Re-visit information sending method and device, equipment, medium and program product

Abstract

The embodiment of the invention discloses a re-visit information sending method and device, equipment, a medium and a program product. A specific embodiment of the method comprises the following steps: acquiring doctor-seeing data and a diagnosis rule information set sent by a doctor-seeing terminal; performing index format conversion and diagnosis word format conversion on the treatment indexes and the diagnosis words in the treatment data to obtain index conversion data and diagnosis word conversion data; screening target diagnosis rule information meeting a target hit condition from the diagnosis rule information set by utilizing a rule matching engine; determining re-diagnosis information corresponding to the target diagnosis rule information; and sending the re-consultation information to a use terminal corresponding to the doctor-seeing object, the doctor-seeing data and the doctor-seeing object having a doctor-seeing relationship. The embodiment is related to intelligent medical treatment, and the diagnosis rule information set is utilized to quickly and efficiently determine the re-consultation information corresponding to the doctor seeing data so as to push the re-consultation information to the doctor seeing object for re-consultation processing.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, apparatus, device, medium, and program product for sending follow-up visit information. Background Art

[0002] At present, in scenarios such as hospital visits, offline physical examinations, and online consultations, users are often provided with corresponding follow-up diagnosis suggestions based on the user's initial diagnosis results. The usual method for generating follow-up diagnosis suggestions is: first, determine the indicator content and diagnostic terms under each indicator corresponding to the user's initial diagnosis results. Then, determine the target rule information corresponding to the indicator content and diagnostic terms through an exhaustive method. Finally, send the follow-up information corresponding to the target rule information to the user's corresponding terminal.

[0003] However, the inventors have found that when the above method is adopted, the following technical problems often occur:

[0004] As the number of users corresponding to various indicators increases, the process of determining the corresponding target rule information becomes longer, resulting in inefficient determination of follow-up information and a waste of a lot of time.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose methods, devices, equipment, media and program products for sending follow-up information to solve the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for sending follow-up visit information, including: obtaining medical data sent by a medical terminal and a pre-set diagnosis rule information set; performing indicator format conversion and diagnosis word format conversion on the medical indicators and diagnosis words in the above medical data, respectively, to obtain indicator conversion data in the form of a key-value pair set and diagnosis word conversion data in the form of a key-value pair set; using a rule matching engine to filter out target diagnosis rule information that meets the target hit condition from the above diagnosis rule information set, wherein the above target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, there is a rule hit relationship between the indicator conversion data and the above target indicator rule information, and there is a rule hit relationship between the diagnosis word conversion data and the above target diagnosis word rule information; determining the follow-up visit information corresponding to the above target diagnosis rule information; sending the follow-up visit information to the user terminal corresponding to the medical object, wherein the medical data has a medical relationship with the above medical object.

[0009] Optionally, the above-mentioned medical indicators and diagnostic words in the above-mentioned medical data are respectively converted into indicator formats and diagnostic word formats to obtain indicator conversion data in the form of a key-value pair set and diagnostic word conversion data in the form of a key-value pair set, including: obtaining the medical consultation mapping relationship corresponding to the above-mentioned medical consultation terminal; according to the above-mentioned medical consultation mapping relationship, performing indicator mapping processing and diagnostic word mapping processing on the above-mentioned medical indicators and the above-mentioned diagnostic words respectively to generate mapping indicators and mapping diagnostic words; converting the above-mentioned mapping indicators and the above-mentioned mapping diagnostic words into a key-value pair set to generate indicator conversion data and diagnostic word conversion data.

[0010] Optionally, the above-mentioned indicator conversion data includes: at least one indicator sub-data in the form of a key-value pair, the above-mentioned diagnostic word conversion data includes: at least one diagnostic sub-data in the form of a key-value pair, and the diagnostic rule information is a diagnostic rule expression; and the above-mentioned target diagnostic rule information that satisfies the target hit condition is filtered out from the above-mentioned diagnostic rule information set, including: for each diagnostic rule information in the above-mentioned diagnostic rule information set, the following generation steps are performed: for each indicator sub-data, the above-mentioned indicator sub-data is substituted into the corresponding indicator condition in the corresponding diagnostic rule expression to obtain a first data comparison result; for each diagnostic sub-data, the above-mentioned diagnostic sub-data is substituted into the corresponding diagnostic condition in the above-mentioned diagnostic rule expression to obtain a second data comparison result; based on the obtained at least one first data comparison result and at least one second data comparison result, a data comprehensive comparison result is generated; and the diagnostic rule information whose corresponding data comprehensive comparison result is the target result is filtered out from the above-mentioned diagnostic rule information set as the target diagnostic rule information.

[0011] Optionally, the above-mentioned generation of a comprehensive data comparison result based on at least one first data comparison result and at least one second data comparison result includes: determining the number of first results corresponding to the first data comparison results in the above-mentioned at least one first data comparison result that characterizes that the data content meets the indicator condition; determining the number of second results corresponding to the second data comparison results in the above-mentioned at least one second data comparison result that characterizes that the data content meets the diagnostic condition; generating the above-mentioned comprehensive data comparison result based on the above-mentioned number of first results and the number of second results.

[0012] Optionally, the above-mentioned medical data also includes: an object parameter data set and a historical diagnosis data set; and the above-mentioned data comprehensive comparison result is generated based on the above-mentioned first result number and the second result number, including: generating a third data comparison result set corresponding to the above-mentioned object parameter data set; generating a fourth data comparison result set corresponding to the above-mentioned historical diagnosis data set; determining the number of third results corresponding to the third data comparison results in the above-mentioned third data comparison result set that represent that the data content meets the object parameter conditions; determining the number of fourth results corresponding to the fourth data comparison results in the above-mentioned fourth data comparison result set that represent that the data content meets the historical diagnosis conditions; for each diagnosis rule information, the corresponding first result number, the corresponding second result number, the above-mentioned third result number and the fourth result number are weightedly summed to generate a weighted number as the above-mentioned data comprehensive comparison result.

[0013] Optionally, the above-mentioned screening out of the diagnostic rule information set whose corresponding data comprehensive comparison result is the target result as the target diagnostic rule information includes: in response to the diagnostic rule information set having the largest corresponding data comprehensive comparison result being the target number, determining the diagnostic rule information having the largest data comprehensive comparison result as the target diagnostic rule information. In response to the diagnostic rule information set having the largest corresponding data comprehensive comparison result being greater than the target number, determining at least one diagnostic rule information having the largest data comprehensive comparison result; screening out the diagnostic rule information having the largest corresponding rule importance from the at least one diagnostic rule information as the target diagnostic rule information.

[0014] Optionally, the diagnosis rule information in the above-mentioned diagnosis rule information set is rule information pre-maintained based on the medical consultation mapping relationship corresponding to the above-mentioned medical consultation terminal, and the diagnosis rule information is one of the following: rule information of numerical type, rule information of diagnosis word type, rule information of yin-yang type, rule information of Roman numeral type, rule information of positivity degree type, rule information based on diagnosis word type and single numerical type, rule information based on diagnosis word type and double numerical type, rule information based on yin-yang type and numerical type, rule information based on object parameter type, rule information of disease history type.

[0015] In a second aspect, some embodiments of the present disclosure provide a device for sending follow-up information, including: an acquisition unit, configured to acquire medical data and a pre-set diagnosis rule information set sent by a medical terminal; a conversion unit, configured to perform indicator format conversion and diagnosis word format conversion on the medical indicators and diagnosis words in the above medical data, respectively, to obtain indicator conversion data in the form of a key-value pair set and diagnosis word conversion data in the form of a key-value pair set; a screening unit, configured to use a rule matching engine to screen out target diagnosis rule information that meets the target hit condition from the above diagnosis rule information set, wherein the above target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, there is a rule hit relationship between the indicator conversion data and the above target indicator rule information, and there is a rule hit relationship between the diagnosis word conversion data and the above target diagnosis word rule information; a determination unit, configured to determine the follow-up information corresponding to the above target diagnosis rule information; a sending unit, configured to send the follow-up information to a user terminal corresponding to the medical object, wherein the medical data has a medical relationship with the medical object.

[0016] Optionally, the conversion unit can be configured to: obtain the medical consultation mapping relationship corresponding to the above-mentioned medical consultation terminal; perform indicator mapping processing and diagnostic word mapping processing on the above-mentioned medical consultation indicators and the above-mentioned diagnostic words according to the above-mentioned medical consultation mapping relationship, so as to generate mapping indicators and mapping diagnostic words; convert the above-mentioned mapping indicators and the above-mentioned mapping diagnostic words into a key-value pair set form, so as to generate indicator conversion data and diagnostic word conversion data.

