Children infectious disease early warning and prevention and control method based on artificial intelligence

Abstract

The invention relates to the technical field of infectious disease monitoring, in particular to an early warning and prevention and control method for children's infectious diseases based on artificial intelligence. The method comprises the following steps: acquiring pathogenic microorganism types and test data of sick children in a current time period; screening out a target microorganism type according to sick children corresponding to the pathogenic microorganism type in the current time period and sick children corresponding to the pathogenic microorganism type in the historical time period; taking a sick child corresponding to the target microorganism type as a target child, taking the test data of the high target child as target data, and according to the change condition of the target child and the occurrence condition of the target data, determining whether the target child is sick or not; and acquiring the epidemic degree of the children's infectious diseases at the current moment to judge whether early warning and prevention and control of the children's infectious diseases are carried out or not. According to the method, the prevalence degree of the children infectious diseases is accurately obtained in real time, so that early warning and prevention and control can be carried out more accurately and timely, and the situations of diagnosis delay and prevention and control response lag of the children infectious diseases are effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of infectious disease monitoring, in particular to an early warning and prevention and control method for children's infectious diseases based on artificial intelligence. BACKGROUND

[0002] Children's infectious diseases are common infectious diseases caused by pathogens such as bacteria, viruses, fungi or parasites, which have the characteristics of fast transmission and complex pathogenesis. Since the immune system of children has not yet matured, their infection risk is significantly higher than that of adults, and the disease progresses rapidly, and some diseases (such as severe influenza, hand-foot-mouth disease and Streptococcus pneumoniae infection, etc.) may lead to serious complications and even endanger life. Therefore, early warning and prevention and control are of great significance in pediatric clinical and public health management.

[0003] In the existing method, doctors early warn and prevent and control children's infectious diseases through the clinical symptoms of sick children, but in actual situations, the pathogen types of children's infectious diseases are various, the disease transmission routes are complex, and different sick children will have different clinical symptoms due to their own differences, which increases the complexity of early warning, and subjective judgment by humans can easily lead to diagnosis delay and prevention and control reaction lag of children's infectious diseases, which is not conducive to the control of children's infectious diseases. SUMMARY

[0004] In order to solve the technical problems of diagnosis delay and prevention and control reaction lag of children's infectious diseases, the purpose of the present application is to provide an early warning and prevention and control method for children's infectious diseases based on artificial intelligence, and the technical solution adopted is as follows: The present application provides an early warning and prevention and control method for children's infectious diseases based on artificial intelligence, which comprises the following steps: Obtain the electronic medical record of each sick child in the current time period; wherein the electronic medical record contains pathogenic microorganism types and various test data; According to the difference between the number of sick children corresponding to each pathogenic microorganism type in the current time period and the number of sick children in the historical time period, the target microorganism type with prominent pathogenicity is screened out; The sick children corresponding to the target microorganism type in the current time period are all target children, and the test data of the target children are all target data. According to the change of the target children in the current time period and the occurrence of each target data in the target children, the prevalence of children's infectious diseases at the current time is obtained. Based on the prevalence, it is judged whether to early warn and prevent and control children's infectious diseases at the current time.

[0005] Further, the method for obtaining the target microorganism type is: According to the proportion of children infected by each pathogenic microorganism type in the current time period and the difference between the proportion of children infected by each pathogenic microorganism type in the current time period and the proportion of children infected by each pathogenic microorganism type in the historical time period, the prominence of each pathogenic microorganism type is obtained. The pathogenic microorganism type corresponding to the maximum prominence is taken as the target microorganism type.

[0006] Further, the method for obtaining the prominence comprises: For each pathogenic microorganism type, the ratio of the number of children infected by the pathogenic microorganism type in the current time period to the total number of children infected in the current time period is taken as a first reference value. The ratio of the number of children infected by the pathogenic microorganism type in the historical time period to the total number of children infected in the historical time period is taken as a second reference value. The difference between the first reference value and the second reference value is normalized to obtain an adjustment weight. The product of the adjustment weight and the first reference value is taken as the prominence of the pathogenic microorganism type.

