining the target characteristics.
In some exemplary embodiments, determining association degree information between each feature to be screened and the target disease includes: determining the association degree information between each feature to be screened and the target disease based on the following formula:
in the formula (1), hθ (x) may be used to represent a result of predicting the probability of a target disease from a feature to be screened, θ may be used to represent association degree information, x may be used to represent a feature to be screened, and g may be used to represent a logic function.
Illustratively, the absolute value magnitude of θ may represent the degree of sensitivity of the corresponding feature to be screened to the target disease. The value of θ may be positive or negative. Wherein positive numbers represent positive effects and negative numbers represent negative effects. The larger the absolute value of θ, the larger the contribution of the corresponding feature to be filtered to the predicted value hθ (x) is indicated. In addition, the logic function may be a sigmoid function.
Illustratively, the hypothesis functions included in the above-mentioned two-classification logistic regression model may be as shown in the formula (1). After each training sample data is input into the two-classification logistic regression model, the loss function can be minimized and the model parameters θ estimated by using GBDT (Gradient Boosting Decison Tree, gradient-lifting decision tree) gradient descent method. And iteratively updating parameters until the loss function converges, so that the final association degree information corresponding to each feature to be screened can be obtained.
In an exemplary embodiment, the training sample data may be divided into a training set and a testing set, and the data of the training set and the testing set are fused together and input into a two-classification logistic regression model for training. The embodiment of the disclosure does not limit the proportion of the training set to the test set, and illustratively, the proportion of the training set to the test set may be 7:3 or 8:2.
in some embodiments, the training sets may be respectively: test set = 7:3, training set: test set = 8:2, and carrying out experiments according to experimental effects. Illustratively, when a training set is employed: test set = 8:2, the training effect obtained by model training by the scheme of the method is better, and the method can be carried out according to 8: the ratio of 2 performs division of training sample data.
Illustratively, 10 ten thousand pieces of data may be included in the training sample data, if a training set is employed: test set = 8:2, 8 ten thousand are training sets and 2 ten thousand are test sets. Then, 10 ten thousand pieces of data can be input into the two-class logistic regression model together for training, and the training is iterated for a plurality of times to obtain a model training result.
For example, an iterative training end condition may be preset, and when the iterative training end condition is satisfied, the iterative training is ended. For example, the iterative training ending condition may be a preset number of iterations, and the preset number of iterations may be set to 20, 50, 100, or the like, for example. In this case, when the number of times of the current iterative training is not less than the preset number of iterations, the iterative training may be ended. It should be noted that the maximum number of iterations allowed by the two-classification logistic regression model algorithm may be 100.
For another example, the iterative training ending condition may be that at least one of the model accuracy and AUC (Area Under the Curve, model evaluation index) reaches a corresponding preset threshold. For example, the preset threshold corresponding to the accuracy of the model may be 0.6, 0.65, 0.7, etc., and the preset threshold corresponding to the AUC may be 0.6, 0.65, 0.7, etc.
In some embodiments, the physical examination data analysis method provided in the embodiments of the present disclosure may further include: and inputting a plurality of training sample data into the training model to obtain weight information corresponding to at least one target feature, wherein any target feature corresponds to one weight information.
In an exemplary embodiment, the weight information corresponding to any target feature may be determined based on the association degree information corresponding to the target feature. For example, the association degree information corresponding to a certain target feature may be proportional to the weight information corresponding to the target feature.
Illustratively, each weight information may take a decimal number greater than zero and less than 1, and the sum of the weight information may be 1.
S206, screening the features to be screened according to the association degree information to obtain at least one target feature.
In some exemplary embodiments, a threshold of association degree may be set, and at least one feature to be screened whose corresponding association degree information is greater than or not less than the association degree condition is taken as a target feature.
It should be noted that, the embodiment of the present disclosure does not limit the magnitude of the association degree threshold, and the value of the association degree threshold may be set based on experience or an application scenario.
The disclosed embodiments do not limit the number of target features. In some embodiments, when the target disease is hyperuricemia, the target characteristic may include at least one of a gender characteristic, a blood uric acid characteristic, a body mass index characteristic, a ratio of serum aspartate aminotransferase to alanine aminotransferase characteristic, a triglyceride characteristic, a total cholesterol characteristic, an average hemoglobin characteristic.
S208, performing physical examination data analysis according to the target characteristics to obtain an analysis result.
In some embodiments, the above-described two-classification logistic regression model may output model formulas in addition to the degree of association information. Wherein the output model formula can be used to perform analysis of physical examination data. The model formula may be as shown in formula (2). In this case, the analysis result may be determined by predicting the baseline probability of the target, the respective target features, and the weight information corresponding to the respective target features.
