CN115831300B - Detection method, device, equipment and medium based on patient information - Google Patents
Detection method, device, equipment and medium based on patient information Download PDFInfo
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Abstract
The technical scheme mainly utilizes sample detection information of a patient and detection information of family members, sets information weights according to the relatives of the family members, respectively inputs the information weights into a generation model and a discrimination model, and corrects the detection result of the detection information of the patient by utilizing the detection information of the family members, so that the accuracy of the detection result of the patient can be improved.
Description
Technical Field
The invention relates to a detection method, device, equipment and medium based on patient information, and belongs to the technical field of intelligent medical treatment.
Background
At present, the detection method of patient information is mainly to detect and analyze according to the detection result of a patient in clinic, however, due to imperfect detection items of the patient, it is impossible to detect all detection items, so that the detection result of the patient has some deviation from the actual situation, in the prior art, for some diseases with genetic properties, the information of the family can be obtained for analysis, however, the method is limited by the time limit of finding out the detection information of the family, and the method of analyzing the condition of the family is not available in the prior art, which results in no wide application.
Therefore, finding a method, a device, equipment and a medium capable of quickly finding and acquiring detection information of a family and analyzing conditions of the family is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a detection method capable of supplementing detection information of a patient according to the detection information of the family member of the patient, and the detection result of the patient can be corrected according to the detection information of the patient and the detection information of the family member, so that the accuracy of the detection result of the patient is greatly improved.
According to an embodiment of the present invention, there is provided a first aspect of: a patient information based detection method comprising the steps of:
acquiring sample detection information of a patient to be detected; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
when the first detection information contains target detection information, acquiring second detection information of the to-be-detected item corresponding to each family member in family information of the patient based on the sample detection information;
Setting information weight for each second detection information according to the family member and patient relative relation and a preset relative relation and weight corresponding table;
inputting the first detection information into a generation model, and inputting the second detection information and the corresponding information weight into a preset judgment model; the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
and correcting the result output by the generating model through the output result of the judging model to obtain the detection result output by the generating model.
Further, as a more preferable embodiment of the present invention, the step of inputting the first detection information into a generation model and inputting the second detection information and the corresponding information weight into a preset discrimination model includes:
acquiring a detection information sample set; the detection information sample set comprises first sample detection information, second sample detection information, weight values corresponding to the second sample detection information and sample detection results corresponding to the patients;
Detecting the first sample with information v 1 Inputting the optimal predicted value r into an initial generation model i The sample detection result r true Inputting the initial generation model through a formulaInitial training is carried out on the initial generation model, and a trained temporary predicted value r is obtained j And an intermediate generation model, which is used for generating a model,
multiplying the second sample detection information by the corresponding weight value to obtain input information v 2 Inputting the input information v 2 Input into the initial discrimination model by the formulaInitial training is carried out on the initial discrimination model to obtainAn intermediate discrimination model; wherein (1)>θ represents the parameter set of the generative model, +.>Parameter set representing discriminant model g θ (r i |v 1 ,r true ) Represented by θ as a coefficient, r i 、v 1 、r true For a first preset function of the parameter, +.>Expressed as +.>Is the coefficient, r i 、v 2 、r true A second preset function of the parameter;
according to the formulaPerforming secondary training on the intermediate generation model and the intermediate discrimination model, and obtaining the generation model and the discrimination model after training is completed; wherein->Represents that the minimum value of θ is taken on the premise of satisfying the above formula +.>Maximum value of O G,D Represents taking the minimum value of θ +.>Indicated by the maximum value of (2).
Further, as a more preferred embodiment of the present invention, the method further comprises the steps of:
Inputting the first sample detection information into a trained generation model, inputting the second sample detection information and corresponding information weight into a trained discrimination model, and correcting the result output by the trained generation model through the output result of the trained discrimination model to obtain a prediction detection result output by the generation model;
obtaining a comprehensive loss value of a generated model after training and a discrimination model after training according to the prediction detection result and the sample detection result;
judging whether the comprehensive loss value is smaller than a preset loss value or not;
if yes, judging that the generated model after training and the judging model after training meet the training requirements.