[0007] Further, the method for early warning and prevention and control of children's infectious diseases based on artificial intelligence further comprises: When there are at least two pathogenic microorganism types corresponding to the maximum prominence, the number of children infected by each pathogenic microorganism type corresponding to the maximum prominence in the current time period is taken as a first number. The pathogenic microorganism type corresponding to the maximum first number is taken as the target microorganism type.

[0008] Further, the method for obtaining the prevalence degree comprises: According to the change of the target children in the current time period, the prevalence tendency degree of the children's infectious diseases at the current time is obtained. According to the proportion of the occurrence of each target data in the target children, the disease test similarity degree of the target microorganism type at the current time is obtained. The product of the prevalence tendency degree and the disease test similarity degree is normalized to obtain the prevalence degree of the children's infectious diseases at the current time.

[0009] Further, the method for obtaining the prevalence tendency degree comprises: The current time period is evenly divided into each local time period, and the number of target children in each local time period is taken as a reference number. According to the change of the reference number, the disease potential stage and the disease prevalence stage in the current time period are obtained. The maximum number of references in the potential stage of the disease is taken as the potential representative number, and each local time period in the epidemic stage of the disease is taken as an analysis time period; For any analysis time period, the ratio of the number of references in the analysis time period to the potential representative number is taken as the disease prevalence analysis value of the analysis time period; The difference between the number of references in the analysis time period and the previous adjacent local time period is taken as the first value of the analysis time period; The ratio of the first value to the number of references in the previous adjacent local time period of the analysis time period is taken as the reference weight of the analysis time period; The product of the disease prevalence analysis value and the reference weight is taken as the local disease prevalence reference value of the analysis time period; The average of the local disease prevalence reference values of all analysis time periods is normalized to obtain the prevalence tendency degree of the infectious disease of children at the current time.

[0010] Further, the method for obtaining the potential stage of the disease and the epidemic stage of the disease in the current time period is: The first value of each local time period is obtained, the local time period corresponding to the maximum first value is taken as the segmentation time period, and the time period corresponding to all local time periods before the segmentation time period is taken as the potential stage of the disease; The time period corresponding to all local time periods after the segmentation time period is taken as the epidemic stage of the disease.

[0011] Further, the early warning and prevention and control method of the infectious disease of children based on artificial intelligence further comprises: When the maximum first value corresponds to multiple local time periods, the local time period corresponding to the first maximum first value is taken as the segmentation time period.

[0012] Further, the method for obtaining the disease test similarity degree is: For any target data, the ratio of the number of target children containing the target data to the total number of target children is taken as the proportion degree of the target data; The sum of the proportion degrees of all target data is taken as the disease test similarity degree of the target microorganism type at the current time.

[0013] Further, the method for judging whether to perform early warning and prevention and control of the infectious disease of children at the current time based on the prevalence degree is: When the prevalence degree is greater than the preset prevalence degree threshold, early warning and prevention and control of the infectious disease of children at the current time are needed; When the prevalence degree is less than or equal to the preset prevalence degree threshold, early warning and prevention and control of the infectious disease of children at the current time are not needed.

[0014] The present application has the following beneficial effects: Firstly, the present application screens out the target microorganism type according to the difference between the number of sick children corresponding to each pathogenic microorganism type in the current time period and the number of sick children corresponding to each pathogenic microorganism type in the historical time period, accurately determines the pathogenic microorganism type that may cause the prevalence of infectious diseases in children in the current time period, and is beneficial to the subsequent accurate and efficient analysis of the prevalence of infectious diseases in children at the current time; further, the target children are all the sick children corresponding to the target microorganism type in the current time period, and the target data are all the test data of the target children with high values, and then the prevalence degree of infectious diseases in children at the current time is obtained according to the change of the target children in the current time period and the occurrence of each target data in the target children, accurately reflecting the severity of the prevalence of infectious diseases in children at the current time; further, the early warning and prevention and control of infectious diseases in children at the current time are accurately judged based on the prevalence degree, so that the early warning and prevention and control of infectious diseases in children are more accurate and timely, the situation of diagnosis delay and prevention and control reaction lag of infectious diseases in children is effectively reduced, and the further control of infectious diseases in children is beneficial. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0016] Figure 1 A schematic flow chart of a method for early warning and prevention and control of infectious diseases in children based on artificial intelligence provided by an embodiment of the present application; Figure 2 A flow chart of a method for obtaining a target microorganism type provided by an embodiment of the present application; Figure 3 A flow chart of a method for obtaining a prevalence degree provided by an embodiment of the present application; Figure 4 A structural diagram of a system for early warning and prevention and control of infectious diseases in children based on artificial intelligence provided by an embodiment of the present application; Figure 5 A schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined inventive purpose, the specific implementation, structure, features and effects of the early warning and prevention and control method for children's infectious diseases based on artificial intelligence according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the early warning and prevention and control method for children's infectious diseases based on artificial intelligence provided by the present application is specifically described below in combination with the drawings.