[0017] Optionally, the above-mentioned indicator conversion data includes: at least one indicator sub-data in the form of a key-value pair, the above-mentioned diagnostic word conversion data includes: at least one diagnostic sub-data in the form of a key-value pair, and the diagnostic rule information is a diagnostic rule expression; and the screening unit can be configured to: for each diagnostic rule information in the above-mentioned diagnostic rule information set, perform the following generation steps: for each indicator sub-data, substitute the above-mentioned indicator sub-data into the corresponding indicator condition in the corresponding diagnostic rule expression to obtain a first data comparison result; for each diagnostic sub-data, substitute the above-mentioned diagnostic sub-data into the corresponding diagnostic condition in the above-mentioned diagnostic rule expression to obtain a second data comparison result; generate a data comprehensive comparison result based on the at least one first data comparison result and the at least one second data comparison result obtained; and screen out the diagnostic rule information whose corresponding data comprehensive comparison result is the target result from the above-mentioned diagnostic rule information set as the target diagnostic rule information.

[0018] Optionally, the screening unit can be configured to: generate a comprehensive data comparison result based on the at least one first data comparison result and the at least one second data comparison result obtained, including: determining the number of first results corresponding to the first data comparison results in the at least one first data comparison result that characterizes that the data content meets the indicator conditions; determining the number of second results corresponding to the second data comparison results in the at least one second data comparison result that characterizes that the data content meets the diagnostic conditions; generating the comprehensive data comparison result based on the number of first results and the number of second results.

[0019] Optionally, the above-mentioned medical data also includes: an object parameter data set and a historical diagnosis data set; and the screening unit can be configured to: generate a third data comparison result set corresponding to the above-mentioned object parameter data set; generate a fourth data comparison result set corresponding to the above-mentioned historical diagnosis data set; determine the number of third results corresponding to the third data comparison results in the above-mentioned third data comparison result set that represent that the data content meets the object parameter conditions; determine the number of fourth results corresponding to the fourth data comparison results in the above-mentioned fourth data comparison result set that represent that the data content meets the historical diagnosis conditions; for each diagnosis rule information, perform weighted summation on the corresponding first result number, the corresponding second result number, the above-mentioned third result number and the fourth result number to generate a weighted number as the comprehensive comparison result of the above-mentioned data.

[0020] Optionally, the screening unit can be configured to: screen out the diagnostic rule information whose corresponding data comprehensive comparison result is the target result from the diagnostic rule information set as the target diagnostic rule information, including: in response to the diagnostic rule information with the largest corresponding data comprehensive comparison result in the diagnostic rule information set being the target number, determining the diagnostic rule information with the largest data comprehensive comparison result as the target diagnostic rule information. In response to the diagnostic rule information with the largest corresponding data comprehensive comparison result in the diagnostic rule information set being greater than the target number, determining at least one diagnostic rule information with the largest data comprehensive comparison result; and screening out the diagnostic rule information with the greatest corresponding rule importance from the at least one diagnostic rule information as the target diagnostic rule information.

[0021] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0022] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0023] In a fifth aspect, some embodiments of the present disclosure provide a computer program product, including a computer program, which implements the method described in any implementation manner in the above-mentioned first aspect when executed by a processor.

[0024] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the method for sending the follow-up information of some embodiments of the present disclosure, by using the diagnosis rule information set, the follow-up information corresponding to the medical data can be determined quickly and efficiently, so as to be pushed to the patient for follow-up treatment. Specifically, the reason why the relevant follow-up information is not fast and efficient is that as the number of users corresponding to various indicators increases, the corresponding process of determining the target rule information becomes longer, resulting in low efficiency in determining the follow-up information and a waste of a lot of time. Based on this, the method for sending the follow-up information of some embodiments of the present disclosure first obtains the medical data sent by the medical terminal and the pre-set diagnosis rule information set. Here, the set diagnosis rule information set is obtained to facilitate the determination of the diagnosis rule information matching the medical data. And the diagnosis rule information set is set correspondingly for each medical indicator and diagnosis word and the corresponding follow-up information, which is convenient for the query of subsequent follow-up information. Then, the medical indicators and diagnosis words in the above-mentioned medical data are converted into indicator format and diagnosis word format respectively, and the indicator conversion data and diagnosis word conversion data are obtained. Here, through the conversion of the indicator format and the diagnosis word format, the indicator format and the diagnosis word format of the consultation terminal are unified with the indicator format and the diagnosis word format involved in the diagnosis rule information set, so as to facilitate the determination of the subsequent target diagnosis rule information. In addition, through the conversion of the indicator format and the diagnosis word format, it is convenient to use the rule matching engine to filter the target diagnosis rule information. Then, using the rule matching engine, the target diagnosis rule information that meets the target hit condition can be quickly and efficiently filtered out from the above diagnosis rule information set. Among them, the above target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, there is a rule hit relationship between the above indicator conversion data and the above target indicator rule information, and there is a rule hit relationship between the above diagnosis word conversion data and the above target diagnosis word rule information. Here, based on the indicator conversion data and the diagnosis word conversion data after format conversion, the rule matching engine corresponding to the matching format can be used to realize the screening of the target diagnosis rule information in a lightweight and efficient manner. Then, the follow-up information corresponding to the above target diagnosis rule information is determined. Finally, the above follow-up information is sent to the user terminal corresponding to the patient, so that the patient can obtain the follow-up information corresponding to the consultation data in real time and efficiently. Among them, the above-mentioned medical consultation data has a medical consultation relationship with the above-mentioned medical consultation object. In summary, on the basis of pre-setting the diagnostic word rule information set, through the indicator format conversion and the diagnostic word format conversion and the use of the rule matching engine to realize the screening of the target diagnostic rule information, the corresponding follow-up consultation information can be generated in a lightweight and efficient manner, so that the medical consultation object can obtain accurate follow-up consultation information more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0026] Figure 1 is a schematic diagram of an application scenario of a method for sending follow-up consultation information according to some embodiments of the present disclosure;

[0027] Figure 2 is a flow chart of some embodiments of the method for sending follow-up consultation information according to the present disclosure;

[0028] Figure 3 is a schematic diagram of a rule setting interface in some embodiments of the method for sending follow-up consultation information disclosed herein;

[0029] Figure 4 It is a schematic diagram of setting content of a numerical type in some embodiments of the method for sending follow-up consultation information disclosed in the present invention;

[0030] Figure 5 is a schematic diagram of content setting of diagnosis word types in some embodiments of the method for sending follow-up consultation information disclosed in the present invention;

[0031] Figure 6 It is a schematic diagram of content setting of positive and negative types in some embodiments of the method for sending follow-up consultation information disclosed in the present invention;

[0032] Figure 7 It is a schematic diagram of setting content of Roman numeral type in some embodiments of the method for sending follow-up consultation information disclosed in the present invention;

[0033] Figure 8 It is a schematic diagram of content setting of diagnosis word + singular value type in some embodiments of the method for sending follow-up consultation information disclosed in the present invention;

[0034] Fig. 9 It is a schematic diagram of content setting of diagnosis word + double numerical value type in some embodiments of the method for sending follow-up consultation information disclosed in the present invention;

[0035] Fig.10 is a schematic diagram of determining the first target number of segmentation times in some embodiments of the method for sending follow-up consultation information disclosed in the present invention;

[0036] Fig.11 is a flow chart of other embodiments of the method for sending follow-up information according to the present disclosure;

[0037] Fig.12 It is a structural schematic diagram of some embodiments of the device for sending follow-up information according to the present disclosure;

[0038] Fig.13 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0039] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0040] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0041] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0042] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0043] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0044] With regard to the collection, storage, and use of user personal information (such as medical treatment data) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the authorization and consent of the personal information subject in advance.

[0045] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0046] Figure 1 It is a schematic diagram of an application scenario of the method for sending follow-up visit information according to some embodiments of the present disclosure.

[0047] exist Figure 1In the application scenario, first, the electronic device 101 can obtain the medical data 103 and the pre-set diagnosis rule information set 104 sent by the medical terminal 102. In this application scenario, the medical terminal 102 can be the medical terminal A. The medical data 103 can be the medical data A corresponding to the patient 1. Then, the electronic device 101 can perform indicator format conversion and diagnostic word format conversion on the medical indicators 105 and diagnostic words 106 in the above-mentioned medical data 103, respectively, to obtain indicator conversion data 107 and diagnostic word conversion data 108. Next, the electronic device 101 can use the rule matching engine 109 to filter out the target diagnosis rule information 110 that meets the target hit condition from the above-mentioned diagnosis rule information set 104. Among them, the above-mentioned target diagnosis rule information 110 includes: target indicator rule information and target diagnosis word rule information. There is a rule hit relationship between the above-mentioned indicator conversion data 107 and the above-mentioned target indicator rule information. There is a rule hit relationship between the above-mentioned diagnostic word conversion data 108 and the above-mentioned target diagnostic word rule information. Furthermore, the electronic device 101 can determine the follow-up information 1111 corresponding to the target diagnosis rule information 110. Finally, the electronic device 101 can send the follow-up information 111 to the user terminal 112 corresponding to the patient. Among them, the medical data 103 and the patient have a medical relationship. In this application scenario, the user terminal 112 corresponding to the patient can be the user terminal corresponding to the patient 1.

