CN112397161A - Method, apparatus, device and medium for generating an assay model - Google Patents
Method, apparatus, device and medium for generating an assay model Download PDFInfo
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Abstract
Embodiments of the present disclosure disclose a method, apparatus, device, and medium for generating an assay model. One embodiment of the method comprises: obtaining a sample set; selecting samples from the sample set, and performing the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample into an initial model to obtain the test recommendation information; analyzing the obtained test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be an assay model. This embodiment improves the efficiency of the doctor's diagnostic work.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technology, and in particular, to methods, apparatuses, devices, and media for generating assay models.
Background
The era is developing and the society is advancing. With the continuous innovation of science and technology, intelligent office becomes an important way for enterprises to improve the working efficiency gradually. The problem to be solved by the intelligent model is a complex problem in a specific field and involves a great deal of professional knowledge, developers are not experts in the field generally, and the familiarity of the developers in the specific field needs a process, so that the software requirements are difficult to define completely in the initial stage. Therefore, implementing a model using a prototype requires refining the software requirements through multiple iterations. The intelligent model takes knowledge as a processing object, and the knowledge has both theoretical knowledge and experience in a specific field. During the development process, the knowledge is extracted from the book and the knowledge base in a specific field (namely knowledge acquisition), and an appropriate method is selected for coding (namely knowledge representation) to establish the knowledge base. The model, software engineering knowledge and knowledge of a specific field are respectively stored in a database, and close cooperation between a system developer and a field expert is required in the process.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure provide methods, apparatuses, and computer-readable media for generating an assay model.
In a first aspect, embodiments of the present disclosure disclose a method for generating an assay model, comprising: acquiring a sample set, wherein samples in the sample set comprise sample case information, and the sample case information comprises basic information of sample patients, symptom information of the sample patients, physical examination information of the sample patients and laboratory recommendation information of the sample patients; selecting samples from the sample set, and performing the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample into an initial model to obtain the test recommendation information; analyzing the obtained test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be an assay model.
In some embodiments, the method further comprises: and responding to the condition that the initial model is not trained completely, adjusting relevant parameters in the initial model, reselecting samples from the sample set, and continuing to execute the training step by using the adjusted initial model as the initial model.
In some embodiments, wherein the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample are input into the initial model to obtain the assay recommendation information, further comprising: sending the assay recommendation information to a terminal device and displaying on a display of the terminal device; and controlling the terminal equipment to send prompt information for prompting a doctor to perform the next operation.
In a second aspect, embodiments of the present disclosure provide an apparatus for generating an assay model, comprising: the system comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is configured to acquire a sample set, samples of the sample set comprise sample case information, and the sample case information comprises basic information of sample patients, symptom information of the sample patients, physical examination information of the sample patients and laboratory recommendation information of the sample patients; a training unit configured to select samples from the sample set and to perform the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample into an initial model to obtain the test recommendation information; analyzing the obtained test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be an assay model.
In a third aspect, embodiments of the present disclosure provide a method for generating assay recommendation information, comprising: acquiring basic information, symptom information and physical examination information in case information of an assay object; inputting the basic information, symptom information and physical examination information in the case information of the test object into the test model generated by the method described in any one of the embodiments of the first aspect, and generating test recommendation information of the test object.
In some embodiments, the method further comprises: sending the assay recommendation information to a terminal device and displaying on a display of the terminal device; and controlling the terminal equipment to send prompt information for prompting a doctor to perform the next operation.
In some embodiments, the method further comprises: analyzing the assay recommendation information to extract keywords; amplifying the keywords; setting an underline style in the font styles of the keywords as an underline; and sending the processed test recommendation information to the terminal equipment, and storing the processed test recommendation information into a new file in a text form.
In a fourth aspect, embodiments of the present disclosure provide an apparatus for generating assay recommendation information, comprising: an acquisition unit configured to acquire basic information, symptom information, and physical examination information in case information of an assay object; a generating unit configured to input the basic information, symptom information and physical examination information in the case information of the test object into the test model generated by the method as described in any one of the embodiments of the first aspect, and generate test recommendation information of the test object.
In a fifth aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement a method as in any one of the first aspects.
In a sixth aspect, the disclosed embodiments provide a computer readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method according to any one of the first aspect.
Some embodiments of the present disclosure provide methods and apparatus for generating an assay model by obtaining a sample set from which samples can be selected for initial model training. The samples in the sample set comprise sample case information, the sample case information comprises basic information of sample patients, symptom information of the sample patients, physical examination information of the sample patients and laboratory recommendation information of the sample patients. Therefore, the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample are input into the initial model, and the test recommendation information can be obtained. The resulting assay recommendation information may then be analyzed with the corresponding assay recommendation information for the sample patient to determine an assay recommendation loss value. The assay recommended loss value is then compared to a target value. Finally, it may be determined whether the initial model is trained based on the comparison. If it is determined that the initial model training is completed, the trained initial model may be used as the test model. Thereby enabling an assay model to be obtained that can be used to generate assay recommendation information.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is an exemplary system architecture diagram in which the present disclosure may be applied;
fig. 2 is a flow diagram of some embodiments of a method for generating an assay model according to embodiments of the present disclosure.