[0020] Embodiment 1: The present application proposes an early warning and prevention and control method for children's infectious diseases based on artificial intelligence, please refer to Figure 1 , which shows a schematic flow chart of an early warning and prevention and control method for children's infectious diseases based on artificial intelligence provided by one embodiment of the present application, which includes the following steps: Step S1: obtaining the electronic medical record of each sick child in the current time period; wherein the electronic medical record contains the pathogenic microorganism type and various test data.

[0021] It is known that children's infectious diseases (such as influenza, pneumonia and hand-foot-mouth disease, etc.) usually show symptoms such as fever, vomiting, diarrhea, cough and shortness of breath, which will make children feel uncomfortable and affect their normal life and rest, and severe infection may also cause complications, and in severe cases, it may even endanger the life of children. Because infectious diseases can harm the immune system of children, especially for infants and children whose immune systems have not yet fully matured and children with weak immunity, they may appear weak and susceptible to infection for a long time after infection, so it is more necessary to early warn and prevent and control children's infectious diseases.

[0022] In order to analyze the prevalence of infectious diseases in children in real time and control the infectious diseases in children in time, the electronic medical records of each sick child in the current time period in the pediatric department are obtained from the database of the hospital, wherein the electronic medical records include pathogenic microorganism types and various test data. The duration of the current time period is set to 30 days in the embodiment, and the implementer can set the size of the current time period according to the actual situation, which is not limited herein. The end time of the current time period is the current time. It should be noted that the pathogenic microorganism types include viruses, bacteria and fungi, etc., and the test data include serum alanine aminotransferase, aspartate aminotransferase and white blood cells, etc., wherein the types of test data of each sick child are consistent.

[0023] Step S2: According to the difference between the number of sick children corresponding to each pathogenic microorganism type in the current time period and the number of sick children corresponding to each pathogenic microorganism type in the historical time period, the target microorganism type with prominent pathogenicity is screened out.

[0024] Specifically, the prevalent infectious diseases in children are usually caused by a certain pathogenic microorganism type, therefore, the number of sick children corresponding to each pathogenic microorganism type in the current time period is analyzed in the embodiment, and the more the number of sick children corresponding to a certain pathogenic microorganism type in the current time period, the more likely that the pathogenic microorganism type is the pathogen causing the prevalence of infectious diseases in children in the current time period.

[0025] Considering that in actual situation, a certain pathogenic microorganism type may itself present more number of sick children than other pathogenic microorganism types under normal circumstances, in order to avoid mistaking the pathogenic microorganism type which does not cause the prevalence of infectious diseases in children as the pathogen causing the prevalence of infectious diseases in children, the difference between the number of sick children corresponding to each pathogenic microorganism type in the current time period and the number of sick children corresponding to each pathogenic microorganism type in the historical time period is further combined in the embodiment to more accurately analyze the pathogenic microorganism type which may cause the prevalence of infectious diseases in children in the current time period.

[0026] Therefore, the target microorganism type with prominent pathogenicity is screened out according to the difference between the number of sick children corresponding to each pathogenic microorganism type in the current time period and the number of sick children corresponding to each pathogenic microorganism type in the historical time period in the embodiment, so as to accurately and efficiently analyze the prevalence of infectious diseases in children at the current time subsequently. It should be noted that the duration of the historical time period is set to 30 days in the embodiment, and the implementer can set the duration of the historical time period according to the actual situation, which is not limited herein. It should be noted that the end time of the historical time period is the initial time of the current time period, and at least two months of sick child medical records in the pediatric department have been recorded in the database of the hospital.

