Physical examination conclusion combination method and device, computer program product

Abstract

The application discloses a physical examination conclusion merging method and device, and a computer program product, wherein the method comprises the following steps: acquiring a physical examination report, and extracting a conclusion word in the physical examination report; constructing a prompt based on the conclusion word, and inputting the prompt into an adjusted model, so that the model determines a standard conclusion word corresponding to the conclusion word from a word library based on the prompt; and merging the standard conclusion word according to a specified merging rule by using a merging rule engine. The physical examination conclusion is merged in an intelligent manner, the merging efficiency and accuracy are improved, and the technical problem that the merging efficiency and accuracy need to be further improved in the prior art is overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information processing, and particularly relates to a physical examination conclusion merging method and device and a computer program product. BACKGROUND

[0002] In the conventional physical examination report generation process, most scenes need to merge abnormal conclusions with high disease relevance, which often depends on relevant medical guidelines and manual merging operations of the main inspector, which not only consumes time and effort, but also is prone to omissions and errors. Especially in the case of insufficient professional main inspectors, the merging efficiency and accuracy of the physical examination report are seriously affected. SUMMARY

[0003] The main purpose of the present application is to provide a physical examination conclusion merging method and device and a computer program product to solve the technical problems existing in the related art.

[0004] In order to achieve the above-mentioned purpose, according to the first aspect of the present application, a physical examination conclusion merging method is provided, comprising: acquiring a physical examination report and extracting a conclusion word in the physical examination report; constructing a prompt based on the conclusion word and inputting the prompt into an adjusted model, so that the model determines a standard conclusion word corresponding to the conclusion word from a word library based on the prompt; and merging the standard conclusion word according to a specified merging rule by using a merging rule engine.

[0005] Optionally, when the model is adjusted: a physical examination report sample is acquired to extract an abnormal conclusion word from the sample; a prompt is constructed based on the abnormal conclusion word, and the prompt is used as the input of the model; an output is constructed based on the standard conclusion word mapped by the abnormal conclusion word; and the model is trained by using the input and the output.

[0006] Optionally, the merging of the standard conclusion word according to the specified merging rule by using the merging rule engine comprises: merging the standard conclusion word according to a plurality of sub-merging rules in the merging rule; wherein each sub-merging rule has a different priority, and it is judged whether the standard conclusion word hits the sub-merging rule when merging; if the standard conclusion word does not hit the current sub-merging rule, the next sub-merging rule is matched according to the priority order; and if the current sub-merging rule is hit, the merging is performed according to the indication of the sub-merging rule.

[0007] Optionally, the sub-merging rule includes a disease merging rule, a human body system structure information merging rule, and a department information merging rule.

[0008] According to a second aspect of the present application, there is provided a physical examination conclusion merging device, comprising: a conclusion word extraction unit configured to obtain a physical examination report and extract a conclusion word in the physical examination report; a prediction unit configured to construct a prompt based on the conclusion word and input the prompt into an adjusted model, so that the model determines a standard conclusion word corresponding to the conclusion word from a word library based on the prompt; and a merging unit configured to merge the standard conclusion word according to a specified merging rule by using a merging rule engine.

[0009] Optionally, when the model is adjusted: a physical examination report sample is obtained to extract an abnormal conclusion word from the sample; a prompt is constructed based on the abnormal conclusion word, and the prompt is input into the model; an output is constructed based on a standard conclusion word mapped by the abnormal conclusion word; and the model is trained by using the input and the output.

[0010] Optionally, the merging unit is configured to merge the standard conclusion word according to a plurality of sub-merging rules in the merging rule; wherein each sub-merging rule has a different priority, and whether the standard conclusion word hits the sub-merging rule is determined when merging; if the standard conclusion word does not hit the current sub-merging rule, the next sub-merging rule is matched according to the priority order; and if the current sub-merging rule is hit, the merging is performed according to the indication of the sub-merging rule.

[0011] According to a third aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a computer to perform a physical examination conclusion merging method.

[0012] According to a fourth aspect of the present application, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to cause the at least one processor to perform a physical examination conclusion merging method.

[0013] According to a fifth aspect of the present application, there is provided a computer program product comprising a computer program, which, when executed by a processor, implements a physical examination conclusion merging method.