Fig. 3 is a flow chart of some embodiments of an apparatus for generating an assay model according to embodiments of the present disclosure.
Fig. 4 is a flow chart of some embodiments of an apparatus for generating assay recommendation information in accordance with embodiments of the present disclosure.
FIG. 5 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant disclosure and are not limiting of the disclosure. It should be noted that, for the convenience of description, only the parts relevant to the related disclosure are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which the methods for generating assay models of the embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 over the network 104 to send the sample set.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting image recognition, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, and the server 105 may obtain a sample set from the terminal devices 101, 102, 103. The sample set may include patient basic information, condition information, physician diagnosis conclusion information, and treatment method information. Such as a server providing support for the generation of assay information applications on the terminal devices 101, 102, 103. The server may train the model to be trained using the sample set stored in the terminal device to obtain the assay model (e.g., a second-trained model, a third-trained model). The server can also input the information submitted by the terminal equipment into the test model to generate test recommendation information.
Alternatively, the server may feed back the above-described assay recommendation information (e.g., the second recognition result) to the terminal device.
It should be noted that the method for generating an assay model provided by the embodiments of the present disclosure is generally performed by the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of yet another embodiment of a method for generating an assay model according to an embodiment of the present disclosure is shown. The method for generating an assay model, comprising the steps of:
In some embodiments, the performing agent (e.g., server 105 shown in fig. 1) of the method for generating an assay model may obtain the sample set by connecting to a terminal device used by a user to store the sample set. Here, the connection may be a wired connection and a wireless connection. It should be noted that the wireless connection mode may include, but is not limited to, a 3G/4G connection, a wifi (wireless fidelity) wireless connection, a bluetooth connection, a wimax (worldwide Interoperability for Microwave access), a worldwide Interoperability for Microwave access (wimax) connection, a uwb (ultra wideband) connection using carrier-less communication technology, and other wireless connection modes now known or developed in the future.
At step 202, a sample is selected from a sample set.
In some embodiments, the performing agent may select samples from the sample set obtained in step 201 to perform the training steps including steps 203-206. The manner of selecting the sample is not limited in this disclosure. For example, at least one sample may be selected randomly, or a sample with good information integrity (i.e., all the examinations are done by the patient) may be selected.
Here, the sample may include sample case information including basic information of the sample patient, symptom information of the sample patient, physical examination information of the sample patient, and laboratory recommendation information of the sample patient. The basic information of the sample patient may include at least one of the following information: gender information, age information. For example, "male, 40 years old". The symptom information of the sample patient may be feedback information obtained by the physician asking the patient. For example, a patient comes to a hospital for a doctor, and a doctor asks the patient: "where is uncomfortable? ", patient answer; "fever had occurred for two days". Then "fever has been given for two days" is the patient's symptom information. The physical examination information of the sample patient may be physical examination information of the sample patient. For example, the physical examination information of the patient may be data information obtained by a doctor measuring the body temperature and the heartbeat of the patient. For another example, a patient comes to a hospital for a doctor, and a doctor performs a physical examination on the patient to obtain patient information: body temperature 38 deg., heartbeat 79/min. Then, "body temperature 38 °, heartbeat 79 beats/minute" is the physical examination information of the patient.
It is understood that the basic information of the sample patient may be manually set in advance, or may be obtained by executing a certain setting program by a subject or other equipment.
In some embodiments, the executing entity may input basic information of the sample patient, symptom information of the sample patient, and physical examination information of the sample patient in the sample case information of the sample selected in step 202 into the initial model. Analyzing the input basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient, and extracting disease keywords. And searching related tests according to the disease keywords to obtain test recommendation information.
In the present embodiment, the initial model may be various existing neural network models created based on machine learning techniques. The neural network model may have various existing neural network structures. The storage location of the initial model is likewise not limiting in this disclosure. For example, the neural network structure may be a Long Short-Term Memory network (LSTM). As another example, the Neural Network structure may be a Recurrent Neural Network (RNN).