[0027] Preferably, in one implementable manner of the present embodiment, the acquisition method of the target microorganism type can refer to Figure 2 which shows a flowchart of an acquisition method of a target microorganism type provided by the present embodiment, the method comprising the following steps: Step S201: According to the proportion of sick children corresponding to each pathogenic microorganism type in the current time period and the difference from the proportion of sick children corresponding to each pathogenic microorganism type in the historical time period, the prominence of each pathogenic microorganism type is acquired.

[0028] When the proportion of sick children corresponding to a certain pathogenic microorganism type in the current time period is greater, it means that the pathogenic microorganism type is more likely to be the main pathogen of sick children in the current time period. Meanwhile, when the proportion of sick children corresponding to the pathogenic microorganism type in the current time period is greater than the proportion of sick children corresponding to the pathogenic microorganism type in the historical time period, it means that the pathogenic microorganism type is more likely to be the main pathogen of sick children in the current time period. Then, according to the proportion of sick children corresponding to each pathogenic microorganism type in the current time period and the difference from the proportion of sick children corresponding to each pathogenic microorganism type in the historical time period, the prominence of each pathogenic microorganism type is acquired. The greater the prominence, the more likely the corresponding pathogenic microorganism type is the main pathogen of sick children in the current time period.

[0029] In one implementable manner of the present embodiment, the acquisition method of the prominence is as follows: for any pathogenic microorganism type, the ratio of the number of sick children corresponding to the pathogenic microorganism type in the current time period to the number of all sick children in the current time period is acquired as a first reference value; the greater the first reference value, the more likely the pathogenic microorganism type is the key attention pathogen of sick children in the current time period; in order to more accurately analyze whether the pathogenic microorganism type is the key attention pathogen of sick children in the current time period, the ratio of the number of sick children corresponding to the pathogenic microorganism type in the historical time period to the number of all sick children in the historical time period is further acquired as a second reference value; when the first reference value is significantly greater than the second reference value, it is more accurate to show that the pathogenic microorganism type is more likely to be the key attention pathogen of sick children in the current time period, and then the result of normalizing the difference between the first reference value and the second reference value is taken as an adjustment weight; the greater the adjustment weight, the more the first reference value has reference significance, and therefore the product of the adjustment weight and the first reference value is taken as the prominence of the pathogenic microorganism type.

[0030] wherein the calculation formula of the prominence is: ; in the formula, is the prominence of the i-th pathogenic microorganism type; is the number of sick children corresponding to the i-th pathogenic microorganism type in the current time period; N is the number of all sick children in the current time period; is a first reference value; is the number of sick children corresponding to the i-th pathogenic microorganism type in the historical time period; is the number of all sick children in the historical time period; is a second reference value; is an adjustment weight.

[0031] At this point, the prominence of each pathogenic microorganism type is obtained.

[0032] Step S202: The pathogenic microorganism type corresponding to the maximum prominence is taken as the target microorganism type.

[0033] It is known that the greater the prominence, the more likely the corresponding pathogenic microorganism type is the main source of sick children in the current time period. Therefore, the pathogenic microorganism type corresponding to the maximum prominence is taken as the target microorganism type. It should be noted that when there are at least two pathogenic microorganism types corresponding to the maximum prominence, the number of sick children corresponding to each pathogenic microorganism type in the current time period is obtained, and each is taken as the first number; the pathogenic microorganism type corresponding to the maximum first number is taken as the target microorganism type.

[0034] Step S3: All sick children corresponding to the target microorganism type in the current time period are taken as target children, and all test data of the target children are taken as target data. According to the change of the target children in the current time period and the occurrence of each target data in the target children, the prevalence of the infectious disease of children at the current time is obtained.

[0035] Specifically, when the infectious disease of children appears to be prevalent, the number of sick children corresponding to the target microorganism type will increase dramatically in a short period of time in the pediatric clinic, especially before and after the time node when the infectious disease of children appears to be prevalent, the number of sick children corresponding to the target microorganism type will present two completely different states. In order to better describe, all sick children corresponding to the target microorganism type in the current time period are taken as target children, and then the prevalence of the infectious disease of children at the current time is analyzed according to the change of the target children in the current time period.