Illustratively, performing physical examination data analysis according to the target feature to obtain an analysis result may include: and (3) performing physical examination data analysis based on the following formula (2) to obtain an analysis result.
Where P is used to represent an analysis result that may be used to indicate the probability of a disease of the target as determined from the target characteristics. Alpha is a constant parameter. Illustratively, α may be used to represent a baseline probability of predicting the target when all feature factors are 0, and α and the various weight information are combined to obtain a final predicted probability. Alpha can be calculated by a logistic regression algorithm. X is x i For representing the ith target feature, beta i And the weight information is used for representing the weight information corresponding to the ith target feature, and n is used for representing the number of the target features.
In some embodiments, if the target characteristic is a gender characteristic, a blood uric acid characteristic, a body mass index characteristic, a ratio of serum aspartate aminotransferase to alanine aminotransferase characteristic, a triglyceride characteristic, a total cholesterol characteristic, an average hemoglobin characteristic. At this time, n=7, and the model formula in this case can be shown as the following formula (3).
In the formula (3), β 1 To beta 7 Namely, the weight information corresponding to the 7 target features can be respectively represented, and x 1 To x 7 The 7 target features described above may be represented correspondingly. P (y= 1|X =x) is used to represent the probability of suffering from the target disease calculated when the target feature is the above 7 features. Y=1 is used to denote having the disease of interest.
In an exemplary embodiment, continuous optimization iterations of the analytical model may continue after the analytical results are obtained. By way of example, the embodiment of the disclosure can add more features to be screened in continuous iterative training of the model to perform upgrading iteration of the model, so that the performance of the model is improved. For example, when the target disease is hyperuricemia, the added feature to be screened may be, for example, a physical examination index affecting kidney function, a metabolic abnormality characteristic factor caused by a non-liver cause, or the like.
Examples of the physical examination index list that generally affects kidney metabolism include creatinine, urea nitrogen, uric acid, blood urea anhydride, urine protein, glomerular filtration rate, blood urea, blood uric acid nitrogen, serum calcium, serum phosphorus, serum sodium, serum potassium, and the like.
In addition, the model in the embodiment of the disclosure can improve the accuracy and the AUC of the model by continuously expanding the number of samples, thereby improving the accuracy and the reliability of physical examination data analysis.
According to the method provided by the embodiment of the disclosure, the characteristics to be screened are determined from the physical examination data of the patient with the confirmed diagnosis of the target disease, and the characteristics are screened according to the association degree information between the characteristics to be screened and the target disease, so that the target characteristics highly related to the target disease are obtained. The embodiment of the disclosure realizes that the object disease probability is obtained by analyzing the conventional physical examination data, improves the utilization rate of the physical examination data, and can improve the accuracy and reliability of disease evaluation.
In an exemplary embodiment, fig. 3 shows a flowchart of a physical examination data analysis method provided in an embodiment of the disclosure. As shown in fig. 3, the physical examination data analysis method may include the following steps S302 to S316.
S302, collecting characteristic data and label data.
Illustratively, the characteristic data is contained in the training sample data described above. The label data is the two-class label, and is used for representing whether the corresponding training sample data is from a subject suffering from the target disease.
S304, data preprocessing and labeling of the two kinds of labels.
Illustratively, the data preprocessing may include at least one of format unification, data cleansing, data sorting, etc.
S306, selecting the features to be screened.
For example, the feature to be screened may be included in physical examination data of a patient diagnosed with the target disease and a list of physical examination indexes affecting human metabolism.
S308, data set division.
For example, the collected training sample data may be used as a data set and divided into a training set and a test set.
S310, model construction and training.
In one possible implementation, each training sample data after data preprocessing and labeling of the classification labels can be input into a constructed model and trained, and the model can be a classification logistic regression model.
S312, outputting a model formula.
S314, evaluating the model.
For example, the model evaluation may be performed by at least one indicator of the accuracy and/or AUC of the model. In one possible implementation manner, the model accuracy and the preset threshold corresponding to AUC may be both set to 0.65, and if the model accuracy is 0.75 and the AUC is 0.71 at this time, the model formula output by the model at this time may be considered to have a higher fitting degree and external utility.
S316, the model continues optimization iteration.
It should be noted that, the implementation manner of steps S302 to S316 may be referred to the corresponding descriptions in steps S202 to S208, and will not be repeated here.
In one possible implementation, a schematic diagram of an AUC provided by an example of the present disclosure may be as shown in fig. 4.
In fig. 4, the X-axis is used to represent FPR (False Postive Rate, negative positive class rate) and the Y-axis is used to represent TPR (True Postive Rate, true class rate). In fig. 4, a ROC (Receiver Operating characteristic, subject working characteristic) curve is shown, the whole graph is divided into two parts according to the curve position, and the area of the lower part of the ROC curve is AUC. The AUC may be used to represent prediction accuracy, with higher AUC values, i.e., larger area under the ROC curve, indicating higher prediction accuracy. The closer the curve is to the upper left corner (the smaller X, the larger Y), the higher the prediction accuracy. Illustratively, as shown in fig. 4, the AUC may take a value of 0.71.