[0014] The embodiment of the physical examination conclusion merging method and device, and the computer program product, wherein the method comprises: acquiring a physical examination report, and extracting a conclusion word in the physical examination report; constructing a prompt based on the conclusion word, and inputting the prompt into an adjusted model, so that the model determines a standard conclusion word corresponding to the conclusion word from a word library based on the prompt; and merging the standard conclusion word according to a specified merging rule by using a merging rule engine. The physical examination conclusion is merged in an intelligent manner, which improves the merging efficiency and accuracy, and overcomes the technical problem that the merging efficiency and accuracy need to be further improved in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 It is a physical examination conclusion merging method flowchart of the embodiment of the present application.

[0017] Figure 2 It is a mapping relationship schematic diagram in the physical examination conclusion merging method of the embodiment of the present application.

[0018] Figure 3 It is a system architecture diagram suitable for the physical examination conclusion merging method of the embodiment of the present application.

[0019] Figure 4 It is a schematic diagram of an electronic device according to the embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification of the present application are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] According to an embodiment of the present application, a physical examination conclusion merging method is provided, as shown in the figure, comprising the following steps 101 to 103: Figure 1

[0024] Step 101: Obtain a physical examination report and extract the conclusion words in the physical examination report.

[0025] In this step, the physical examination report to be merged can be obtained from the terminal, and then the abnormal conclusion words can be extracted based on the department summary in the physical examination report.

[0026] Step 102: Construct a prompt based on the conclusion words and input the prompt into the adjusted model, so that the model determines the standard conclusion words corresponding to the conclusion words from the word library based on the prompt.

[0027] In this step, the large model can be pre-trained and fine-tuned, so that after fine-tuning, the standard conclusion words can be predicted for the input physical examination report. When predicting, the large model can match the standard conclusion words mapped to the extracted abnormal conclusion words in the mapping relationship library.

[0028] The mapping relationship library of conclusion words and standard conclusion words can be constructed before prediction by the large model, and the mapping relationship between different conclusion words and standard conclusion words is stored in the mapping relationship library.

[0029] ​The standard conclusion words can be stored in the annotation library alone. When determining the standard conclusion words, the abnormal conclusion words in the department summary content of the physical examination report sample can be extracted based on the large model. The position words and degree words can be removed during extraction, such as “pulmonary upper lobe nodule”. The word “pulmonary nodule” can be extracted. Then, the extracted abnormal conclusion words are corrected to standard conclusion words by manual correction. The corrected standard conclusion words can be stored in the standard library. Then, the standard conclusion words can be selected from the standard library when the mapping relationship is constructed.

[0030] Step 103: merging the standard conclusion words according to the specified merging rule by using the merging rule engine.

[0031] As an optional implementation manner of the embodiment, the standard conclusion words are merged according to a plurality of sub-merging rules in different merging rules. The merging rules include disease merging rules, human system structure information merging rules, and department information merging rules. The disease merging rules include sub-merging rules configured according to different diseases. The human system structure information merging rules include sub-merging rules defined according to different systems of the human body. The department information merging rules include sub-merging rules defined according to hospital departments. Each of the sub-merging rules is associated with a plurality of standard words. Each of the merging rules has different priorities. When merging, it is determined whether the standard conclusion words hit the sub-merging rules in the current merging rule. If the standard conclusion words do not hit the sub-merging rules in the current merging rule, the sub-merging rules of the next merging rule are matched in the order of priority. If the current sub-merging rule is hit, the merging is performed.

[0032] In the optional implementation manner, the merging rules can include three categories: monism merging rules, i.e., disease merging rules, human system merging rules, and department merging rules. Each rule includes a plurality of sub-merging rules. Each sub-merging rule is associated with a standard word set. The standard word set includes a plurality of standard words. For example, the disease merging rule in the monism merging rule includes: the abnormal conclusion with high disease relevance or the complication caused by the disease is merged together. The human system structure information merging rule includes: the motion system, the nervous system, the endocrine system, the circulatory system, the respiratory system, the digestive system, the urinary system, and the reproductive system merging rule. The department information merging rule includes: the abnormal conclusions of the same hospital department are merged together, such as the ophthalmology department, the ear-nose-throat department, and the like.