[0036] In view of the differences in the target children due to their own state, the symptoms of the disease of different target children will be different, but the changes of the test data of the target children should be consistent, because the causes of the target children are the same, which are caused by the target microorganism type. It is known that when a certain test data deviates from the corresponding normal range, the test data is abnormal. In order to further determine the accuracy of the analysis of the prevalence of infectious diseases of children at the current time, the test data of the target children that is higher than the normal range is regarded as the target data, that is, the test data of the target children that is higher than the normal range is regarded as the target data, and then the occurrence of each target data in the target children is analyzed. When the proportion of the target data in the target children is larger, it means that the prevalence of infectious diseases of children in the current time period is more accurate. Therefore, according to the change of the target children in the current time period and the occurrence of each target data in the target children, the prevalence of infectious diseases of children at the current time is obtained. The greater the prevalence, the more the early warning and prevention and control of infectious diseases of children are needed at the current time, so as to avoid the further spread of infectious diseases of children.

[0037] Preferably, in an implementable manner of the present embodiment, the method for obtaining the prevalence is as follows: Figure 3 The method for obtaining the prevalence provided by the present embodiment is shown in the flowchart, which comprises the following steps: Step S301: According to the change of the target children in the current time period, the prevalence tendency of infectious diseases of children at the current time is obtained.

[0038] The greater the prevalence tendency, the more serious the prevalence of infectious diseases of children at the current time, and the more the early warning is needed, so as to timely prevent and control the infectious diseases of children.

[0039] The method for obtaining the prevalence tendency is: first, the current time period is evenly divided into each local time period, then the number of target children in each local time period is obtained, and each is regarded as a reference number, so that the change of the target children in the current time period can be accurately analyzed subsequently. The implementer can set the size of the local time period according to the actual situation, which is not limited here. It is known that when the prevalence of infectious diseases of children occurs, the reference number will increase rapidly, therefore, according to the change of the reference number, the potential stage and the prevalence stage of the disease in the current time period are obtained, so that the prevalence of infectious diseases of children in the current time period can be accurately analyzed subsequently. The method for obtaining the potential stage of the disease and the epidemic stage of the disease is: obtaining the difference between the reference quantity of each local time period and the reference quantity of the previous adjacent local time period as the first value of each local time period; it should be noted that the first local time period does not have a previous adjacent local time period, therefore, the first local time period in the current time period is not analyzed; then, the local time period corresponding to the maximum first value is taken as the segmentation time period, and then the time period corresponding to all local time periods before the segmentation time period is taken as the potential stage of the disease; all local time periods corresponding to the time period after the segmentation time period are taken as the epidemic stage of the disease. It should be noted that when the maximum first value corresponds to multiple local time periods, the local time period corresponding to the first maximum first value is taken as the segmentation time period.

[0040] In order to analyze the epidemic situation of the infectious disease of children in the current time period, the maximum reference quantity in the potential stage of the disease is taken as the potential representative quantity, and all local time periods in the epidemic stage of the disease are taken as analysis time periods; for any analysis time period, the ratio of the reference quantity of the analysis time period to the potential representative quantity is taken as the disease epidemic analysis value of the analysis time period; the larger the disease epidemic analysis value, the more serious the epidemic of the infectious disease of children represented by the analysis time period; in order to more accurately represent the epidemic situation of the infectious disease of children corresponding to the analysis time period, the ratio of the first value of the analysis time period to the reference quantity of the previous adjacent local time period of the analysis time period is taken as the reference weight of the analysis time period; the larger the reference weight, the more serious the epidemic of the infectious disease of children represented by the analysis time period; then, the product of the disease epidemic analysis value and the reference weight is taken as the local disease epidemic reference value of the analysis time period; the larger the local disease epidemic reference value, the more serious the epidemic of the infectious disease of children represented by the analysis time period. In order to analyze the epidemic situation of the infectious disease of children in the current time period as a whole, that is, to determine the epidemic situation of the infectious disease of children at the current time, the mean value of the local disease epidemic reference values of all analysis time periods is normalized to obtain the epidemic tendency degree of the infectious disease of children at the current time.