Further, as a more preferable embodiment of the present invention, the step of acquiring, when the first detection information contains target detection information, second detection information of each family member corresponding to the item to be detected, from family information of the patient based on the sample detection information, includes:
sending a family-related membership information acquisition request to a public security system based on the identity information of the patient;
And receiving family-related membership information fed back by the public security system, and finding out second detection information corresponding to the item to be detected based on the family-related membership information.
Further, as a more preferred embodiment of the present invention, the method further comprises the steps of:
initiating an authentication request to the patient based on the identity information of the patient;
if the authentication request passes, the request of executing the step of acquiring the second detection information of the item to be detected corresponding to each family member in the family information of the patient based on the sample detection information when the first detection information contains the target detection information is judged to be satisfied.
Further, as a more preferred embodiment of the present invention, the method further comprises the steps of:
acquiring evaluation content of a detection result output by a hospital doctor on the generated model;
analyzing the evaluation by adopting an emotion analysis tool to obtain adjectives representing emotion tendencies and emotion polarity values thereof;
when the emotion polarity value is negative evaluation, setting a corresponding parameter adjustment amplitude according to the emotion polarity value; wherein the emotion polarity value and the parameter adjustment amplitude are in one-to-one correspondence;
And adjusting parameters in the generation model and the judgment model according to the parameter adjustment amplitude until the evaluation content of the hospital doctor is positive evaluation.
Further, as a more preferable embodiment of the present invention, the step of acquiring sample test information of a patient to be tested includes:
acquiring a platform database in which the sample detection information is located;
and acquiring sample detection information in the platform database through the sqoop script.
According to an embodiment of the present invention, there is provided a second aspect of: a patient information based detection device comprising the following modules:
the first acquisition module is used for acquiring sample detection information of a patient to be detected; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
the second acquisition module is used for acquiring second detection information of the to-be-detected item corresponding to each family member in family information of the patient based on the sample detection information when the first detection information contains target detection information;
the setting module is used for setting information weight for each piece of second detection information according to the relative relation between the family member and the patient and a preset relative relation and weight corresponding table;
The input module is used for inputting the first detection information into a generation model, and inputting the second detection information and the corresponding information weight into a preset judgment model; the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
and the correction module is used for correcting the result output by the generation model through the output result of the discrimination model to obtain the detection result output by the generation model.
A computer-readable storage medium, comprising: a computer program is stored which, when executed by a processor, causes the processor to perform the steps of:
acquiring sample detection information of a patient to be detected; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
when the first detection information contains target detection information, acquiring second detection information of the to-be-detected item corresponding to each family member in family information of the patient based on the sample detection information;
setting information weight for each second detection information according to the family member and patient relative relation and a preset relative relation and weight corresponding table;
Inputting the first detection information into a generation model, and inputting the second detection information and the corresponding information weight into a preset judgment model; the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
and correcting the result output by the generating model through the output result of the judging model to obtain the detection result output by the generating model.
Compared with the prior art, the technical scheme mainly utilizes the sample detection information of the patient and the detection information of the family members, sets information weight according to the relatives of the family members, respectively inputs the information weight into the generation model and the discrimination model, and corrects the detection result of the detection information of the patient by utilizing the detection information of the family members, so that the accuracy of the detection result of the patient can be improved.
Drawings
FIG. 1 is a flow chart of a method for detecting patient information according to an embodiment of the present invention;
FIG. 2 is a flow chart of a detection method based on patient information according to another embodiment of the present invention;
FIG. 3 is a schematic block diagram of a patient information-based detection device according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the internal structure of a computer device in one embodiment.