In this embodiment, the disease keyword may be the basic information of the sample patient, the symptom information of the sample patient, and the vocabulary for describing the disease condition of the patient in the physical examination information of the sample patient. The relevant assays may be those related to the condition, and which need to be performed. The test recommendation information may be recommendation information for recommending a patient to perform a test. For example, the patient information entered includes: the basic information of the patient is "sex: a woman; age: age 35; height: 165cm "; the patient's symptom information is "fever and sore throat"; the physical examination information of the patient is "body temperature 39 °, cardiopulmonary auscultation: there is a wet rale in the lung. Then, the disease keywords are "fever", "sore throat", "body temperature 39 °", "wet rales in the lung", and the relevant tests may be blood routine test, acute pharyngitis test, tonsil test. The test recommendation information may be "recommended blood routine test", "recommended tonsil test".
And 204, analyzing the obtained test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value.
In some embodiments, the executive agent may analyze the test recommendation information obtained in step 203 with the corresponding test recommendation information of the sample patient, so that a test recommendation loss value may be determined. The assay recommended loss value is then compared to a target value. Here, the target value is an expected output value of the assay recommended loss value. It is understood that the target value may be artificially set in advance according to actual requirements. The recommended loss value for the test may be a value obtained by inputting the obtained recommended test information and the recommended test information of the corresponding sample patient as parameters into a specified loss function. Here, the loss function (e.g., a square loss function, an exponential loss function, etc.) is generally used to measure the degree of inconsistency between the predicted value (e.g., the test recommendation information of the sample patient in the sample case information) and the actual value (e.g., the test recommendation information obtained through step 203) of the model. It is a non-negative real-valued function. In general, the smaller the loss function, the better the robustness of the model. The loss function may be set according to actual requirements.
And step 205, determining whether the training of the initial model is finished according to the comparison result.
In some embodiments, the performing subject may obtain a comparison result based on the comparison of the assay recommended loss value to the target value in step 204. And determining that the initial model training is finished in response to the comparison result that the test recommended loss value is smaller than the target value.
In step 206, it is determined whether a preset training termination condition is satisfied.
In some embodiments, the execution subject may determine whether a preset end training condition is satisfied. The training end condition may be a condition predetermined by a technician for ending the training step. For example, the end training condition may include, but is not limited to, at least one of the following: the training times reach or exceed the preset times; the training time reaches or exceeds the preset time length; the function value of the predetermined loss function is smaller than a preset threshold value, and so on.
In some optional implementations of some embodiments, in response to the comparison result test recommending a loss value greater than a target value, determining that the initial model training is incomplete. In response to determining that the initial model is not trained, adjusting relevant parameters in the initial model, and re-selecting samples from the sample set, using the adjusted initial model as the initial model, and continuing to perform the training steps including steps 203-206.
With continued reference to fig. 3, an apparatus 300 for generating an assay model of some embodiments includes: an acquisition unit 301 and a training unit 302.
In some embodiments, the obtaining unit 301 is configured to obtain a set of samples. Here, the acquiring unit 301 may acquire the sample set in various ways. For example, the acquisition unit 301 may acquire the sample set by connecting (wired connection and wireless connection) a terminal device for storing the sample set by a user. The samples of the sample set comprise sample case information, wherein the sample case information comprises basic information of sample patients, symptom information of the sample patients, physical examination information of the sample patients and laboratory recommendation information of the sample patients.
In some embodiments, the training unit 302 is configured to select samples from the sample set, and perform the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample into an initial model to obtain the test recommendation information; analyzing the test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be an assay model.
In some optional implementation manners of some embodiments, the inputting, in the training unit 302, the basic information of the sample patient, the symptom information of the sample patient, and the physical examination information of the sample patient in the sample case information of the selected sample into the initial model, and the obtaining of the test recommendation information further includes: sending the assay recommendation information to a terminal device and displaying on a display of the terminal device; and controlling the terminal equipment to send prompt information for prompting a doctor to perform the next operation.
With continued reference to fig. 4, an apparatus 300 for generating an assay model of some embodiments includes: an acquisition unit 401 and a generation unit 402.
In some embodiments, the obtaining unit 401 is configured to obtain a sample set. Here, the acquiring unit 301 may acquire the sample set in various ways. For example, the acquisition unit 301 may acquire the sample set by connecting (wired connection and wireless connection) a terminal device for storing the sample set by a user. The samples of the sample set comprise sample case information, wherein the sample case information comprises basic information of sample patients, symptom information of the sample patients, physical examination information of the sample patients and laboratory recommendation information of the sample patients.
In some embodiments, the training unit 402 is configured to select samples from the sample set, and perform the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample into an initial model to obtain the test recommendation information; analyzing the test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be an assay model.
In some optional implementation manners of some embodiments, the generating unit 402 inputs the basic information of the sample patient, the symptom information of the sample patient, and the physical examination information of the sample patient in the sample case information of the selected sample into the initial model, and the obtaining of the test recommendation information further includes: sending the assay recommendation information to a terminal device and displaying on a display of the terminal device; and controlling the terminal equipment to send prompt information for prompting a doctor to perform the next operation.