Taking target diseases as hyperuricemia as an example, the embodiment of the disclosure can start from the essence (namely, poor metabolism) of the diseases, and probe the characteristic factors of the diseases, so that the evaluation of the diseases is more scientific and objective. Moreover, the crowd distribution of the acquired samples by utilizing the characteristics of the Internet medical platform is more reasonable, so that the accuracy and the reliability of evaluating partial diseases through physical examination data can be improved.
It should be noted that, in the technical solution of the present disclosure, the acquiring, storing, using, processing, etc. of data all conform to relevant regulations of national laws and regulations, and various types of data such as personal identity data, operation data, behavior data, etc. relevant to individuals, clients, crowds, etc. acquired in the embodiments of the present disclosure have been authorized.
Based on the same inventive concept, a physical examination data analysis device is also provided in the embodiments of the present disclosure, as described in the following embodiments. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.
Fig. 5 shows a schematic diagram of a physical examination data analysis device according to an embodiment of the disclosure, as shown in fig. 5, the device includes:
The feature to be screened determining module 501 is configured to determine a plurality of features to be screened according to physical examination data of a patient diagnosed with a target disease;
the association degree information determining module 502 is configured to determine association degree information between each feature to be screened and the target disease;
the target feature determining module 503 is configured to screen the feature to be screened according to the association degree information, so as to obtain at least one target feature;
and the physical examination data analysis module 504 is configured to perform physical examination data analysis according to the target feature, so as to obtain an analysis result.
In some embodiments of the present disclosure, the feature to be screened determining module 501 is configured to determine an abnormal index list according to physical examination data of a patient diagnosed with a target disease; and determining a plurality of features to be screened according to the abnormal index list.
In some embodiments of the present disclosure, the feature to be screened determining module 501 is configured to determine a plurality of features to be screened according to physical examination data of the patient diagnosed with the target disease and a physical examination index list affecting metabolism of a human body.
In some embodiments of the present disclosure, the association degree information determining module 502 is configured to obtain a plurality of training sample data, where any training sample data includes a two-class label and corresponding physical examination data; and inputting a plurality of training sample data into a training model to obtain the association degree information between each feature to be screened and the target disease.
In some embodiments of the present disclosure, the physical examination data analysis device provided in the embodiments of the present disclosure further includes:
the weight information determining module is used for inputting a plurality of training sample data into the training model to obtain weight information corresponding to at least one target feature, wherein any one target feature corresponds to one weight information.
In some embodiments of the present disclosure, the association degree information determining module 502 is configured to determine association degree information between each feature to be screened and the target disease based on the following formula:
wherein, hθ (x) is used for representing the result of predicting the disease probability of the target disease according to the feature to be screened, θ is used for representing the association degree information, x is used for representing the feature to be screened, and g is used for representing a logic function.
In some embodiments of the present disclosure, the physical examination data analysis module 504 is configured to perform physical examination data analysis based on the following formula, to obtain an analysis result:
wherein P is used for representing analysis results which are used for indicating the disease probability of the target disease determined according to the target characteristics, alpha is a constant parameter, and x i For representing the ith target feature, beta i And the weight information is used for representing the weight information corresponding to the ith target feature, and n is used for representing the number of the target features.
In some embodiments of the present disclosure, when the target disease is hyperuricemia, the target characteristic comprises at least one of a gender characteristic, a blood uric acid characteristic, a body mass index characteristic, a ratio of serum aspartate aminotransferase to alanine aminotransferase characteristic, a triglyceride characteristic, a total cholesterol characteristic, an average hemoglobin characteristic.
The device provided by the embodiment of the disclosure determines a plurality of features to be screened from physical examination data of a patient diagnosed with a target disease, and performs feature screening according to association degree information between the features to be screened and the target disease, thereby obtaining target features highly related to the target disease. The embodiment of the disclosure realizes that the object disease probability is obtained by analyzing the conventional physical examination data, improves the utilization rate of the physical examination data, and can improve the accuracy and reliability of disease evaluation.
It should be noted that, the feature determining module 501 to be filtered, the association degree information determining module 502, the target feature determining module 503, and the physical examination data analyzing module 504 correspond to S202 to S208 in the method embodiment, and the above modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the method embodiment. It should be noted that the modules described above may be implemented as part of an apparatus in a computer system, such as a set of computer-executable instructions.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
The disclosed embodiments provide an electronic device, which illustratively includes: a processor and a memory. The memory may be used to store executable instructions for the processor. Wherein the processor is configured to provide the physical examination data analysis method according to the embodiment of the present disclosure via execution of the executable instructions.