Detailed Description
In order to better understand the technical solutions in the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It is noted that when an element is referred to as being "fixed" or "disposed on" another element, it can be directly on the other element or be indirectly disposed on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present application and simplify description, and do not indicate or imply that the devices or components referred to must have a particular orientation, be configured and operated in a particular orientation, and thus should not be construed as limiting the present application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" or "a number" is two or more, unless explicitly defined otherwise.
It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the scope of the present disclosure, since any structural modifications, proportional changes, or dimensional adjustments made by those skilled in the art should not be made in the present disclosure without affecting the efficacy or achievement of the present disclosure.
Referring to fig. 1, according to an embodiment of the present invention, there is provided a first scheme of: a patient information based detection method comprising the steps of:
s1: acquiring sample detection information of a patient to be detected; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
S2: when the first detection information contains target detection information, acquiring second detection information of the to-be-detected item corresponding to each family member in family information of the patient based on the sample detection information;
s3: setting information weight for each second detection information according to the family member and patient relative relation and a preset relative relation and weight corresponding table;
s4: inputting the first detection information into a generation model, and inputting the second detection information and the corresponding information weight into a preset judgment model; the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
s5: and correcting the result output by the generating model through the output result of the judging model to obtain the detection result output by the generating model.
In step S1, sample detection information of the patient to be detected is obtained, where the identity information and the first detection information of the patient may be a barcode, an experiment number, a sample type, an experiment type, etc. of the patient, and the obtaining manner may be input by a related person, or may be directly obtained from the system. In step S2, after the sample detection information is obtained, the family member information of the patient is obtained according to the identity information of the patient, where the family member information may be recorded in advance, or may be directly obtained here, or may be obtained from a public security system according to the patient information, and then after the family member information is obtained, the second detection information is obtained from a database of the hospital. In step S3, the preset relatives are the relatives of the patient and each family member, for example, the relatives of the family are set with a larger weight, the relatives of the alternate are set with a smaller weight, and the weight of the family member of the couple relationship may be set to 0, even the second detection information of the family member is not acquired. In the steps S4-S5, the generated model is responsible for generating the result, but the result is not necessarily accurate, so that the generated model is corrected by adopting the discrimination network, that is, the generated model mainly generates the final result according to the first detection information, the discrimination network inputs the second detection information, and corrects the result of the generated model, wherein the correction mode is that the output result of the generated model is verified through the discrimination network, if the verification is not passed, the output result is fed back to the generated model, parameters in the generated model are changed, and the output result is regenerated until the verification is passed through the discrimination model, and in addition, the specific training mode of the model is described in detail later, and is not repeated here.
Specifically describing, in the embodiment of the present invention, the step S4 of inputting the first detection information into a generation model and inputting the second detection information and the corresponding information weight into a preset discrimination model includes:
s401: acquiring a detection information sample set; the detection information sample set comprises first sample detection information, second sample detection information, weight values corresponding to the second sample detection information and sample detection results corresponding to the patients;
s402: detecting the first sample with information v 1 Inputting the optimal predicted value r into an initial generation model i The sample detection result r true Inputting the initial generation model through a formulaInitial training is carried out on the initial generation model, and a trained temporary predicted value r is obtained j And an intermediate generation model, which is used for generating a model,
multiplying the second sample detection information by the corresponding weight value to obtain input information v 2 Inputting the input information v 2 Input into the initial discrimination model by the formulaPerforming initial training on the initial discrimination model to obtain an intermediate discrimination model; wherein (1)>θ represents the parameter set of the generative model, +. >Parameter set representing discriminant model g θ (r i |v 1 ,r true ) Represented by θ as a coefficient, r i 、v 1 、r true For a first preset function of the parameter, +.>Expressed as +.>Is the coefficient, r i 、v 2 、r true A second preset function of the parameter;
s403: according to the formula Performing secondary training on the intermediate generation model and the intermediate discrimination model, and obtaining the generation model and the discrimination model after training is completed; wherein->Represents that the minimum value of θ is taken on the premise of satisfying the above formula +.>Maximum value of O G,D Represents taking the minimum value of θ +.>Indicated by the maximum value of (2).