In some optional implementation manners of some embodiments, analyzing the assay recommendation information to extract keywords; amplifying the keywords; setting an underline style in the font styles of the keywords as an underline; and sending the processed test recommendation information to the terminal equipment, and storing the processed test recommendation information into a new file in a text form. Here, the analyzing and extracting the keyword is to analyze the assay recommendation information, and extract a word describing an assay in the assay recommendation information as the keyword. For example, "the patient has done a blood routine test", then "the blood routine test" is the keyword. Specifically, the enlargement processing is to enlarge the font size based on the font size in the font style of the current test recommendation information. For example, if the current font size is 14, the font size of the keyword is adjusted to 16.
With continued reference to FIG. 5, a block diagram 500 of a computer system suitable for use in implementing the electronic device of an embodiment of the present disclosure is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium 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 the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer 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. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a sample set, wherein sample information in the sample set comprises sample case information, the sample case information comprises basic information of a sample patient, symptom information of the sample patient, processing information of a sample doctor and case text information of the sample patient; selecting samples from the sample set, and performing the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the processing information of the sample doctor in the sample case information of the selected sample into an initial model to obtain case text information; analyzing the obtained case text information and the case text information of the corresponding sample patient, determining a case text loss value, and comparing the case text loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be a case input model.
Computer program code for carrying out operations for embodiments 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, Smalltalk, C + +, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that the method for generating an assay model according to the embodiments of the present disclosure may be used to test the assay model generated according to the above embodiments. The assay model can then be continuously optimized based on the test results. The method may also be a practical application method of the assay model generated in the above embodiments.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept as defined above.
Claims (10)
1. A method for generating an assay model, comprising:
acquiring a sample set, wherein samples in the sample set comprise sample case information, the sample case information comprises basic information of sample patients, symptom information of the sample patients, physical examination information of the sample patients and laboratory recommendation information of the sample patients;
selecting samples from the sample set, and performing the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample into an initial model to obtain the test recommendation information; analyzing the obtained test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value; determining whether the initial model is trained according to the comparison result; determining the initial model as an assay model in response to determining that the initial model training is complete.
2. The method of claim 1, wherein the method further comprises:
and in response to determining that the initial model is not trained completely, adjusting relevant parameters in the initial model, reselecting samples from the sample set, and continuing to perform the training step by using the adjusted initial model as the initial model.
3. The method of claim 1, wherein the inputting of the basic information of the sample patient, the symptom information of the sample patient, and the physical examination information of the sample patient in the sample case information of the selected sample into the initial model to obtain the assay recommendation information, further comprises:
sending the assay recommendation information to a terminal device, and displaying on a display of the terminal device;
and controlling the terminal equipment to send prompt information for prompting a doctor to perform the next operation.
4. An apparatus for generating an assay model, comprising:
an acquisition unit configured to acquire a sample set, wherein samples of the sample set include sample case information including basic information of sample patients, symptom information of the sample patients, physical examination information of the sample patients, and laboratory recommendation information of the sample patients;
a training unit configured to select samples from the set of samples and to perform the following training steps: inputting the basic information of the sample patient, the symptom information of the sample patient and the physical examination information of the sample patient in the sample case information of the selected sample into an initial model to obtain the test recommendation information; analyzing the test recommendation information and the corresponding test recommendation information of the sample patient, determining a test recommendation loss value, and comparing the test recommendation loss value with a target value; determining whether the initial model is trained according to the comparison result; in response to determining that the initial model training is complete, the initial model is determined to be an assay model.
5. A method for generating assay recommendation information, comprising:
acquiring basic information, symptom information and physical examination information in case information of an assay object;
inputting basic information, symptom information and physical examination information in case information of the test subject into a test model generated by the method according to any one of claims 1 to 3, and generating test recommendation information of the test subject.
6. The method of claim 5, wherein the method further comprises:
sending the assay recommendation information to a terminal device, and displaying on a display of the terminal device;
and controlling the terminal equipment to send prompt information for prompting a doctor to perform the next operation.
7. The method of claim 5, wherein the method further comprises:
analyzing the test recommendation information to extract keywords;
amplifying the keywords;
setting an underline style in the font styles of the keywords as underline;
and sending the processed test recommendation information to the terminal equipment, and storing the processed test recommendation information into a new file in a text form.
8. An apparatus for generating assay recommendation information, comprising:
an acquisition unit configured to acquire basic information, symptom information, and physical examination information in case information of an assay object;
a generating unit configured to input basic information, symptom information and physical examination information in case information of the test object into a test model generated using the method according to any one of claims 1 to 3, and generate test recommendation information of the test object.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-3, 5-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any of claims 1-3, 5-7.
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