An electronic device 600 according to such an embodiment of the present disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, and a bus 630 that connects the various system components, including the memory unit 620 and the processing unit 610.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 610 may perform the following steps of the method embodiment described above: determining a plurality of features to be screened according to physical examination data of a patient with confirmed diagnosis of the target disease; determining the association degree information between each feature to be screened and the target disease; screening the features to be screened according to the association degree information to obtain at least one target feature; and performing physical examination data analysis according to the target characteristics to obtain an analysis result.
The storage unit 620 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 640 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. As shown, network adapter 660 communicates with other modules of electronic device 600 over bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In particular, according to embodiments of the present disclosure, the process described above with reference to the flowcharts may be implemented as a computer program product comprising: and a computer program which, when executed by the processor, implements the physical examination data analysis method described above.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a computer program, which when executed by a processor, may implement the physical examination data analysis method provided by the embodiments of the present disclosure. The computer readable storage medium may be a readable signal medium or a readable storage medium.
Fig. 7 illustrates a schematic diagram of a computer-readable storage medium in an embodiment of the present disclosure, as shown in fig. 7, on which a program product capable of implementing the method of the present disclosure is stored 700. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
More specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In this disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Alternatively, the program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In particular implementations, the program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the description of the above embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims.
Claims (11)
1. A method for analyzing physical examination data, comprising:
determining a plurality of features to be screened according to physical examination data of a patient with confirmed diagnosis of the target disease;
determining the association degree information between each feature to be screened and the target disease;
screening the features to be screened according to the association degree information to obtain at least one target feature;
and performing physical examination data analysis according to the target characteristics to obtain an analysis result.
2. The method of claim 1, wherein determining a plurality of features to be screened based on physical examination data of the patient diagnosed with the target disease comprises:
Determining an abnormal index list according to physical examination data of the patient with the confirmed diagnosis of the target disease;
and determining a plurality of features to be screened according to the abnormal index list.
3. The method of claim 1, wherein determining a plurality of features to be screened based on physical examination data of the patient diagnosed with the target disease comprises:
and determining a plurality of characteristics to be screened according to the physical examination data of the target disease diagnosis patient and the physical examination index list affecting human metabolism.
4. The method for analyzing physical examination data according to claim 1, wherein the determining the degree of association information between each feature to be screened and the target disease comprises:
acquiring a plurality of training sample data, wherein any training sample data comprises two classification labels and corresponding physical examination data;
and inputting a plurality of training sample data into a training model to obtain the association degree information between each feature to be screened and the target disease.
5. The method of claim 4, further comprising:
and inputting a plurality of training sample data into the training model to obtain weight information corresponding to at least one target feature, wherein any target feature corresponds to one weight information.
6. The method for analyzing physical examination data according to claim 1, 4 or 5, wherein the determining the degree of association information between each feature to be screened and the target disease comprises:
determining the association degree information between each feature to be screened and the target disease based on the following formula:
wherein, hθ (x) is used for representing the result of predicting the disease probability of the target disease according to the feature to be screened, θ is used for representing the association degree information, x is used for representing the feature to be screened, and g is used for representing a logic function.
7. The physical examination data analysis method according to claim 1 or 5, wherein the performing physical examination data analysis according to the target feature to obtain an analysis result comprises:
and performing physical examination data analysis based on the following formula to obtain an analysis result:
wherein P is used for representing analysis results, the analysis results are used for indicating the disease probability of the target disease determined according to the target characteristics, alpha is a constant parameter, and x i For representing the ith target feature, beta i And the weight information is used for representing the weight information corresponding to the ith target feature, and n is used for representing the number of the target features.
8. The method according to claim 1, wherein when the target disease is hyperuricemia, the target characteristic comprises at least one of a sex characteristic, a blood uric acid characteristic, a body mass index characteristic, a ratio characteristic of serum aspartate aminotransferase to alanine aminotransferase, a triglyceride characteristic, a total cholesterol characteristic, and an average hemoglobin characteristic.
9. A physical examination data analysis device, comprising:
the to-be-screened characteristic determining module is used for determining a plurality of to-be-screened characteristics according to physical examination data of the patient with the confirmed diagnosis of the target disease;
the association degree information determining module is used for determining association degree information between each feature to be screened and the target disease;
the target feature determining module is used for screening the features to be screened according to the association degree information to obtain at least one target feature;
and the physical examination data analysis module is used for carrying out physical examination data analysis according to the target characteristics to obtain an analysis result.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the physical examination data analysis method of any one of claims 1 to 8 via execution of the executable instructions.
11. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the physical examination data analysis method of any one of claims 1 to 8.
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