It should be noted that, the initial generation model has a random parameter set, which is a pre-constructed parameter set, so that it can normally output results, so as to facilitate training, and the method is based on the formulaIn addition, the training mode can adopt a random gradient descent method for training, update the parameters, namely, after the current sample training is completed, the training of the next sample is performed, and update the parameter set after each training is completed, so that the training of the initial generation model is completed. Similarly, let's go of formula->Training the intermediate discriminant model, updating the parameter set after each training is completed, and training the initial generation model by a random gradient descent method, specifically, according to the formula O G,D ＝min(θ)max(φ){r true [log(d φ (r i |v 6 ,r true ))]+r j [log(1-d φ (r i |v 6 ,r true ))]Synthesizing, and performing secondary training on the initial generation model and the discrimination model, wherein, the description is that,the training of each sample requires training of the above three formulas, i.e., two updates to the sample are required during the training of a set of samples. Finally, the optimal values of the intermediate generation model parameter set theta and the intermediate discrimination model parameter set phi are obtained, and in order to make the discrimination effect of the model better and the obtained medicine composition more accurate, the intermediate generation model parameter set theta should be taken as minimum as possible and the intermediate discrimination model parameter set phi should be taken as maximum.
Specifically, in the embodiment of the invention, the method further comprises the following steps: inputting the first sample detection information into a trained generation model, inputting the second sample detection information and corresponding information weight into a trained discrimination model, and correcting the result output by the trained generation model through the output result of the trained discrimination model to obtain a prediction detection result output by the generation model;
obtaining a comprehensive loss value of a generated model after training and a discrimination model after training according to the prediction detection result and the sample detection result;
Judging whether the comprehensive loss value is smaller than a preset loss value or not;
if yes, judging that the generated model after training and the judging model after training meet the training requirements.
In order to avoid errors caused by the results, it is necessary to input a training-completed generation model and a discrimination model, input the respective first sample detection information into the training-completed generation model, and input the second sample detection information and the corresponding information weight into the training-completed discrimination model, so as to obtain a prediction detection result thereof, where a comprehensive loss value of the generation model and the intermediate discrimination model can be obtained from the prediction detection result and the sample detection result, and the loss value can be calculated by Wherein y is i Represents the detection result of the ith sample, f j (x i ) Representing predicted detection results obtained by detection data corresponding to an ith sample detection result, n representing the number of the sample detection results, ρ representing a preset parameter value, ε i Representing a preset weight value L corresponding to the detection result of the ith sample φ (y i ,f(x i ) Represents the integrated loss value. If the comprehensive loss value is smaller than the preset loss value, the generated model and the judging model are indicated to meet the training requirement, otherwise, training is needed to be continued until the training requirement is met.
Specifically describing, in this embodiment of the present invention, when the first detection information includes target detection information, the step of obtaining, based on the sample detection information, second detection information of each family member corresponding to the item to be detected in family information of the patient includes:
sending a family-related membership information acquisition request to a public security system based on the identity information of the patient;
and receiving family-related membership information fed back by the public security system, and finding out second detection information corresponding to the item to be detected based on the family-related membership information.
Specifically, in the embodiment of the invention, the method further comprises the following steps:
initiating an authentication request to the patient based on the identity information of the patient;
if the authentication request passes, the request of executing the step of acquiring the second detection information of the item to be detected corresponding to each family member in the family information of the patient based on the sample detection information when the first detection information contains the target detection information is judged to be satisfied.
It should be noted that, since the family-related membership information generally exists only in the public security system, the family-related membership information can be obtained from the public security system after the identity information is obtained. And the public security system searches the household information according to the identity information provided by the public security system, further finds the corresponding family related membership information, receives the family membership information fed back by the public security system, and finds the corresponding second detection information in the hospital system based on the family related membership information.