[0033] The disease combination rules include, but are not limited to, a hypertension sub-combination rule, a diabetes sub-combination rule, a blood fat sub-combination rule, and the like, wherein the hypertension sub-combination rule can include a word set composed of standard words such as hypertension, bilateral carotid arteriosclerosis, and tortuous vertebral artery; the diabetes sub-combination rule can include a word set composed of standard words such as diabetes, high normal fasting blood glucose, and increased glycosylated hemoglobin; and the blood fat sub-combination rule can include a word set composed of standard words such as hypertriglyceridemia, hyperlipidemia, and aortic arteriosclerosis. The human system combination rules can include a respiratory system sub-combination rule and a digestive system sub-combination rule, wherein the respiratory system sub-combination rule includes a word set composed of standard words such as chronic pneumonia, bronchial disease, and pulmonary nodule; and the digestive system sub-combination rule includes a word set composed of standard words such as positive fecal occult blood test and chronic non-atrophic gastritis. The department sub-combination rules include a word set composed of standard words such as ophthalmology and stomatology, wherein the ophthalmology includes a word set composed of standard words such as corneal edema and cataract; and the stomatology includes a word set composed of standard words such as dental calculus, dental caries, and tooth loss.

[0034] Further, each combination rule is provided with a priority, and the higher the priority, the earlier the matching order, and the priority relationship is as follows: the priority of the monism combination rule is higher than that of the human system combination rule, and the priority of the human system combination rule is higher than that of the department combination rule. The obtained standardized conclusion words are matched and combined according to the priority of the combination rules, and the combination is preferentially performed on the standard conclusion words with high priority. If the standard conclusion words do not hit the current combination rule, the next combination rule is matched in sequence until the matching is successful or all the combination rules are matched.

[0035] As an optional implementation manner of the embodiment, the judgment on whether the standard conclusion word hits the sub-combination rule in the current combination rule during the combination includes: judging whether the standard conclusion word hits the standard word associated with the sub-combination rule in the current rule.

[0036] In the optional implementation manner, the standard conclusion word is matched with the standard word since the sub-combination rule is associated with a standard word set.

[0037] As an optional implementation manner of the embodiment, after the judgment on whether the standard conclusion word hits the sub-combination rule in the current rule, the standard conclusion words hitting multiple standard words are combined for the same sub-rule in the same rule, and the combination of the conclusion words corresponding to the standard conclusion words is performed during the combination.

[0038] In the optional implementation manner, the conclusion words are combined for the case that the standard conclusion words hitting multiple standard words exist for the same sub-rule, and the combination of the conclusion words corresponding to the standard conclusion words hitting the standard words is performed, such as splicing the conclusion words by a connection symbol.

[0039] For example, if the two standard conclusion words are lung nodules and emphysema, and the two standard conclusion words can simultaneously hit the standard words of the respiratory system sub-merge rule (a sub-merge rule in the human body system merge rule), then the corresponding conclusion words of the small nodule lesion in the left lower lobe of the lung, considering the Lu-RADS2 category and the left upper lung paraseptal emphysema, are spliced ​​as the final merge result.

[0040] As an optional implementation of this embodiment, for the same sub-rule in the same rule, if the number of standard conclusion words that hit the standard word is one, the conclusion word corresponding to the standard conclusion word is directly used as the result.

[0041] In this optional implementation, for the same sub-rule, if the number of hits is one instead of multiple, the conclusion word corresponding to the standard conclusion word can be directly determined as the final result without involving the merging under the sub-rule.

[0042] As an optional implementation method of this embodiment, when the model is adjusted: obtain a physical examination report sample to extract abnormal conclusion words from the sample; construct a prompt based on the abnormal conclusion words, and use the prompt as the input of the model; construct an output based on the standard conclusion words mapped to the abnormal conclusion words; and train the model using the input and the output.

[0043] In this optional implementation, each piece of training data consists of an input and an output. The input is generated by a prompt template and a set of conclusion words, and the output is generated by an output template and the standard conclusion words corresponding to this set of conclusion words. When constructing training data, since it is necessary to determine the input and output, the mapping relationship between the conclusion words and the standard conclusion words in the mapping relationship library can be used as a reference to implement the construction of training data. Figure 2 Schematic diagram of the indicated conclusion words and standard conclusion words.