[0048] It should be noted that the electronic device 101 can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0049] It should be understood that Figure 1 The number of electronic devices in the embodiment is only for illustration. Any number of electronic devices may be provided according to implementation requirements.

[0050] Continue to refer Figure 2 , shows a process 200 of some embodiments of the method for sending follow-up information according to the present disclosure. The method for sending follow-up information includes the following steps:

[0051] Step 201, obtaining the medical consultation data sent by the medical consultation terminal and the preset diagnosis rule information set.

[0052] In some embodiments, the execution subject of the above-mentioned method for sending follow-up information (for example Figure 1The electronic device 101 shown) can obtain the medical data and the pre-set diagnostic rule information set sent by the medical terminal through a wired connection or a wireless connection. Among them, the medical terminal can be a regional terminal corresponding to the medical area. In practice, the medical area can be a target hospital or a target clinic. The regional terminal is pre-installed with an HIS (Hospital Information System) system. The medical data can be the medical results after the patient undergoes a preliminary examination. The medical data can be the medical results that have been pre-structured. That is, the medical data can be data that has been structured in the HIS system. The diagnostic rule information can be the rule information for determining the follow-up information based on the preliminary medical results. The diagnostic rule information includes: indicator rule information and diagnostic word rule information. The indicator rule information can be the indicator value requirement conditions corresponding to each medical indicator. For example, for the indicator 1, the corresponding indicator rule information can be "require the corresponding value of indicator 1 to be greater than 10". The diagnostic word rule information can be the numerical requirement conditions corresponding to each diagnostic word. For example, for the diagnostic word 1, the corresponding diagnostic word rule information can be "require the corresponding value of diagnostic word 1 to be greater than 30%". The corresponding indicators of the indicator rule information and the corresponding diagnostic terms of the diagnostic term rule information are matched. The diagnostic rule information can represent the disease conditions of the subject and the follow-up information corresponding to the corresponding disease conditions. The diagnostic rule information is used to determine which indicators are exceeded or abnormal and the diagnosis conditions, so as to facilitate the subsequent recommendation of the follow-up department and follow-up time for the patient. Diagnostic terms are usually descriptive words, involving the naming and classification of diseases. They are medical professional phrases with conclusive nature based on comprehensive information such as the patient's symptoms, signs, laboratory test results and imaging test results. For example: diabetes, hypertension, etc. Indicators are specific data or biomarkers that can be obtained through laboratory tests or other medical examinations and are used to monitor and evaluate health conditions or disease progression. For example, blood glucose concentration values, systolic blood pressure, diastolic blood pressure measurement values, etc. The diagnostic rule information set can be a rule information set pre-configured for various indicators and diagnostic terms.

[0053] In practice, diagnostic rule information can be generated through the following steps:

[0054] The first step is to add the attribution diagnostic words (i.e., the diagnostic words corresponding to the target rule) in the rule setting interface corresponding to the target rule. The selectable items corresponding to the attribution diagnostic words are the diagnostic words in the standard diagnostic word list. The diagnostic words in the standard diagnostic word list are a list of diagnostic words obtained by summarizing the comments when the doctor writes the consultation summary. The standard diagnostic word list is constantly maintained and updated. The target rule can be the diagnostic rule to be set and generated.

[0055] The second step is to select the corresponding test indicators (i.e. diagnostic indicators) and the data corresponding to the diagnostic words in the rule setting interface according to the assigned diagnostic words. Each diagnostic word has at least one corresponding diagnostic indicator. Similarly, each diagnostic indicator can correspond to a different diagnostic word. The data corresponding to the diagnostic word can be restricted data that further restricts the content of the assigned diagnostic word. For example, moderate stenosis of the internal carotid artery lumen by 30%.

[0056] The third step is to add additional judgment adjustments such as age, gender, medical history, etc. corresponding to the attribution diagnostic words in the rule setting interface to improve the matching accuracy of the attribution diagnostic words.

[0057] The fourth step is to add the attention level (i.e., the importance of the rule) in the rule setting interface. Here, the importance of the rule can represent the priority level corresponding to the target rule, that is, it can also represent the severity of the corresponding diagnosed disease.

[0058] Step 5. Add the corresponding follow-up department and follow-up examination items in the rule setting interface.

[0059] The sixth step is to save the information in the set rule setting interface and convert the page rule language into a program rule language to generate diagnostic rule information.

[0060] See also Figure 3 , which shows a schematic diagram of the rule setting interface.

[0061] For example, medical visit data could be:

[0062] Patient information: Zhang San, male, 60 years old

[0063] Date of visit: [2023-04-12]

[0064] Chief complaint: [Headache and nausea]

[0065] Past medical history: [hypertension, diabetes]

[0066] Check results:

[0067] Systolic blood pressure:

[150] mmHg

[0068] Heart rate:

[88] bpm

[0069] Body temperature: [37.2]℃

[0070] Respiratory rate:

[18] times / minute

[0071] [No obvious jaundice, no abnormality in heart and lung auscultation]

[0072] Laboratory test results:

[0073] Blood routine test:

[0074] White blood cell count: [9.5]x10^9 / L (normal range: 4-10x10^9 / L)

[0075] Red blood cell count: [4.8]x10^12 / L (normal range: 4.2-5.4x10^12 / L)

[0076] Hemoglobin concentration:

[142] g / L (normal range: 120-160 g / L)

[0077] Platelet count:

[280] x10^9 / L (normal range: 100-300x10^9 / L)

[0078] Biochemical indicators:

[0079] Liver function: ALT

[45] U / L, AST

[38] U / L (normal range: ALT 0-40 U / L, AST 0-40 U / L)

[0080] Renal function: serum creatinine

[85] μmol / L (normal range: 44-133 μmol / L)

[0081] Blood sugar: [7.8]mmol / L (normal fasting blood sugar range: 3.9-6.1mmol / L)

[0082] Imaging examination:

[0083] Head CT: [No obvious brain parenchymal lesions were found]

[0084] Conclusion of the visit:

[0085] According to the patient's clinical manifestations, physical examination and auxiliary examination results, the preliminary diagnosis was [intermittent headache] caused by hypertension. The patient's ALT value was

[45] U / L, slightly higher than the normal range, suggesting the possibility of [abnormal liver function]. At the same time, the patient's blood glucose value was [7.8] mmol / L, higher than the normal fasting blood glucose range, suggesting the possibility of [diabetes].

[0086] In some optional implementations of some embodiments, the diagnosis rule information in the above-mentioned diagnosis rule information set is rule information pre-maintained based on the medical consultation mapping relationship corresponding to the above-mentioned medical consultation terminal. The diagnosis rule information includes at least one of the following: sub-rule information of numerical type, sub-rule information of diagnosis word type, sub-rule information of yin-yang type, sub-rule information of Roman numeral type, sub-rule information of positivity type, sub-rule information based on diagnosis word type and single numerical type, sub-rule information based on diagnosis word type and double numerical type, sub-rule information based on yin-yang type and numerical type, sub-rule information of object parameter type, and sub-rule information of disease history type.

[0087] Here, the diagnosis rule information set is the rule information for periodic rule maintenance. That is, as the diagnosis requirements and indicator requirements corresponding to the medical terminal are updated, the diagnosis requirements and indicator requirements of the updated diagnosis rule information in the diagnosis rule information set are updated through the medical mapping relationship. The medical mapping relationship can characterize the mapping relationship between the corresponding indicators and diagnostic words of the execution subject (which can be the terminal where the operation end is located) and the indicators and diagnostic words in the medical terminal. The sub-rule information of the numerical type can be the sub-rule information of the indicator and / or diagnostic word corresponding to the value range of the numerical type. For example, for indicator A, the corresponding value range is the value range of the numerical type, and the corresponding sub-rule information can be in the form of [Indicator Code] [Condition] [Value]. The indicator Code can be the code corresponding to the indicator. The condition can be the indicator requirement. For example, the condition is an interval type, such as: Fully open: minimum value < [SC00001] && [SC00001] < maximum value. Left closed and right open: minimum value <= [SC00001] && [SC00001] < maximum value. Left open and right closed: minimum value < [SC00001] && [SC00001] <= maximum value. Fully closed: minimum value <= [SC00001] && [SC00001] <= maximum value. SC00001 can be the indicator Code corresponding to the indicator. The sub-rule information of the diagnostic word type can be the rule information for determining whether the target diagnostic word exists. The sub-rule information of the diagnostic word type can be [StringUtil.equals (diagnostic word Code, "diagnostic word")]. StringUtil can be a method of the java tool class StringUtil. The sub-rule information of the yin-yang type can be the rule information for judging whether the indicator or diagnostic word is a yin-yang type. In practice, the sub-rule information of the yin-yang type can be [StringUtil.equals (diagnostic word Code or indicator Code, "positive / negative")]. For example, the sub-rule information of the yin-yang type can be [StringUtil.equals (SC00001, "negative")].