Specifically, in the embodiment of the invention, the method further comprises the following steps:
acquiring evaluation content of a detection result output by a hospital doctor on the generated model;
analyzing the evaluation by adopting an emotion analysis tool to obtain adjectives representing emotion tendencies and emotion polarity values thereof;
when the emotion polarity value is negative evaluation, setting a corresponding parameter adjustment amplitude according to the emotion polarity value; wherein the emotion polarity value and the parameter adjustment amplitude are in one-to-one correspondence;
and adjusting parameters in the generation model and the judgment model according to the parameter adjustment amplitude until the evaluation content of the hospital doctor is positive evaluation.
It should be noted that, an emotion analysis tool SentiWordNet (SentiWordNet is a tool for opinion mining), where SentiWordNet can divide content into enthusiasm and enthusiasm according to corresponding emotion scores, analyze evaluation content to obtain words representing emotion tendencies and emotion polarity values thereof, set adjectives with emotion polarity values greater than 0.5 (set values, and adjust according to specific conditions) as adjectives with positive emotions, words with emotion polarity values less than or equal to 0.5 as adjectives with negative emotions, and if doctor emotion tendencies as negative emotions, this means that output results of generated models do not conform to output results of doctors, so that retraining of discriminant models and generated models is required, update of discriminant models and generated models by doctor opinion can be realized, and training efficiency and accuracy of models are improved.
Specifically describing, in an embodiment of the present invention, the step of obtaining sample detection information of a patient to be detected includes:
acquiring a platform database in which the sample detection information is located;
and acquiring sample detection information in the platform database through the sqoop script.
It should be noted that, the Sqoop script is a tool for transferring data in Hadoop and relational databases to each other, and may be used to import data in a relational database (for example, mySQL, oracle, postgres, etc.) into the HDFS of Hadoop, or may be used to import data of the HDFS into the relational database. Namely, sample detection information is obtained by climbing sample detection information at the corresponding position of the platform through the Sqoop script, so that the sample detection information is obtained.
As shown in fig. 2, in one embodiment, a more specific embodiment of a patient information-based detection method is provided, including the steps of:
s201, acquiring a platform database where sample detection information is located, and acquiring the sample detection information in the platform database through an sqoop script; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
s202, sending a family related membership information acquisition request to a public security system based on the identity information of the patient;
S203, receiving family-related membership information fed back by the public security system, and finding out second detection information corresponding to the item to be detected based on the family-related membership information;
s204, setting information weight for each piece of second detection information according to the relative relationship between the family member and the patient and a preset relative relationship and weight corresponding table;
s205, inputting the first detection information into a generation model, and inputting the second detection information and the corresponding information weight into a preset judgment model; the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
s206, correcting the result output by the generation model through the output result of the discrimination model to obtain the detection result output by the generation model;
s207, acquiring evaluation contents of detection results output by the hospital doctor on the generated model;
s208, analyzing the evaluation by adopting an emotion analysis tool to obtain adjectives representing emotion tendencies and emotion polarity values thereof;
s209, when the emotion polarity value is negative evaluation, setting a corresponding parameter adjustment amplitude according to the emotion polarity value; wherein the emotion polarity value and the parameter adjustment amplitude are in one-to-one correspondence;
And S210, adjusting parameters in the generation model and the judgment model according to the parameter adjustment amplitude until the evaluation content of the hospital doctor is positive evaluation.
Referring to fig. 3, the detection device based on patient information provided by the present embodiment includes the following steps:
a first acquisition module 10 for acquiring sample detection information of a patient to be detected; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
a second obtaining module 20, configured to obtain, when the first detection information contains target detection information, second detection information corresponding to the item to be detected from each family member in family information of the patient based on the sample detection information;
a setting module 30, configured to set information weights for each second detection information according to a preset relatives and weight correspondence table according to the relatives of the family members and the patients;
the input module 40 is configured to input the first detection information into a generation model, and input the second detection information and a corresponding information weight into a preset discrimination model; the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
And the correction module 50 is configured to correct the result output by the generating model according to the output result of the discriminating model, so as to obtain a detection result output by the generating model.