[0044] For example, consider a set of conclusion words: multiple pulmonary nodules, considering the LU-RADS 2-3S category; multiple gallbladder stones. Based on the mapping relationship, their corresponding standardized conclusion words are: pulmonary nodules; gallbladder stones. The input data might be: You are an excellent chief medical examiner, and now you find conclusion words separated by | in the general inspection report. You are required to refine the description of the abnormal item into standard conclusion words that conform to medical knowledge conventions. Standard conclusion words should not contain positional information, quantifiers, or modifiers. Conclusion words: multiple pulmonary nodules, LU-RADS 2-3S category; multiple gallbladder stones. What are the standard conclusion words for these conclusion words? The results are returned as JSONArray data, with the JSON object keys being the conclusion words and the values ​​being the standard conclusion words. The corresponding output data is as follows: [{"multiple pulmonary nodules, LU-RADS 2-3S category":"pulmonary nodules"},{"multiple gallbladder stones":"gallbladder stones"}].

[0045] The trained large model can be deployed on a server and provided as an API service. By calling the large model API, the conclusion words in the physical examination results in the physical examination report are standardized to obtain standardized conclusion words. The mapping relationship between the conclusion words and the standard conclusion words is stored in the database, which serves as a cache and facilitates manual correction in the later stage.

[0046] Reference Figure 3 The system architecture suitable for the method of the embodiment is illustrated, including a large model fine-tuning module, a large model service module, a conclusion intelligent merging module, and a merging rule engine module. When merging the conclusions, the LLM service can be called to extract multiple conclusion words in the physical examination report, and then the LLM service is called to determine multiple standard conclusion words corresponding to the multiple conclusion words. In the merging rule engine, the multiple standard conclusion words are merged according to the merging rule to output the final merged conclusion.

[0047] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0048] According to the embodiment of the present application, a physical examination conclusion merging device is also provided, which includes: a conclusion word extraction unit configured to obtain a physical examination report and extract conclusion words in the physical examination report; a prediction unit configured to construct a prompt based on the conclusion words and input the prompt into an adjusted model to determine a standard conclusion word corresponding to the conclusion word from a word library based on the prompt; and a merging unit configured to merge the standard conclusion words according to a specified merging rule using a merging rule engine.

[0049] As an optional implementation manner of the embodiment, when the model is adjusted: a physical examination report sample is obtained to extract abnormal conclusion words from the sample; a prompt is constructed based on the abnormal conclusion words, and the prompt is used as the input of the model; an output is constructed based on the standard conclusion words mapped by the abnormal conclusion words; and the model is trained using the input and the output.

[0050] As an optional implementation of the embodiment, the merging unit is configured to merge the standard conclusion words according to a plurality of sub-merging rules in different merging rules, wherein the merging rules include a disease merging rule, a human system structure information merging rule, and a department information merging rule; the disease merging rule includes sub-merging rules configured according to different diseases; the human system structure information merging rule includes sub-merging rules defined according to different systems of the human body; the department information merging rule includes sub-merging rules defined according to departments of a hospital; each of the sub-merging rules is associated with a plurality of standard words; wherein each of the merging rules has different priorities, and whether the standard conclusion words hit the sub-merging rule in the current merging rule is determined when merging; if the standard conclusion words do not hit the sub-merging rule in the current merging rule, the sub-merging rule of the next merging rule is matched according to the priority order; if the current sub-merging rule is hit, merging is performed.

[0051] As an optional implementation of the embodiment, determining whether the standard conclusion words hit the sub-merging rule in the current merging rule when merging includes determining whether the standard conclusion words hit the standard associated with the sub-merging rule in the current rule.

[0052] As an optional implementation of the embodiment, after the determination of whether the standard conclusion words hit the sub-merging rule in the current rule, a plurality of standard conclusion words that hit a plurality of standard words are merged for the same sub-merging rule in the same rule, wherein the conclusion words corresponding to the standard conclusion words are merged when merging.

[0053] As an optional implementation of the embodiment, for the same sub-merging rule in the same rule, if the number of the standard conclusion words that hit the standard words is one, the conclusion word corresponding to the standard conclusion word is directly taken as the result.

[0054] According to the embodiments of the present application, the present application further provides an electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to implement the object method described in any of the above embodiments.

[0055] According to the embodiments of the present application, the present application further provides a readable storage medium, which stores computer instructions for enabling a computer to implement the object matching method described in any of the above embodiments when the computer executes the computer instructions.

[0056] According to the embodiments of the present application, the present application further provides a computer program product, which can implement the method described in any of the above embodiments when executed by a processor.

[0057] Figure 4A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0058] like Figure 4 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0059] Various components in device 300 are connected to I / O interface 305, including: an input unit 306, such as a keyboard, mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, optical disk, etc.; and a communication unit 309, such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0060] The computing unit 301 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as the object matching method. For example, in some embodiments, the object matching method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method described above can be performed.

