Pediatric clinical data acquisition and management system and method and storage medium

Abstract

The invention discloses a pediatric clinical data acquisition and management system and method and a storage medium, relates to the technical field of clinical data acquisition and management, and solves the technical problem that the epidemic disease type of the current stage cannot be analyzed and deduced according to stored clinical data in the prior art. Specifically, a clinical data acquisition decision-making unit acquires clinical symptoms of a real-time doctor-seeing child patient, and performs treatment decision-making through symptom analysis of the doctor-seeing child patient; the real-time disease prediction unit carries out comparison according to the disease type recorded in the clinical treatment in the current time period, acquires disease frequency information and disease probability information, and deduces whether the disease type of the real-time treatment child patient is an epidemic disease or not according to the information comparison; and the data storage and analysis unit performs data storage and analysis on the current clinical treatment record, obtains disease span data and disease type data, and deduces whether a clinical child patient corresponding to the storage data has a disease prevention and control high risk or not according to data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical data collection and management, and in particular to a pediatric clinical data collection and management system, method and storage medium. Background Art

[0002] Pediatric clinical data collection and management is a complex and critical process, which is extremely important for improving the quality of pediatric medical care, conducting medical research, and protecting children's health.

[0003] The patent with announcement number CN118412080A discloses a pediatric clinical data collection method, system and equipment; cluster analysis is performed based on comprehensive difference characteristic values ​​to form clusters with consistent characteristics; in each cluster, the positional relationship and comprehensive difference characteristic values ​​between clinical sample data are comprehensively considered to determine the performance characteristic values, further reflecting the uniqueness of the clinical sample data; finally, the abnormal characteristic values ​​are determined based on the differences in the performance characteristic values ​​to accurately reflect the individual differences of premature infants; the abnormal data screened out by this method is more accurate, and only the abnormal data is proofread, which significantly improves work efficiency.

[0004] However, with the existing technology, it is impossible to diagnose children and make targeted treatment decisions based on clinical data collection. It is also impossible to compare disease types, determine whether it is a current epidemic disease, and infer the type of epidemic disease at the current stage based on stored clinical data analysis, which reduces the efficiency of disease prevention and control.

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] The purpose of the present invention is to solve the above-mentioned problems and to provide a pediatric clinical data collection management system, method and storage medium.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A pediatric clinical data collection and management system, comprising a data collection center, wherein the data collection center is communicatively connected to a clinical data collection decision unit, a real-time disease prediction unit, and a data storage and analysis unit; The clinical data collection and decision-making unit obtains the clinical symptoms of the children in real time and makes treatment decisions based on the symptom analysis of the children. After completing the clinical data collection decision, the real-time disease prediction unit compares the disease types in the clinical visit records of the current period, collects disease frequency information and disease probability information, and infers whether the disease type of the real-time visit patient is an epidemic disease based on the information comparison; After completing the real-time disease prediction, the data storage and analysis unit performs data storage and analysis on the current clinical visit records, obtains disease span data and disease type data, and infers whether the clinical child corresponding to the stored data is at high risk of disease prevention and control based on the data analysis.

[0008] As a preferred embodiment of the present invention, the clinical data collection decision unit process is as follows: Obtain the clinical symptoms of the children who are visiting the hospital in real time, and obtain the duration of the clinical symptoms and the accompanying symptoms based on the information of the children; collect the characteristic indicators of the children based on the real-time clinical symptoms, and collect the numerical fluctuation of the characteristic indicators during the duration of the clinical symptoms, and divide the characteristic indicators into abnormal indicators and normal indicators based on the floating values; Based on the historical medical records and the floating trend of real-time abnormal indicators, the real-time disease type of the child is inferred and set as the preset disease type. Based on the historical clinical treatment records of the preset disease type, the characteristic indicators that cause the indicator fluctuations of the preset disease type and the characteristic indicator parameters that fluctuate before the disease occurs are obtained, and are marked as influencing indicators and predisposing indicators respectively.