Specifically describing, in an embodiment of the present invention, the input module includes:
the acquisition sub-module is used for acquiring a detection information sample set; the detection information sample set comprises first sample detection information, second sample detection information, weight values corresponding to the second sample detection information and sample detection results corresponding to the patients;
an input sub-module for detecting the first sample with information v 1 Inputting the optimal predicted value r into an initial generation model i The sample detection result r true Inputting the initial generation model through a formula Initial training is carried out on the initial generation model, and a trained temporary predicted value r is obtained j And an intermediate generation model, which is used for generating a model,
multiplying the second sample detection information by the corresponding weight value to obtain input information v 2 Inputting the input information v 2 Input into the initial discrimination model by the formulaPerforming initial training on the initial discrimination model to obtain an intermediate discrimination model; wherein (1) >θ represents the parameter set of the generative model, +.>Parameter set representing discriminant model g θ (r i |v 1 ,r true ) Represented by θ as a coefficient, r i 、v 1 、r true For a first preset function of the parameter, +.>Expressed as +.>Is the coefficient, r i 、v 2 、r true A second preset function of the parameter;
training submodule for according to the formula Performing secondary training on the intermediate generation model and the intermediate discrimination model, and obtaining the generation model and the discrimination model after training is completed; wherein->Represents that the minimum value of θ is taken on the premise of satisfying the above formula +.>Maximum value of O G,D Represents taking the minimum value of θ +.>Indicated by the maximum value of (2).
Specifically, in the embodiment of the present invention, the input module further includes the following modules:
the sample detection information input sub-module is used for inputting the first sample detection information into a trained generation model, inputting the second sample detection information and the corresponding information weight into a trained discrimination model, and correcting the result output by the trained generation model through the output result of the trained discrimination model to obtain a prediction detection result output by the generation model;
the comprehensive loss value calculation submodule is used for obtaining a comprehensive loss value of a generated model after training and a discrimination model after training according to the prediction detection result and the sample detection result;
The judging submodule is used for judging whether the comprehensive loss value is smaller than a preset loss value or not;
and the judging sub-module is used for judging that the generated model after training and the judging model after training meet the training requirement if the comprehensive loss value is smaller than the preset loss value.
Specifically describing, in an embodiment of the present invention, the second obtaining module includes:
the sending sub-module is used for sending a family related membership information acquisition request to the public security system based on the identity information of the patient;
and the receiving sub-module is used for receiving the family related membership information fed back by the public security system and finding out second detection information corresponding to the item to be detected based on the family related membership information.
Specifically describing, in an embodiment of the present invention, the apparatus further includes the following modules:
an initiation module for initiating an authentication request to the patient based on the identity information of the patient;
and the requirement satisfaction module is used for judging that the requirement of executing the step of acquiring the second detection information of the to-be-detected item corresponding to each family member in the family information of the patient based on the sample detection information when the first detection information contains the target detection information if the authentication request passes.
In this technical solution, it should be noted that the implementation manner of the detection device based on patient information is the same as the principle of the detection method based on patient information, and will not be described herein again.
Referring to FIG. 4, an internal block diagram of a computer device is shown in one embodiment. The computer device may specifically be a terminal or a server. The computer device includes a processor, a memory, and a network interface connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device may store an operating system, and may also store a computer program which, when executed by the processor, causes the processor to implement the patient information-based detection method described above. The internal memory may also store a computer program which, when executed by the processor, causes the processor to perform the above-described patient information-based detection method. It will be appreciated by those skilled in the art that the block diagrams of the elements described in connection with the aspects of the application are not intended to limit the apparatus to which the aspects of the application are applied, and that a particular apparatus may include more or less elements than those shown, or may combine certain elements, or may have a different arrangement of elements.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored a computer program which, when executed by the processor, causes the processor to perform the steps of the above-described patient information based detection method.