[0041] The calculation formula of the epidemic tendency degree is: ; in the formula, W is the epidemic tendency degree of the infectious disease of children at the current time; J is the number of analysis time periods; is the reference quantity of the jth analysis time period; is the potential representative quantity; is the disease epidemic analysis value of the jth analysis time period; is the reference quantity of the (j-1)th analysis time period; is the first value of the jth analysis time period; is the reference weight of the jth analysis time period; is the local disease prevalence reference value for the jth analysis time period; norm is a normalization function. It should be noted that when j is 1, is the reference quantity of the segmentation time period.

[0042] Step S302: According to the occurrence proportion of each target data in the target children, the disease test similarity degree of the target microorganism type at the current moment is obtained.

[0043] When each target data appears in each target child, it means that the epidemic tendency degree of the child infectious disease at the current analysis moment is more accurate, and then the disease test similarity degree of the target microorganism type at the current moment is obtained according to the occurrence proportion of each target data in the target children. The greater the disease test similarity degree, the more accurate the epidemic tendency degree of the child infectious disease at the current moment.

[0044] In an implementable manner of the embodiment, the method for obtaining the disease test similarity degree is: for any target data, the ratio of the number of target children containing the target data to the number of all target children is taken as the proportion degree of the target data; and the sum of the proportion degrees of all target data is taken as the disease test similarity degree of the target microorganism type at the current moment.

[0045] Step S303: The result of normalizing the product of the epidemic tendency degree and the disease test similarity degree is taken as the prevalence degree of the child infectious disease at the current moment.

[0046] It is known that the greater the epidemic tendency degree, the more serious the prevalence of the child infectious disease at the current moment; the greater the disease test similarity degree, the more accurate the epidemic tendency degree; in order to accurately represent the prevalence of the child infectious disease at the current moment, the product of the epidemic tendency degree and the disease test similarity degree is normalized, and the result is taken as the prevalence degree of the child infectious disease at the current moment. The product of the epidemic tendency degree and the disease test similarity degree is normalized by the norm normalization function in the embodiment.

[0047] Step S4: Determine whether to perform early warning and prevention and control of the child infectious disease at the current moment based on the prevalence degree.

[0048] It is known that the greater the prevalence degree, the more the need to issue a warning of the prevalence of the child infectious disease at the current moment, and then the child infectious disease is timely prevented and controlled to avoid further spread of the child infectious disease. Therefore, the embodiment determines whether to perform early warning and prevention and control of the child infectious disease at the current moment based on the prevalence degree.

[0049] Preferably, in an implementable manner of the present embodiment, the method for determining whether early warning and prevention and control of the infectious disease of children at the current time is carried out based on the prevalence degree is: the present embodiment sets the preset prevalence degree threshold value as 0.42, and the implementer can set the size of the preset prevalence degree threshold value according to the actual situation, which is not limited here; when the prevalence degree is greater than the preset prevalence degree threshold value, early warning and prevention and control of the infectious disease of children at the current time are needed, that is, the real-time warning panel of the doctor terminal at the current time will issue early warning of the infectious disease of children, and at the same time submit a prevalence trend report to the CDC, reminding that the infectious disease of children needs to be prevented and controlled at the current time, providing prevention and control suggestions in advance for schools, parents and medical institutions, and preparing corresponding prevention and control resources (such as vaccines, medicines, medical facilities, etc.), so as to improve the ability of early identification of the infectious disease of children and effectively reduce the risk of transmission of the infectious disease of children; When the prevalence degree is less than or equal to the preset prevalence degree threshold value, early warning and prevention and control of the infectious disease of children at the current time are not needed.

[0050] In summary, the present embodiment obtains the pathogenic microorganism type and the test data of the sick children in the current time period; according to the corresponding sick children of the pathogenic microorganism type in the current time period and the corresponding sick children in the historical time period, a target microorganism type is screened out; the sick children corresponding to the target microorganism type are taken as target children, and the test data of the target children are taken as target data; according to the change of the target children and the occurrence of the target data, the prevalence degree of the infectious disease of children at the current time is obtained to determine whether early warning and prevention and control of the infectious disease of children are carried out. The present application accurately obtains the prevalence degree of the infectious disease of children in real time, so that early warning and prevention and control are more accurate and timely, and the situation of delay in diagnosis and lag in prevention and control reaction of the infectious disease of children is effectively reduced.