[0009] As a preferred embodiment of the present invention, according to the numerical fluctuation inducing factors of the inducing index, the environment and execution events of the real-time patient before the visit are inquired and the pre-visit factors are statistically constructed; If the inducing factor of the value fluctuation is consistent with any of the factors before the consultation, and the consistency is accompanied by a continuous increase in duration, the corresponding inducing index will be determined, and the current preset disease type will be set as the confirmed disease type; If the factors causing the fluctuation in values ​​are not consistent with any of the factors before the visit, or if the consistency lasts for a short period of time, the corresponding inducing indicators will be re-screened and tested.

[0010] As a preferred embodiment of the present invention, after the disease type is confirmed, the influencing indicators of the confirmed basic type are analyzed, the influencing indicators are compared with the abnormal indicators of the real-time test, and the indicators with overlapping types are set as affected indicators. Conversely, the indicators with non-overlapping types are set as unaffected indicators; the real-time numerical floating speed of the affected indicators and the span amount of the real-time value exceeding the red line value at the current floating speed are obtained. If the real-time numerical floating speed of the affected indicators exceeds the speed threshold, and the span amount of the real-time value exceeding the red line value at the current floating speed exceeds the span threshold, an emergency warning is issued to the currently treated child; if the real-time numerical floating speed of the affected indicators does not exceed the speed threshold, or the span amount of the real-time value exceeding the red line value at the current floating speed does not exceed the span threshold, a preliminary warning is issued to the currently treated child.

[0011] As a preferred embodiment of the present invention, during emergency warning, the currently affected indicators and predisposing indicators are synchronously controlled, and the unaffected indicators are continuously monitored. If there is a fluctuation, it is immediately controlled; during preliminary warning, the currently affected indicators are used as monitoring objects, and the predisposing indicators are controlled. According to the control of the predisposing indicators, it is judged whether the fluctuation of the affected indicators is in a beneficial state, so as to facilitate decision-making and control of the symptoms; At the same time, the frequency of the accompanying symptoms is continuously monitored during the treatment period. That is, if the continuous frequency of the accompanying symptoms decreases when any indicator is controlled during the treatment period, the corresponding indicator will be marked as an associated accompanying indicator for synchronous control.

[0012] As a preferred embodiment of the present invention, the disease frequency information and disease probability information include the speed at which the interval between the frequency of occurrence of the currently determined disease type of children who visit the hospital in real time during the current period and the frequency of occurrence of the epidemic disease type set for the current period is shortened, and the numerical deviation value corresponding to the probability of repeated treatment of the determined disease type of children who visit the hospital in real time during the current period and the probability of repeated treatment of the epidemic disease type.

[0013] As a preferred embodiment of the present invention, if the disease frequency information exceeds the interval shortening speed threshold, or the disease probability information does not exceed the treatment repetition probability threshold, a treatment signal is generated; if the disease frequency information does not exceed the interval shortening speed threshold, and the disease probability information exceeds the treatment repetition probability threshold, a normal treatment signal is generated.

[0014] As a preferred embodiment of the present invention, the disease span data and the disease type data are respectively the corresponding span value ratio of the real-time increase span of the number of newly added children with the same type of disease in the stored data to the decrease span of the probability of the treated children visiting the doctor again, and the increase speed of the number of different disease types with different spans of the same abnormal indicator value in the stored data; If the disease span data exceeds the span value ratio threshold, or the disease type data exceeds the quantity increase rate threshold, a high-risk prevention and control signal is generated; if the disease span data does not exceed the span value ratio threshold, and the disease type data does not exceed the quantity increase rate threshold, a low-risk prevention and control signal is generated.

[0015] A pediatric clinical data collection and management method, the specific method steps are as follows: Clinical data collection: collect clinical data of children who visit the clinic in real time; Clinical data collection and decision-making: obtaining the clinical symptoms of the children in real time and making treatment decisions based on symptom analysis of the children; Real-time disease prediction: after completing the clinical data collection decision, compare the disease type in the clinical visit record of the current period, collect disease frequency information and disease probability information, and infer whether the disease type of the child visiting the hospital in real time is an epidemic disease based on the information comparison; Data storage and analysis, after completing real-time disease prediction, data storage and analysis of current clinical visit records are performed to obtain disease span data and disease type data. Based on data analysis, it is inferred whether the clinical children corresponding to the stored data are at high risk of disease prevention and control.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned pediatric clinical data collection and management method.