In one embodiment, a computer readable storage medium is provided having a computer program stored therein, which when executed by a processor causes the processor to perform the steps of the above-described patient information based detection method.
It will be appreciated that the above-described patient information-based detection method, apparatus, computer device, and computer-readable storage medium belong to one general inventive concept, and that the embodiments are mutually applicable.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
According to the technical scheme, the sample detection information of the patient and the detection information of the family members are mainly utilized, the information weight is set according to the relatives of the family members and is respectively input into the generation model and the discrimination model, the detection result of the detection information of the patient is corrected by utilizing the detection information of the family members, and therefore the accuracy of the detection result of the patient can be improved.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A patient information-based detection method, comprising the steps of:
acquiring sample detection information of a patient to be detected; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
When the first detection information contains target detection information, acquiring second detection information of the to-be-detected item corresponding to each family member in family information of the patient based on the sample detection information;
setting information weight for each second detection information according to the family member and patient relative relation and a preset relative relation and weight corresponding table;
inputting the first detection information into a generation model, and inputting the second detection information and the corresponding information weight into a preset judgment model; the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
correcting the result output by the generating model through the output result of the judging model to obtain the detection result output by the generating model;
the step of inputting the first detection information into a generation model and inputting the second detection information and the corresponding information weight into a preset discrimination model includes:
acquiring a detection information sample set; the detection information sample set comprises first sample detection information, second sample detection information, weight values corresponding to the second sample detection information and sample detection results corresponding to the patients;
Detecting the first sample with information v 1 Inputting the optimal predicted value r into an initial generation model i The sample detection result r ture Inputting the initial generation model through a formulaInitial training is carried out on the initial generation model, and a trained temporary predicted value r is obtained j And an intermediate generation model, which is used for generating a model,
multiplying the second sample detection information by the corresponding weight value to obtain input information v 2 Inputting the input information v 2 Input into the initial discrimination model by the formulaPerforming initial training on the initial discrimination model to obtain an intermediate discrimination model;wherein (1)>θ represents the parameter set of the generative model, +.>Parameter set representing discriminant model g θ (r i |v 1 ,r true ) Represented by θ as a coefficient, r i 、v 1 、r true For a first preset function of the parameter, +.>Expressed as +.>Is the coefficient, r i 、v 2 、r true A second preset function of the parameter;
according to the formulaPerforming secondary training on the intermediate generation model and the intermediate discrimination model, and obtaining the generation model and the discrimination model after training is completed; wherein->Represents that the minimum value of θ is taken on the premise of satisfying the above formula +.>Maximum value of O G,D Represents taking the minimum value of θ +.>An indication value of the maximum value of (2);
the method further comprises the following steps:
Inputting the first sample detection information into a trained generation model, inputting the second sample detection information and corresponding information weight into a trained discrimination model, and correcting the result output by the trained generation model through the output result of the trained discrimination model to obtain a prediction detection result output by the generation model;
obtaining a comprehensive loss value of a generated model after training and a discrimination model after training according to the prediction detection result and the sample detection result;
judging whether the comprehensive loss value is smaller than a preset loss value or not;
if yes, judging that the generated model after training and the judging model after training meet the training requirements.
2. The method according to claim 1, wherein when the first detection information contains target detection information, the step of acquiring second detection information of each family member corresponding to the item to be detected from family information of the patient based on the sample detection information includes:
sending a family-related membership information acquisition request to a public security system based on the identity information of the patient;
And receiving family-related membership information fed back by the public security system, and finding out second detection information corresponding to the item to be detected based on the family-related membership information.
3. The patient information based detection method according to claim 1, wherein the method further comprises the steps of:
initiating an authentication request to the patient based on the identity information of the patient;
if the authentication request passes, the request of executing the step of acquiring the second detection information of the item to be detected corresponding to each family member in the family information of the patient based on the sample detection information when the first detection information contains the target detection information is judged to be satisfied.