[0088] The sub-rule information of the Roman numeral type may be the rule information of the indicator judged by the Roman numeral type. The Roman numerals here represent the level corresponding to the indicator. In practice, the sub-rule information of the Roman numeral type may be [StringUtil.equlas(Indicator Code, I)||StringUtil.equlas(Indicator Code, II)||…]. What this means is that the indicator level corresponding to the indicator Code can select the level corresponding to I, or the level corresponding to II, and so on. The sub-rule information of the positive degree type may be the rule information of the indicator judged by the positive degree type. The positive degree here represents the positive severity corresponding to the indicator. In practice, the sub-rule information of the positive degree type may be [StringUtil.equlas(Indicator Code, 1+)||StringUtil.equlas(Indicator Code, 2+)||…].

[0089] The sub-rule information based on the diagnostic word type and the single value type can be the rule information of the type under the diagnostic word and the value restriction. For example, the diagnostic word here is blood glucose concentration. The specific example of the type under the diagnostic word and the value restriction is blood glucose concentration>6. In practice, the sub-rule information based on the diagnostic word type and the single value type can be [diagnostic word Code> / < / = / > = / <=target value]. For example, when the numerical interval corresponding to the diagnostic word is fully open, the corresponding sub-rule information may be minimum value <[diagnostic word Code] && [diagnostic word Code] < maximum value. When the numerical interval corresponding to the diagnostic word is closed on the left and open on the right, the corresponding sub-rule information may be minimum value <=[diagnostic word Code] && [diagnostic word Code] < maximum value. When the numerical interval corresponding to the diagnostic word is open on the left and closed on the right, the corresponding sub-rule information may be minimum value <[diagnostic word Code] && [diagnostic word Code] <= maximum value. When the numerical interval corresponding to the diagnostic word is fully closed, the corresponding sub-rule information may be minimum value <=[diagnostic word Code] && [diagnostic word Code] <= maximum value.

[0090] The sub-rule information based on the diagnostic word type and the dual-numeric type can be the rule information of the type under the restriction of the diagnostic word and two numerical values. In practice, the sub-rule information based on the diagnostic word type and the dual-numeric type can be: [diagnostic word code] [length and width / length or width] [condition] [value]. Length and width are two numerical values ​​in actual scenarios, so when parsing the rule information, it is necessary to parse the length and width into two indicators, and add _1 to represent length and _2 to represent width after the diagnostic word code. Therefore, the sub-rule information based on the diagnostic word type and the dual-numeric type is: ordinary judgment of length and width [diagnostic word Code_1> / < / = / > = / <=Numerical value&&Diagnosis word Code_2> / < / = / > = / <=value]. Length or width general judgment [diagnosis word Code_1> / < / = / > = / <=Numerical value||Diagnostic word Code_2> / < / = / > = / <=value].

[0091] Specifically, the sub-rule information for the corresponding length and width is:

[0092] When fully open: (minimum value < [diagnostic word Code_1] && [diagnostic word Code_1] < maximum value) && (minimum value < [diagnostic word Code_2] && [diagnostic word Code_2] < maximum value).

[0093] In the case of left closing and right opening: (minimum value <= [diagnosis word Code_1] && [diagnosis word Code_1] < maximum value) && (minimum value <= [diagnosis word Code_2] && [diagnosis word Code_2] < maximum value).

[0094] In the case of left opening and right closing: (minimum value < [diagnosis word Code_1] && [diagnosis word Code_1] <= maximum value) && (minimum value < [diagnosis word Code_2] && [diagnosis word Code_2] <= maximum value).

[0095] In the fully closed case: (minimum value <= [diagnosis word Code_1] && [diagnosis word Code_1] <= maximum value) && (minimum value <= [diagnosis word Code_2] && [diagnosis word Code_2] <= maximum value).

[0096] The sub-rule information for the length or width is:

[0097] When fully open: (minimum value < [diagnostic word Code_1] && [diagnostic word Code_1] < maximum value) || (minimum value < [diagnostic word Code_2] && [diagnostic word Code_2] < maximum value).

[0098] In the case of left closing and right opening: (minimum value <= [diagnosis word Code_1] && [diagnosis word Code_1] < maximum value) || (minimum value <= [diagnosis word Code_2] && [diagnosis word Code_2] < maximum value).

[0099] In the case of left opening and right closing: (minimum value < [diagnosis word Code_1] && [diagnosis word Code_1] <= maximum value) || (minimum value < [diagnosis word Code_2] && [diagnosis word Code_2] <= maximum value).

[0100] In the fully closed case: (minimum value <= [diagnosis word Code_1] && [diagnosis word Code_1] <= maximum value) || (minimum value <= [diagnosis word Code_2] && [diagnosis word Code_2] <= maximum value).

[0101] The sub-rule information based on the positive and negative type and the numerical type can be the sub-rule information under the restriction of the positive and negative type and the single numerical value corresponding to the indicator. The conditional judgment is made by adding _1 to the code suffix to represent positive and _2 to represent negative.

[0102] In practice, the sub-rule information based on the positive and negative type and the value type can be: positive general judgment [indicator Code_1> / < / = / > = / <=value].

[0103] For the positive interval, the corresponding examples are as follows:

[0104] When fully open: minimum value < [Indicator Code_1] && [Indicator Code_1] < maximum value.

[0105] In the case of left closing and right opening: minimum value <= [Indicator Code_1] && [Indicator Code_1] < maximum value.

[0106] In the case of left-open and right-closed: minimum value < [Indicator Code_1] && [Indicator Code_1] <= maximum value.

[0107] In the fully closed case: minimum value <= [Indicator Code_1] && [Indicator Code_1] <= maximum value.

[0108] For the negative interval, the corresponding examples are as follows:

[0109] When fully open: minimum value < [Indicator Code_2] && [Indicator Code_2] < maximum value.

[0110] In the case of left closing and right opening: minimum value <= [Indicator Code_2] && [Indicator Code_2] < maximum value.

[0111] In the case of left open and right closed: minimum value < [Indicator Code_2] && [Indicator Code_2] <= maximum value.

[0112] In the fully closed case: minimum value <= [Indicator Code_2] && [Indicator Code_2] <= maximum value.

[0113] The sub-rule information of the object parameter type may be the sub-rule information for the object parameter. Here, the object parameter may be each parameter related to the object that is pre-set. For example, the object parameter may include: a gender parameter and an age parameter. The sub-rule information of the object parameter type may include: sub-rule information corresponding to the gender parameter and sub-rule information corresponding to the age parameter. For the sub-rule information corresponding to the gender parameter, please refer to the relevant schematic diagram corresponding to the sub-rule information of the numerical type. For the sub-rule information corresponding to the age parameter, please refer to the relevant schematic diagram corresponding to the sub-rule information of the diagnostic word type. The sub-rule information of the disease history type may be the sub-rule information set for the disease history of the object.

[0114] For the setting of sub-rule information of disease history type, first, maintain a set of disease standard ICD (International Classification of Diseases) in the database. ICD code is a universal code that associates the hospital's diseases with ICD. Then, when configuring the disease list, use the standard ICD code to record each disease. Finally, store the sub-rule information composed of standard ICD.

[0115] The judgment criteria for the generated sub-rule information is: using CollectionUtil.containsAny, whether the disease ICD list configured on the operation side (the side where the execution subject is located) contains any disease ICDCode synchronized from the HIS system.