[0061] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0062] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0063] In the context of the present application, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, 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.

Claims

1. A method for merging physical examination conclusions, characterized in that: include: Obtaining a physical examination report and extracting conclusion words from the physical examination report, wherein the conclusion words are abnormal conclusion words extracted from the department summary in the physical examination report; Constructing a prompt based on the conclusion word, and inputting the prompt into the adjusted model, so that the model can determine a standard conclusion word corresponding to the conclusion word from a vocabulary based on the prompt; Utilizing a merge rule engine to merge the standard conclusion words according to a specified merge rule includes merging the standard conclusion words according to a plurality of sub-merge rules in different merge rules, wherein the merge rule includes a symptom merge rule, a human body system structure information merge rule, and a department information merge rule; the symptom merge rule includes sub-merge rules configured according to different diseases; the human body system structure information merge rule includes sub-merge rules defined according to different human body systems; the department information merge rule includes sub-merge rules defined according to hospital departments; each of the sub-merge rules is associated with a plurality of standard words; wherein each merge rule has a different priority, and when merging, it is judged whether the standard conclusion word hits the sub-merge rule in the current merge rule; if the standard conclusion word does not hit the sub-merge rule in the current merge rule, then the sub-merge rule of the next merge rule is matched according to the priority order; if the current sub-merge rule is hit, then the merge is performed; During merging, judging whether the standard conclusion word hits the sub-merge rule in the current merge rule includes: judging whether the standard conclusion word hits the standard word associated with the sub-merge rule in the current rule; The method further includes: after determining whether a hit is made, for the same sub-rule in the same rule, merging multiple standard conclusion words that hit multiple standard words, wherein during the merging, conclusion words corresponding to the standard conclusion words are merged; For the same sub-rule in the same rule, if the number of standard conclusion words that hit the standard word is one, the conclusion word corresponding to the standard conclusion word is directly used as the result.

2. The method for merging physical examination conclusions according to claim 1, characterized in that: When the model is being adjusted: obtaining a sample of a medical examination report to extract abnormal conclusion words from the sample; Constructing a prompt based on the abnormal conclusion word, and using the prompt as an input to the model; Constructing an output based on the standard conclusion word mapping of the abnormal conclusion word; The model is trained using the input and the output.

3. A physical examination conclusion merging device, characterized in that: include: a conclusion word extraction unit, configured to obtain a physical examination report and extract conclusion words from the physical examination report; a prediction unit configured to construct a prompt based on the conclusion word, and input the prompt into the adjusted model, so that the model determines a standard conclusion word corresponding to the conclusion word from a vocabulary based on the prompt; The merging unit is configured to use a merging rule engine to merge the standard conclusion words according to a specified merging rule, and the merging of the standard conclusion words according to the specified merging rule by using the merging rule engine includes merging the standard conclusion words according to multiple sub-merging rules in different merging rules, wherein the merging rule includes a symptom merging rule, a human body system structure information merging rule, and a department information merging rule; the symptom merging rule includes sub-merging rules configured according to different diseases; the human body system structure information merging rule includes sub-merging rules defined according to different human body systems; the department information merging rule includes sub-merging rules defined according to hospital departments; each of the sub-merging rules is associated with multiple standard words; wherein each merging rule has a different priority, and when merging, it is judged whether the standard conclusion word hits the sub-merging rule in the current merging rule; if the standard conclusion word does not hit the sub-merging rule in the current merging rule, then the sub-merging rule of the next merging rule is matched in order of priority; if the current sub-merging rule is hit, then merging is performed; During merging, judging whether the standard conclusion word hits the sub-merge rule in the current merge rule includes: judging whether the standard conclusion word hits the standard word associated with the sub-merge rule in the current rule; The method further includes: after determining whether a hit is made, for a same sub-rule in a same rule, merging a plurality of standard conclusion words that hit a plurality of standard words, wherein during the merging, conclusion words corresponding to the standard conclusion words are merged; For the same sub-rule in the same rule, if the number of standard conclusion words that hit the standard word is one, the conclusion word corresponding to the standard conclusion word is directly used as the result.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the physical examination conclusion merging method according to any one of claims 1-2.

5. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the physical examination conclusion merging method described in any one of claims 1-2.

6. A computer program product, characterized in that The method comprises a computer program, which, when executed by a processor, implements the method for merging physical examination conclusions according to any one of claims 1 to 2.

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