4. The patient information based detection method according to claim 1, wherein the method further comprises the steps of:
acquiring evaluation content of a detection result output by a hospital doctor on the generated model;
analyzing the evaluation by adopting an emotion analysis tool to obtain adjectives representing emotion tendencies and emotion polarity values thereof;
when the emotion polarity value is negative evaluation, setting a corresponding parameter adjustment amplitude according to the emotion polarity value; wherein the emotion polarity value and the parameter adjustment amplitude are in one-to-one correspondence;
And adjusting parameters in the generation model and the judgment model according to the parameter adjustment amplitude until the evaluation content of the hospital doctor is positive evaluation.
5. The method for detecting based on patient information according to claim 1, wherein the step of acquiring sample detection information of the patient to be detected includes:
acquiring a platform database in which the sample detection information is located;
and acquiring sample detection information in the platform database through the sqoop script.
6. A patient information based detection device, comprising the steps of:
the first acquisition module is used for acquiring sample detection information of a patient to be detected; wherein the sample detection information comprises identity information of a patient and first detection information of an item to be detected;
the second acquisition module is used for acquiring second detection information of the to-be-detected item corresponding to each family member in family information of the patient based on the sample detection information when the first detection information contains target detection information;
the setting module is used for setting information weight for each piece of second detection information according to the relative relation between the family member and the patient and a preset relative relation and weight corresponding table;
The input module is configured to input the first detection information into a generation model, and input the second detection information and the corresponding information weight into a preset discrimination model, and includes: acquiring a detection information sample set; the detection information sample set comprises first sample detection information, second sample detection information, weight values corresponding to the second sample detection information and sample detection results corresponding to the patients;
detecting the first sample with information v 1 Inputting the optimal predicted value r into an initial generation model i The sample detection result r true Inputting the initial generation model through a formulaInitial training is carried out on the initial generation model, and a trained temporary predicted value r is obtained j And an intermediate generation model, which is used for generating a model,
multiplying the second sample detection information by the corresponding weight value to obtain input information v 2 Inputting the input information v 2 Input into the initial discrimination model by the formulaPerforming initial training on the initial discrimination model to obtain an intermediate discrimination model; wherein (1)>θ represents the parameter set of the generative model, +.>Parameter set representing discriminant model g θ (r i |v 1 ,r true ) Represented by θ as a coefficient, r i 、v 1 、r true For a first preset function of the parameter, +.>Expressed as +.>Is the coefficient, r i 、v 2 、r true A second preset function of the parameter;
according to the formulaPerforming secondary training on the intermediate generation model and the intermediate discrimination model, and obtaining the generation model and the discrimination model after training is completed; wherein->Represents that the minimum value of θ is taken on the premise of satisfying the above formula +.>Maximum value of O G,D Represents taking the minimum value of θ +.>An indication value of the maximum value of (2); the generating model and the judging model are synchronously trained through different detection information and corresponding detection results, and the generating model and the judging model are neural network models;
the correction module is used for correcting the result output by the generation model through the output result of the discrimination model to obtain the detection result output by the generation model;
the input module also comprises the following modules:
the sample detection information input sub-module is used for inputting the first sample detection information into a trained generation model, inputting the second sample detection information and the corresponding information weight into a trained discrimination model, and correcting the result output by the trained generation model through the output result of the trained discrimination model to obtain a prediction detection result output by the generation model;
The comprehensive loss value calculation submodule is used for obtaining a comprehensive loss value of a generated model after training and a discrimination model after training according to the prediction detection result and the sample detection result;
the judging submodule is used for judging whether the comprehensive loss value is smaller than a preset loss value or not;
and the judging sub-module is used for judging that the generated model after training and the judging model after training meet the training requirement if the comprehensive loss value is smaller than the preset loss value.
7. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the patient information based detection method of any one of claims 1 to 5.
8. A computer device comprising a memory and a processor, characterized in that the memory stores a computer program which, when executed by the processor, causes the processor to perform the steps of the patient information based detection method according to any one of claims 1 to 5.
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