[0051] Embodiment 2: The present application also provides an early warning and prevention and control system for infectious diseases of children based on artificial intelligence, please refer to Figure 4 which shows a structure diagram of an early warning and prevention and control system for infectious diseases of children based on artificial intelligence provided by an embodiment of the present application, the system comprises a data acquisition module 10, a target microorganism type acquisition module 20, a prevalence degree acquisition module 30 and a data processing module 40.

[0052] The data acquisition module 10 is used for acquiring the electronic medical record of each sick child in the current time period; wherein the electronic medical record contains the pathogenic microorganism type and various test data.

[0053] The target microorganism type acquisition module 20 is configured to filter out a target microorganism type that is pathogenic prominent according to a difference between a number of sick children corresponding to each pathogenic microorganism type in a current time period and a number of sick children corresponding to each pathogenic microorganism type in a historical time period.

[0054] The prevalence acquisition module 30 is configured to take each target child corresponding to the target microorganism type in the current time period as a target child, take each test data corresponding to the target child as target data, and acquire a prevalence of the infectious disease of children at the current time according to a change of the target child in the current time period and an occurrence of each target data in the target child.

[0055] The data processing module 40 is configured to determine whether to perform early warning and prevention and control of the infectious disease of children at the current time based on the prevalence.

[0056] It should be noted that the system provided in the above embodiments is only used as an example for the division of the above functional modules, and in actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the child infectious disease early warning and prevention and control system based on artificial intelligence and the child infectious disease early warning and prevention and control method based on artificial intelligence provided in the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0057] Embodiment 3: The application further provides a child infectious disease early warning and prevention and control device based on artificial intelligence, which comprises a memory and a processor, wherein the memory stores executable program codes, and the processor is configured to call and execute the executable program codes to execute the child infectious disease early warning and prevention and control method based on artificial intelligence provided in the embodiments. The device can be a chip, an assembly or a module. The chip can comprise a processor and a memory connected to each other. When the processor calls and executes the instructions, the chip can execute the child infectious disease early warning and prevention and control method based on artificial intelligence provided in the above embodiments.

[0058] In addition, the embodiments of the present application also protect a computer device, please refer to Figure 5 The computer device comprises a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any of the child infectious disease early warning and prevention and control methods based on artificial intelligence described above.

[0059] Embodiment 4 The embodiment also provides a computer readable storage medium, which stores computer program codes, and when the computer program codes are run on a computer, the computer is caused to execute the above-mentioned related method steps to realize the method for early warning and prevention and control of infectious diseases of children based on artificial intelligence provided in the above-mentioned embodiment.

[0060] Embodiment 5 The embodiment also provides a computer program product, which, when run on a computer, causes the computer to execute the above-mentioned related steps to realize the method for early warning and prevention and control of infectious diseases of children based on artificial intelligence provided in the above-mentioned embodiment.

[0061] Among them, the device, computer readable storage medium, computer program product or chip provided in the embodiment are used to execute the corresponding method provided above, so the beneficial effects that can be achieved are for reference to the beneficial effects in the corresponding method provided above, which will not be described here.

[0062] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0063] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. An artificial intelligence-based early warning and prevention method for childhood infectious diseases, characterized in that: The method comprises the following steps: Obtain the electronic medical records of each sick child in the current time period; wherein the electronic medical records contain the type of pathogenic microorganisms and various laboratory test data; Based on the number of sick children corresponding to each pathogenic microorganism type in the current time period and the difference in the number of sick children corresponding to the historical time period, the target microorganism types with prominent pathogenicity were screened out; All sick children corresponding to the target microorganism type in the current time period are regarded as target children, and all the high test data of the target children are regarded as target data. Based on the changes in the target children in the current time period and the occurrence of each target data in the target children, the prevalence of childhood infectious diseases at the current moment is obtained; Determine whether to conduct early warning and prevention of childhood infectious diseases based on the prevalence level at the current moment.

2. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 1, characterized in that: The method for obtaining the target microorganism type is: Obtain the prominence of each pathogenic microorganism type based on the proportion of sick children corresponding to each pathogenic microorganism type in the current time period and the difference between the proportion of sick children corresponding to the historical time period; The pathogenic microorganism type corresponding to the greatest prominence is taken as the target microorganism type.

3. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 2, characterized in that: The method for obtaining the protrusion degree is: For any type of pathogenic microorganism, the ratio of the number of sick children corresponding to the type of pathogenic microorganism in the current time period to the number of all sick children in the current time period is obtained as a first reference value; Obtaining the ratio of the number of sick children corresponding to the type of pathogenic microorganism in the historical period to the total number of sick children in the historical period as the second reference value; Normalizing the difference between the first reference value and the second reference value as the adjustment weight; The product of the adjusted weight and the first reference value is used as the prominence of the type of pathogenic microorganism.

4. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 3, characterized in that: The artificial intelligence-based early warning and prevention and control method for childhood infectious diseases also includes: When there are at least two types of pathogenic microorganisms corresponding to the greatest prominence, the number of sick children corresponding to each type of pathogenic microorganism corresponding to the greatest prominence in the current time period is obtained, and both are taken as the first number; The pathogenic microorganism type corresponding to the largest first quantity is used as the target microorganism type.

5. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 1, characterized in that: The method for obtaining the popularity is: According to the changes in the target children in the current time period, the prevalence trend of childhood infectious diseases at the current moment is obtained; According to the occurrence ratio of each target data in the target children, the disease test similarity of the target microorganism type at the current moment is obtained; The result of normalizing the product of the epidemic tendency and the disease test similarity is taken as the prevalence of childhood infectious diseases at the current moment.

6. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 5, characterized in that: The method for obtaining the degree of popular tendency is: Evenly divide the current time period into various local time periods, and obtain the number of target children in each local time period as a reference number; According to the changes in the reference quantity, the potential stage and epidemic stage of the disease in the current time period are obtained; The largest reference number in the potential stage of the disease is used as the potential representative number, and the local time periods in the epidemic stage of the disease are all used as the analysis time periods; For any analysis period, the ratio of the reference number to the potential representative number in that analysis period is used as the disease prevalence analysis value for that analysis period; The difference between the reference quantity of the analysis time period and the previous adjacent local time period is used as the first value of the analysis time period; The ratio of the first value to the reference number of the previous adjacent local time period of the analysis time period is used as the reference weight of the analysis time period; The product of the disease epidemic analysis value and the reference weight is used as the local disease epidemic reference value for the analysis period; The result of normalizing the mean of the local disease epidemic reference values ​​of all analysis time periods is used as the epidemic tendency of childhood infectious diseases at the current moment.

7. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 6, characterized in that: The method for obtaining the potential stage and epidemic stage of a disease in the current time period is: Obtaining the first value of each local time period, taking the local time period corresponding to the largest first value as the split time period, and taking the time period corresponding to the split time period and all local time periods before it as the potential stage of the disease; The time period corresponding to all local time periods after the split time period is regarded as the disease epidemic stage.

8. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 7, characterized in that: The artificial intelligence-based early warning and prevention and control method for childhood infectious diseases also includes: When the largest first value corresponds to multiple local time periods, the local time period corresponding to the first-occurring largest first value is used as the divided time period.

9. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 5, characterized in that: The method for obtaining the similarity degree of the disease test is: For any type of target data, the ratio of the number of target children with that target data to the number of all target children is taken as the proportion of that type of target data; The sum of the proportions of all target data is used as the disease test similarity of the target microorganism type at the current moment.

10. The artificial intelligence-based early warning and prevention method for childhood infectious diseases according to claim 1, characterized in that: The method for determining whether to conduct early warning and prevention of childhood infectious diseases at the current moment based on the prevalence is: When the prevalence is greater than the preset prevalence threshold, early warning and prevention of childhood infectious diseases are required at this moment; When the prevalence is less than or equal to the preset prevalence threshold, early warning and prevention of childhood infectious diseases are not required at the current moment.

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