[0116] In practice, the sub-rule information for the disease history type is: CollectionUtil.containsAny(seq.list("1", "2", "3"), ADDITIONAL_CODE_x). This sub-rule information is complex rule information, where seq.list("1", "2", "3") represents the string 1, 2, 3 into an array List. The CollectionUtil.containsAny method has two parameters. Parameter 1 represents sourceList, which is the disease CodeList configured on the operation side. Parameter 2 represents targetList, which is the CodeList of the disease history synchronized from the HIS system. The containsAny method will determine whether any Code in the targetList exists in the sourceList. If it exists, it returns true. At the same time, there is an "or" relationship between each type of disease, an "or" relationship between each disease type with " / ", and an "and" relationship between each type list. At the same time, the same disease history may be configured multiple times. For example, current medical history / past medical history, current medical history / family history, current medical history, current medical history appears three times. Combine the judgment of the current medical history: First, assign a unique code to each disease and add the prefix ADDITIONAL_CODE_ to indicate that the condition is an additional condition type. There are currently 5 types of diseases, namely (current medical history, past medical history, family history, surgical history, and menstrual history), and the code values ​​are 1-5 respectively. For example, ADDITIONAL_CODE_1 represents the current medical history. Then, perform the conditional splitting and combination of the medical history.

[0117] In specific practice, the following steps are included:

[0118] The first step is to split the disease types with " / ". For example, current medical history / past medical history is split into current medical history and past medical history. The ICD codes for these two diseases are the same and the two disease types are in an OR relationship. If the operator configures two diseases for current medical history / past medical history, the codes are 74 and 75 respectively. Therefore, the final output expression is:

[0119] CollectionUtil.containsAny(seq.list(“74”,“75”),ADDITIONAL_CONDITION_1)||CollectionUtil.containsAny(seq.list(“74”,“75”),ADDITIONAL_CONDITION_2).

[0120] The second step is to combine the ICD codes under each disease history. That is, at most five disease history ICD code sets are obtained, namely, current medical history, past medical history, family history, surgical history, and menstrual history, and they are in an AND relationship with each other. For example, the operator configures disease codes 70 and 71 for the current medical history and disease codes 72 and 73 for the family history. The final output rule information is:

[0121] CollectionUtil.containsAny(seq.list("70","71"),ADDITIONAL_CONDITION_1)&&CollectionUtil.containsAny(seq.list("72","73"),ADDITIONAL_CONDITION_3).

[0122] In addition, the rule information for smoking and drinking can be: [StringUtil.equals(Drinking, "true")&&StringUtil.equals(Smoking, "true")].

[0123] Correspondingly, 8 types of setting contents are planned corresponding to the diagnostic words and indicators, and the diagnostic words and indicators can be set for the corresponding 8 types of contents on the corresponding pages in the medical consultation terminal.

[0124] For numerical types, you can use numbers to represent the results, such as systolic blood pressure, diastolic blood pressure, waist-to-hip ratio, body fat percentage, etc. The judgment conditions are: [greater than, less than, equal to, greater than or equal to, less than or equal to, interval (left open and right closed), interval (left closed and right open), interval (fully closed), interval (fully open)] {set value}. For a diagram of setting the content of a specific numerical type, see Figure 4 .

[0125] For the diagnosis word type, the medical professional phrases are used to describe the results. For example, sebaceous cyst, etc. The judgment condition is: [=] {set diagnosis word}. For the specific diagnosis word type content setting diagram, see Figure 5 .

[0126] For positive and negative types, use positive or negative to describe the results. For example, HIV positive, urine pregnancy test positive, etc. The judgment condition is: [=] {set negative or positive}. For specific positive and negative type content settings, see Figure 6 .

[0127] For Roman numeral type, use Roman numerals to describe the severity of the disease. For example, heart failure stage III, tumor stage I, etc. The judgment condition is: [=] {set Roman numerals}. For a specific diagram of the content setting of Roman numeral type, see Figure 7.

[0128] For positive types, the results are described using the degree of positivity, for example, urine protein 1+, urine sugar 3+, etc. The judgment condition is: [=] {set degree of positivity}.

[0129] For the diagnostic word + single value type, it is similar to the value type, except that the value here describes the diagnostic word. For example, the internal carotid artery lumen is moderately stenotic by 30%. The judgment conditions are: [greater than, less than, equal to, greater than or equal to, less than or equal to, interval (left open and right closed), interval (left closed and right open), interval (fully closed), interval (fully open)] {set value}. For a specific diagram of the content setting of the diagnostic word + single value type, see Figure 8 .

[0130] For the diagnosis word + double numerical type, it is similar to the diagnosis word + single numerical type, except that there are two restricted numerical values ​​here. For example, atrial septal defect, length 2mm, width 1mm. The judgment conditions are: [length and width, length or width] [greater than, less than, equal to, greater than or equal to, less than or equal to, interval (left open and right closed), interval (left closed and right open), interval (fully closed), interval (fully open)] {set numerical value}. For a specific diagram of the content setting of the diagnosis word + double numerical type, see Fig. 9 .

[0131] For positive-negative + numerical type, for example, hepatitis B surface antigen (HBsAg): positive means the patient is infected with hepatitis B virus, negative means not infected, and the numerical value is the concentration of the test result. The judgment conditions are: [negative, positive] [greater than, less than, equal to, greater than or equal to, less than or equal to, interval (left open and right closed), interval (left closed and right open), interval (fully closed), interval (fully open)] {set numerical value}. For a specific diagram of the content setting of the diagnostic word + double numerical type, see Fig.10 .

[0132] Step 202, performing indicator format conversion and diagnostic word format conversion on the medical consultation indicators and diagnostic words in the medical consultation data to obtain indicator conversion data and diagnostic word conversion data.

[0133] In some embodiments, the above-mentioned execution subject can perform indicator format conversion and diagnostic word format conversion on the medical indicators and diagnostic words in the above-mentioned medical data, respectively, to obtain indicator conversion data and diagnostic word conversion data. Among them, the indicator format conversion can be converted into an indicator format corresponding to the current execution subject, and the indicator format is the same as the indicator corresponding format in the diagnosis rule information. The diagnostic word format conversion can be converted into a diagnostic word format corresponding to the current execution subject, and the diagnostic word format is the same as the diagnostic word corresponding format in the diagnosis rule information.

[0134] As an example, the execution subject may convert the medical treatment indicators and diagnostic words in the medical treatment data into key-value pair set formats to generate indicator conversion data in key-value pair set format and diagnostic word conversion data in key-value pair set format. The key-value pair set format may be a Map data format.

[0135] Step 203: Utilize the rule matching engine to filter out target diagnosis rule information that meets the target hit condition from the above diagnosis rule information set.

[0136] In some embodiments, the execution subject may utilize a rule matching engine to filter out target diagnosis rule information that satisfies the target hit condition from the diagnosis rule information set. The target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, a rule hit relationship exists between the indicator conversion data and the target indicator rule information, and a rule hit relationship exists between the diagnosis word conversion data and the target diagnosis word rule information. For example, the rule matching engine may be an AviatorEvaluator engine. The target hit condition may be that the indicator conversion data and the diagnosis word conversion data satisfy at least one sub-condition in the diagnosis rule information. That is, the diagnosis rule information includes: at least one condition. Each condition corresponds to the indicator rule information or the diagnosis word rule information. The existence of a rule hit relationship between the indicator conversion data and the target indicator rule information indicates that the corresponding data content of the indicator conversion data is substituted into the corresponding condition of the target indicator rule information. The existence of a rule hit relationship between the diagnosis word conversion data and the target diagnosis word rule information indicates that the corresponding data content of the diagnosis word conversion data is substituted into the corresponding condition of the target diagnosis word rule information.

[0137] As an example, first, the above-mentioned execution entity can use the rule matching engine. For each diagnostic rule information, the first step is to determine the various conditions corresponding to the diagnostic rule information. The various conditions include: at least one first condition corresponding to the target indicator rule information and at least one second condition corresponding to the target diagnostic word rule information. The second step is to determine the condition weight value corresponding to each condition. The third step is to substitute the indicator conversion data into at least one first condition to obtain at least one first target condition that the characterization data content meets. The fourth step is to substitute the diagnostic word conversion data into at least one second condition to obtain at least one second target condition that the characterization data content meets. The fifth step is to add at least one weight value corresponding to at least one first target condition and at least one weight value corresponding to at least one second target condition to obtain a weighted sum value. Finally, the diagnostic rule information with the largest weighted sum value is selected from the diagnostic rule information set as the target diagnostic rule information.

[0138] In some optional implementations of some embodiments, the above-mentioned indicator conversion data includes: at least one indicator sub-data in the form of a key-value pair, the above-mentioned diagnostic word conversion data includes: at least one diagnostic sub-data in the form of a key-value pair, and the diagnostic rule information is a diagnostic rule expression. The indicator conversion data can be data in the form of a key-value pair set. For example, the indicator conversion data is {"key: indicator 1, value: indicator content 1", "key: indicator 2, value: indicator content 2", "key: indicator 3, value: indicator content 3"}. Similarly, the diagnostic word conversion data can be data in the form of a key-value pair set. For example, the diagnostic word conversion data is {"key: diagnostic word 1, value: diagnostic word content 1", "key: diagnostic word 2, value: diagnostic word content 2", "key: diagnostic word 3, value: diagnostic word content 3"}. The diagnostic rule expression can be an expression that characterizes the diagnostic rule corresponding to each condition. The structure of the corresponding expression of the diagnostic rule expression can be pre-set. The diagnostic rule expression includes: a sub-condition (i.e., a sub-expression) corresponding to at least one diagnostic indicator and a sub-condition (i.e., a sub-expression) corresponding to at least one diagnostic word. For example, the diagnosis rule expression may be {"code corresponding to indicator 1 > required value of indicator 1" & "code corresponding to indicator 2 < required value of indicator 2" & "code corresponding to diagnosis word 3 = required value of diagnosis word"}.

[0139] Optionally, the execution subject may filter out target diagnosis rule information that meets the target hit condition from the diagnosis rule information set, including the following steps:

[0140] In the first step, for each diagnostic rule information in the above diagnostic rule information set, perform the following generation steps:

[0141] Sub-step 1: for each indicator sub-data, substitute the indicator sub-data into the corresponding indicator condition in the diagnosis rule expression to obtain the first data comparison result corresponding to the indicator sub-data. The first data comparison result can be a comparison result of whether the data content corresponding to the indicator sub-data meets the indicator condition.

[0142] Sub-step 2: for each diagnosis sub-data, substitute the diagnosis sub-data into the corresponding diagnosis condition in the diagnosis rule expression to obtain the second data comparison result corresponding to the diagnosis sub-data. The second data comparison result can be a comparison result of whether the data content corresponding to the diagnosis sub-data meets the diagnosis condition.

[0143] Sub-step 3: generating a comprehensive data comparison result based on the obtained at least one first data comparison result and at least one second data comparison result.

[0144] As an example, the execution entity may combine at least one first data comparison result and at least one second data comparison result to generate a comprehensive data comparison result.

[0145] In the second step, the diagnostic rule information whose corresponding data comprehensive comparison result is the target result is selected from the above diagnostic rule information set as the target diagnostic rule information. The target result can be a result value that satisfies a preset condition set for at least one first data comparison result and at least one second data comparison result. In practice, the preset condition can be any pre-set condition that meets the application of the scenario. For example, the preset condition can be a target result with the highest proportion of the first result and the highest proportion of the second result.

[0146] As an example, first, determine the first result ratio corresponding to the first data comparison result that characterizes that the data content meets the indicator condition in at least one first data comparison result. Then, determine the second result ratio corresponding to the second data comparison result that characterizes that the data content meets the diagnosis condition in at least one second data comparison result. Finally, filter out the diagnostic rule information with the highest first result ratio and the highest second result ratio from the diagnostic rule information set as the target diagnostic rule information.

[0147] Optionally, the execution subject may generate a comprehensive data comparison result according to the obtained at least one first data comparison result and at least one second data comparison result, comprising the following steps:

[0148] The first step is to determine the number of first results corresponding to the first data comparison results that characterize that the data content meets the index condition in the at least one first data comparison result. The index condition may be a corresponding index requirement. The first data comparison result whose data content meets the index condition may be a first data comparison result whose corresponding data content meets the corresponding index requirement.

[0149] The second step is to determine the number of second results corresponding to the second data comparison results that characterize that the data content meets the diagnostic condition in the at least one second data comparison result. The diagnostic condition may be a corresponding diagnostic requirement. The second data comparison result whose data content meets the diagnostic condition may be a second data comparison result whose corresponding data content meets the corresponding diagnostic requirement.

[0150] The third step is to generate the above data comprehensive comparison result based on the above first result number and the second result number.

[0151] As an example, the execution entity may add the first result number and the second result number to generate the comprehensive data comparison result.

[0152] Optionally, the above-mentioned medical data also includes: an object parameter data set and a historical diagnosis data set. Among them, the object parameter data set can be the parameter data content corresponding to each object parameter. In practice, each object parameter can include: the age of the object and the gender of the object. The historical diagnosis data set is a data set for diagnosis within a historical time period. In practice, the historical diagnosis data set can include: a historical disease data set. For example, the historical disease data set can include: family history data, current medical history data, past medical history data, surgical history data, smoking history data, and drinking history data.

[0153] Optionally, the execution subject may filter out the target diagnosis rule information from the diagnosis rule information set according to the first result number and the second result number, comprising the following steps:

[0154] The first step is to generate a third data comparison result set corresponding to the object parameter data set. The specific implementation method can refer to the generation of the first data comparison result.

[0155] The second step is to generate a fourth data comparison result set corresponding to the historical diagnosis data set. The specific implementation method can refer to the generation of the first data comparison result.

[0156] The third step is to determine the number of third results corresponding to the third data comparison results in the third data comparison result set whose data content meets the object parameter conditions. The specific implementation method can refer to the generation of the first result number.

[0157] The fourth step is to determine the number of fourth results corresponding to the fourth data comparison results in the fourth data comparison result set whose data content meets the historical diagnosis condition. The specific implementation method can refer to the generation of the first result number.

[0158] In the fifth step, for each piece of diagnostic rule information, weighted sum is performed on the corresponding first result number, the corresponding second result number, the third result number and the fourth result number to generate a weighted number as the comprehensive comparison result of the above data.

[0159] Optionally, the execution subject may select the diagnosis rule information whose corresponding data comprehensive comparison result is the target result from the diagnosis rule information set as the target diagnosis rule information, including the following steps:

[0160] In the first step, in response to the diagnosis rule information set having the largest corresponding data comprehensive comparison result being the target number, the diagnosis rule information having the largest data comprehensive comparison result is determined as the target diagnosis rule information. The target number may be 1.

[0161] In the second step, in response to the diagnosis rule information in the diagnosis rule information set having the largest corresponding data comprehensive comparison result being greater than the target number, at least one diagnosis rule information having the largest data comprehensive comparison result is determined.

[0162] The third step is to select the diagnostic rule information with the greatest importance from the at least one diagnostic rule information as the target diagnostic rule information. The importance of the rule can represent the severity of the disease diagnosed by the corresponding diagnostic rule, that is, the more serious the disease is, the higher the importance of the corresponding rule is.

[0163] Step 204, determining the follow-up consultation information corresponding to the above target diagnosis rule information.

[0164] In some embodiments, the execution subject may determine the follow-up information corresponding to the target diagnosis rule information. The follow-up information may be diagnosis information related to the follow-up diagnosis. In practice, the follow-up information may include: the follow-up time and the follow-up department.

[0165] As an example, the execution subject may query the follow-up visit related information in the target diagnosis rule information to obtain the follow-up visit information.

[0166] Step 205: Send the above-mentioned follow-up consultation information to the user terminal corresponding to the patient.

[0167] In some embodiments, the execution subject may send the follow-up consultation information to a user terminal corresponding to the patient. The medical data and the patient have a medical relationship. That is, the patient is a patient who has undergone a preliminary diagnosis and is waiting for a follow-up consultation.

[0168] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the method for sending the follow-up information of some embodiments of the present disclosure, by using the diagnosis rule information set, the follow-up information corresponding to the medical data can be determined quickly and efficiently, so as to be pushed to the patient for follow-up treatment. Specifically, the reason why the relevant follow-up information is not fast and efficient is that as the number of users corresponding to various indicators increases, the corresponding process of determining the target rule information becomes longer, resulting in low efficiency in determining the follow-up information and a waste of a lot of time. Based on this, the method for sending the follow-up information of some embodiments of the present disclosure first obtains the medical data sent by the medical terminal and the pre-set diagnosis rule information set. Here, the set diagnosis rule information set is obtained to facilitate the determination of the diagnosis rule information matching the medical data. And the diagnosis rule information set is set correspondingly for each medical indicator and diagnosis word and the corresponding follow-up information, which is convenient for the query of subsequent follow-up information. Then, the medical indicators and diagnosis words in the above-mentioned medical data are converted into indicator format and diagnosis word format respectively, and the indicator conversion data and diagnosis word conversion data are obtained. Here, through the conversion of the indicator format and the diagnosis word format, the indicator format and the diagnosis word format of the consultation terminal are unified with the indicator format and the diagnosis word format involved in the diagnosis rule information set, so as to facilitate the determination of the subsequent target diagnosis rule information. In addition, through the conversion of the indicator format and the diagnosis word format, it is convenient to use the rule matching engine to filter the target diagnosis rule information. Then, using the rule matching engine, the target diagnosis rule information that meets the target hit condition can be quickly and efficiently filtered out from the above diagnosis rule information set. Among them, the above target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, there is a rule hit relationship between the above indicator conversion data and the above target indicator rule information, and there is a rule hit relationship between the above diagnosis word conversion data and the above target diagnosis word rule information. Here, based on the indicator conversion data and the diagnosis word conversion data after format conversion, the rule matching engine corresponding to the matching format can be used to realize the screening of the target diagnosis rule information in a lightweight and efficient manner. Then, the follow-up information corresponding to the above target diagnosis rule information is determined. Finally, the above follow-up information is sent to the user terminal corresponding to the patient, so that the patient can obtain the follow-up information corresponding to the consultation data in real time and efficiently. Among them, the above-mentioned medical consultation data has a medical consultation relationship with the above-mentioned medical consultation object. In summary, on the basis of pre-setting the diagnostic word rule information set, through the indicator format conversion and the diagnostic word format conversion and the use of the rule matching engine to realize the screening of the target diagnostic rule information, the corresponding follow-up consultation information can be generated in a lightweight and efficient manner, so that the medical consultation object can obtain accurate follow-up consultation information more quickly.

[0169] Further references Fig.11, shows a process 1100 of another embodiment of the method for sending follow-up information according to the present disclosure. The method for sending follow-up information includes the following steps:

[0170] Step 1101, obtaining the medical consultation data sent by the medical consultation terminal and the preset diagnosis rule information set.

[0171] Step 1102, obtaining the medical consultation mapping relationship corresponding to the medical consultation terminal.

[0172] In some embodiments, an execution entity (e.g. Figure 1 The electronic device 101 shown can obtain the medical consultation mapping relationship corresponding to the above-mentioned medical consultation terminal. The medical consultation mapping relationship can represent the mapping conversion relationship between the indicators and the diagnosis words between the execution subject corresponding to the operation end and the medical consultation terminal. That is, the medical consultation mapping relationship includes: the mapping relationship corresponding to the diagnostic indicator and the mapping relationship of the diagnosis word.

[0173] Step 1103 , according to the above-mentioned medical consultation mapping relationship, indicator mapping processing and diagnostic word mapping processing are performed on the above-mentioned medical consultation indicators and the above-mentioned diagnostic words respectively to generate mapping indicators and mapping diagnostic words.

[0174] In some embodiments, the execution subject may perform indicator mapping processing and diagnostic word mapping processing on the medical visit indicators and the diagnostic words respectively according to the medical visit mapping relationship to generate mapping indicators and mapping diagnostic words.

[0175] Step 1104 , converting the mapping indicators and the mapping diagnostic words into a key-value pair set to generate indicator conversion data and diagnostic word conversion data.

[0176] In some embodiments, the execution subject may convert the mapping index and the mapping diagnosis word into a key-value pair set to generate index conversion data and diagnosis word conversion data, wherein the key-value pair set may be a Map set.

[0177] Step 1105: Utilize the rule matching engine to filter out target diagnosis rule information that meets the target hit condition from the above diagnosis rule information set.

[0178] Step 1106, determining the follow-up information corresponding to the above target diagnosis rule information.

[0179] Step 1107, sending the above-mentioned follow-up consultation information to the user terminal corresponding to the patient.

[0180] In some embodiments, the specific implementation of steps 1101, 1105-1107 and the technical effects thereof can be referred to in Figure 2 The steps 201, 203-205 in the corresponding embodiment are not described in detail here.

[0181] from Fig.11 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Fig.11 In the corresponding process 1100 of the method for sending follow-up information in some embodiments, the medical consultation indicators and diagnostic words are mapped and processed through the medical consultation mapping relationship, and the key-value pair combination form is converted to unify the indicator format and diagnostic word format corresponding to the medical consultation terminal and the indicator format and diagnostic word format involved in the diagnostic rule information set, so as to facilitate the subsequent determination of the target diagnostic rule information. In addition, through the indicator format conversion and the diagnostic word format conversion, it is convenient to use the rule matching engine to screen the target diagnostic rule information.

[0182] Further references Fig.12 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a device for sending follow-up information. Figure 2 Corresponding to the method embodiments shown, the follow-up information sending device can be specifically applied to various electronic devices.

[0183] like Fig.12 As shown, a follow-up information sending device 1200 includes: an acquisition unit 1201, a conversion unit 1202, a screening unit 1203, a determination unit 1204 and a sending unit 1205. Among them, the acquisition unit 1201 is configured to acquire the medical data sent by the medical terminal and the pre-set diagnosis rule information set; the conversion unit 1202 is configured to perform indicator format conversion and diagnosis word format conversion on the medical indicators and diagnosis words in the above-mentioned medical data, respectively, to obtain indicator conversion data and diagnosis word conversion data; the screening unit 1203 is configured to use the rule matching engine to screen out target diagnosis rule information that meets the target hit condition from the above-mentioned diagnosis rule information set, wherein the above-mentioned target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, there is a rule hit relationship between the above-mentioned indicator conversion data and the above-mentioned target indicator rule information, and there is a rule hit relationship between the above-mentioned diagnosis word conversion data and the above-mentioned target diagnosis word rule information; the determination unit 1204 is configured to determine the follow-up information corresponding to the above-mentioned target diagnosis rule information; the sending unit 1205 is configured to send the above-mentioned follow-up information to the user terminal corresponding to the medical object, wherein the above-mentioned medical data has a medical relationship with the above-mentioned medical object.

[0184] In some optional implementations of some embodiments, the conversion unit 1202 can be further configured to: obtain the medical consultation mapping relationship corresponding to the above-mentioned medical consultation terminal; perform indicator mapping processing and diagnostic word mapping processing on the above-mentioned medical consultation indicators and the above-mentioned diagnostic words according to the above-mentioned medical consultation mapping relationship, so as to generate mapping indicators and mapping diagnostic words; convert the above-mentioned mapping indicators and the above-mentioned mapping diagnostic words into a key-value pair set form, so as to generate indicator conversion data and diagnostic word conversion data.

[0185] In some optional implementations of some embodiments, the above-mentioned indicator conversion data includes: at least one indicator sub-data in the form of a key-value pair, the above-mentioned diagnostic word conversion data includes: at least one diagnostic sub-data in the form of a key-value pair, and the diagnostic rule information is a diagnostic rule expression; and the screening unit 1203 can be further configured to: for each diagnostic rule information in the above-mentioned diagnostic rule information set, perform the following generation steps: for each indicator sub-data, substitute the above-mentioned indicator sub-data into the corresponding indicator condition in the corresponding diagnostic rule expression to obtain a first data comparison result; for each diagnostic sub-data, substitute the above-mentioned diagnostic sub-data into the corresponding diagnostic condition in the above-mentioned diagnostic rule expression to obtain a second data comparison result; generate a data comprehensive comparison result based on the at least one first data comparison result and the at least one second data comparison result obtained; and screen out the diagnostic rule information whose corresponding data comprehensive comparison result is the target result from the above-mentioned diagnostic rule information set as the target diagnostic rule information.

[0186] In some optional implementations of some embodiments, the screening unit 1203 can be further configured to: determine the number of first results corresponding to the first data comparison results in the above-mentioned at least one first data comparison result that characterizes that the data content meets the indicator condition; determine the number of second results corresponding to the second data comparison results in the above-mentioned at least one second data comparison result that characterizes that the data content meets the diagnostic condition; and generate the above-mentioned comprehensive data comparison result based on the above-mentioned first result number and the second result number.

[0187] In some optional implementations of some embodiments, the above-mentioned medical data also includes: an object parameter data set and a historical diagnosis data set; and the screening unit 1203 can be further configured to: generate a third data comparison result set corresponding to the above-mentioned object parameter data set; generate a fourth data comparison result set corresponding to the above-mentioned historical diagnosis data set; determine the number of third results corresponding to the third data comparison results in the above-mentioned third data comparison result set that represent that the data content meets the object parameter conditions; determine the number of fourth results corresponding to the fourth data comparison results in the above-mentioned fourth data comparison result set that represent that the data content meets the historical diagnosis conditions; for each diagnosis rule information, perform weighted summation on the corresponding first result number, the corresponding second result number, the above-mentioned third result number and the fourth result number to generate a weighted number as the comprehensive comparison result of the above-mentioned data.

[0188] In some optional implementations of some embodiments, the screening unit 1203 may be further configured to: in response to the diagnostic rule information set having the largest corresponding data comprehensive comparison result being the target number, determine the diagnostic rule information having the largest data comprehensive comparison result as the target diagnostic rule information. In response to the diagnostic rule information set having the largest corresponding data comprehensive comparison result being greater than the target number, determine at least one diagnostic rule information having the largest data comprehensive comparison result; and screen out the diagnostic rule information having the largest corresponding rule importance from the at least one diagnostic rule information as the target diagnostic rule information.

[0189] It is understandable that the units recorded in the follow-up information sending device 1200 are similar to those in the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the follow-up information sending device 1200 and the units included therein, and will not be described in detail here.

[0190] Reference below Fig.13 , which shows an electronic device (eg, Figure 1 Schematic diagram of the structure of the electronic device 101)1300. Fig.13 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0191] like Fig.13As shown, the electronic device 1300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1301, which can perform various appropriate actions and processes according to a program stored in a read-only memory 1302 or a program loaded from a storage device 1308 to a random access memory 1303. Various programs and data required for the operation of the electronic device 1300 are also stored in the random access memory 1303. The processing device 1301, the read-only memory 1302, and the random access memory 1303 are connected to each other via a bus 1304. An input / output interface 1305 is also connected to the bus 1304.

[0192] Typically, the following devices may be connected to the input / output interface 1305: an input device 1306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1309. The communication device 1309 may allow the electronic device 1300 to communicate with other devices wirelessly or by wire to exchange data. Although Fig.13 The electronic device 1300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Fig.13 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0193] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 1309, or installed from the storage device 1308, or installed from the read-only memory 1302. When the computer program is executed by the processing device 1301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0194] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0195] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0196] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the medical data sent by the medical terminal and the preset diagnostic rule information set; performs indicator format conversion and diagnostic word format conversion on the medical indicators and diagnostic words in the medical data, respectively, to obtain indicator conversion data and diagnostic word conversion data; uses a rule matching engine to filter out target diagnostic rule information that meets the target hit condition from the diagnostic rule information set, wherein the target diagnostic rule information includes: target indicator rule information and target diagnostic word rule information, there is a rule hit relationship between the indicator conversion data and the target indicator rule information, and there is a rule hit relationship between the diagnostic word conversion data and the target diagnostic word rule information; determines the follow-up information corresponding to the target diagnostic rule information; sends the follow-up information to the user terminal corresponding to the medical object, wherein the medical data has a medical relationship with the medical object.

[0197] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0198] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0199] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be set in a processor, for example, it may be described as: a processor includes an acquisition unit, a conversion unit, a screening unit, a determination unit and a sending unit. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the acquisition unit may also be described as "a unit for acquiring the medical data sent by the medical terminal and a pre-set diagnostic rule information set."

[0200] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0201] Some embodiments of the present disclosure further provide a computer program product, including a computer program, which implements any of the above-mentioned methods for sending follow-up information when executed by a processor.

[0202] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for sending follow-up consultation information, comprising: Obtaining the medical consultation data sent by the medical consultation terminal and the pre-set diagnosis rule information set; Performing indicator format conversion and diagnostic word format conversion on the medical treatment indicators and diagnostic words in the medical treatment data respectively, to obtain indicator conversion data and diagnostic word conversion data; Utilizing a rule matching engine, target diagnosis rule information satisfying a target hit condition is screened out from the diagnosis rule information set, wherein the target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, a rule hit relationship exists between the indicator conversion data and the target indicator rule information, and a rule hit relationship exists between the diagnosis word conversion data and the target diagnosis word rule information; Determine the follow-up consultation information corresponding to the target diagnosis rule information; The follow-up consultation information is sent to a user terminal corresponding to the patient, wherein the consultation data has a consultation relationship with the patient.

2. The method according to claim 1, wherein: The step of converting the medical treatment indicators and diagnostic terms in the medical treatment data into indicator format and diagnostic term format respectively to obtain indicator conversion data and diagnostic term conversion data includes: Obtaining a medical consultation mapping relationship corresponding to the medical consultation terminal; According to the medical consultation mapping relationship, the medical consultation indicators and the diagnostic terms are respectively subjected to indicator mapping processing and diagnostic term mapping processing to generate mapping indicators and mapping diagnostic terms; The mapping indicators and the mapping diagnostic words are converted into a key-value pair set to generate indicator conversion data and diagnostic word conversion data.

3. The method according to claim 1, wherein: The indicator conversion data includes: at least one indicator sub-data in the form of a key-value pair, the diagnosis word conversion data includes: at least one diagnosis sub-data in the form of a key-value pair, and the diagnosis rule information is a diagnosis rule expression; and The step of screening out target diagnosis rule information satisfying a target hit condition from the diagnosis rule information set includes: For each piece of diagnosis rule information in the diagnosis rule information set, the following generation steps are performed: For each indicator sub-data, substituting the indicator sub-data into a corresponding indicator condition in a corresponding diagnosis rule expression to obtain a first data comparison result; For each diagnosis sub-data, substituting the diagnosis sub-data into the corresponding diagnosis condition in the diagnosis rule expression to obtain a second data comparison result; Generate a comprehensive data comparison result based on the obtained at least one first data comparison result and at least one second data comparison result; Diagnosis rule information whose corresponding data comprehensive comparison result is a target result is screened out from the diagnosis rule information set as target diagnosis rule information.

4. The method according to claim 3, wherein: The step of generating a comprehensive data comparison result according to the obtained at least one first data comparison result and at least one second data comparison result comprises: Determine the number of first results corresponding to the first data comparison result representing that the data content meets the indicator condition in the at least one first data comparison result; Determine the number of second results corresponding to the second data comparison result indicating that the data content meets the diagnosis condition in the at least one second data comparison result; The data comprehensive comparison result is generated according to the first result number and the second result number.

5. The method according to claim 4, wherein: The medical consultation data also includes: a subject parameter data set and a historical diagnosis data set; and Generating the comprehensive data comparison result according to the first result number and the second result number includes: generating a third data comparison result set corresponding to the object parameter data set; Generating a fourth data comparison result set corresponding to the historical diagnostic data set; Determine the number of third results corresponding to the third data comparison results representing that the data content meets the object parameter condition in the third data comparison result set; Determine the number of fourth results corresponding to the fourth data comparison results representing that the data content meets the historical diagnosis condition in the fourth data comparison result set; For each piece of diagnosis rule information, a weighted sum is performed on the corresponding first result number, the corresponding second result number, the third result number and the fourth result number to generate a weighted number as the comprehensive data comparison result.

6. The method according to claim 3, wherein: The step of selecting the diagnostic rule information whose corresponding data comprehensive comparison result is the target result from the diagnostic rule information set as the target diagnostic rule information includes: In response to the diagnosis rule information set having the largest corresponding data comprehensive comparison result being the target number, determining the diagnosis rule information having the largest data comprehensive comparison result as the target diagnosis rule information; In response to the diagnosis rule information in the diagnosis rule information set having the largest corresponding data comprehensive comparison result being greater than the target number, determining at least one diagnosis rule information having the largest data comprehensive comparison result; The diagnosis rule information with the greatest importance of the corresponding rule is screened out from the at least one diagnosis rule information as the target diagnosis rule information.

7. The method according to claim 1, wherein: The diagnosis rule information in the diagnosis rule information set is rule information pre-maintained based on the medical consultation mapping relationship corresponding to the medical consultation terminal, and the diagnosis rule information includes at least one of the following: sub-rule information of numerical type, sub-rule information of diagnosis word type, sub-rule information of yin-yang type, sub-rule information of Roman numeral type, sub-rule information of positivity degree type, sub-rule information based on diagnosis word type and single numerical type, sub-rule information based on diagnosis word type and double numerical type, sub-rule information based on yin-yang type and numerical type, sub-rule information of object parameter type, and sub-rule information of disease history type.

8. A device for sending follow-up information, comprising: An acquisition unit, configured to acquire the medical consultation data sent by the medical consultation terminal and a preset diagnosis rule information set; a conversion unit configured to perform indicator format conversion and diagnostic word format conversion on the medical treatment indicators and diagnostic words in the medical treatment data, respectively, to obtain indicator conversion data and diagnostic word conversion data; A screening unit is configured to use a rule matching engine to screen out target diagnosis rule information that meets a target hit condition from the diagnosis rule information set, wherein the target diagnosis rule information includes: target indicator rule information and target diagnosis word rule information, a rule hit relationship exists between the indicator conversion data and the target indicator rule information, and a rule hit relationship exists between the diagnosis word conversion data and the target diagnosis word rule information; A determination unit, configured to determine the follow-up visit information corresponding to the target diagnosis rule information; The sending unit is configured to send the follow-up visit information to a user terminal corresponding to the patient, wherein the medical consultation data has a medical consultation relationship with the patient.

9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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