[0017] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, pediatric clinical data is collected, symptoms of children are diagnosed by collecting clinical data, and treatment decisions are made for children whose symptoms meet the diagnosis, thereby improving the efficiency of clinical data collection and the accuracy of diagnosis; Compare the disease types in the clinical visit records of the current period to infer whether the children currently visiting are suffering from epidemic diseases, so as to facilitate targeted control and carry out targeted management and monitoring of the children visiting, ensure the real-time treatment efficiency of the children visiting, and also provide early warning for the current epidemic diseases; Data storage and analysis of current clinical medical records are carried out to infer the impact of current epidemic diseases on children through stored data analysis, and to effectively infer whether the current diagnostic method can fundamentally cure the epidemic disease. Targeted adjustments to the cure method can be made based on the real-time cure situation to ensure effective control of epidemic diseases. Disease prevention and control and effective diagnosis of diseases can be effectively carried out based on clinical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 It is a principle block diagram of the system of the present invention; Figure 2 The schematic diagram of the method of the present invention is shown in FIG. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] See also Figure 1 As shown, a pediatric clinical data collection and management system includes a data collection center, wherein the data collection center is communicatively connected to a clinical data collection decision unit, a real-time disease prediction unit, and a data storage and analysis unit; The pediatric clinical data in this application include basic information: The patient's name, gender, age, date of birth, ethnicity, home address, contact information, etc. Age information is particularly important in pediatrics because children have very different susceptibility to diseases, clinical manifestations, and treatment responses at different stages of growth and development. For example, infectious diseases in the neonatal period may be different from those in infants and young children, and the calculation of drug dosage during treatment is also closely related to age.

[0023] Medical history information: This includes past medical history (such as whether the child has ever had other diseases or surgery), family medical history (whether there are children with hereditary diseases or similar diseases in the family), allergy history (allergies to food, drugs, environmental factors, etc.), and birth history (whether the child was born prematurely, by natural birth or cesarean section, birth weight, Apgar score, etc.). For example, knowing that there is a history of hereditary blood diseases in the family can be an important reminder for the diagnosis direction when the child has symptoms such as unexplained anemia; premature birth in the birth history may be related to the child's subsequent growth retardation or increased risk of certain diseases.

[0024] Symptoms and Signs: Record the various symptoms of the child in detail, such as the duration, severity, and accompanying symptoms of fever, cough, diarrhea, rash, etc. Physical signs include vital signs (temperature, respiration, heart rate, blood pressure), examination results of various body systems (such as reflexes of the nervous system, heart murmurs of the cardiovascular system, lung rales of the respiratory system, etc.). For example, for a child with fever and cough, observing the nature of the cough (dry cough or cough with sputum), respiratory rate, and whether there are signs of dyspnea, etc., can help determine whether it is a common respiratory infection or a more serious disease such as pneumonia.

[0025] The data collection center generates a clinical data collection decision signal and sends the clinical data collection decision signal to the clinical data collection decision unit. After receiving the clinical data collection decision signal, the clinical data collection decision unit collects pediatric clinical data, diagnoses the symptoms of the children by collecting clinical data, and makes treatment decisions for the symptoms of the children who meet the diagnosis, thereby improving the efficiency of clinical data collection and the accuracy of diagnosis; Obtain clinical symptoms of children who visit the hospital in real time, such as fever, cough, etc., and obtain the duration of clinical symptoms and accompanying symptoms based on the information of the children. Accompanying symptoms are symptoms that appear when clinical symptoms occur, such as cough and spitting; The characteristic indicators of the children are collected according to the real-time clinical symptoms, and the numerical fluctuation of the characteristic indicators is collected during the continuous stage of clinical symptoms. The characteristic indicators are divided into abnormal indicators and normal indicators according to the floating values. The characteristic indicators are expressed as the physical characteristic parameters of the children, such as body temperature, respiration, heart rate, and blood pressure; According to the historical medical records, combined with the real-time abnormal index floating trend, the real-time disease type of the child is inferred and set as the preset disease type. According to the historical clinical records of the preset disease type, the characteristic index that causes the index to float and the characteristic index parameters that float before the disease occurs are obtained, and they are marked as influencing indicators and predisposing indicators respectively; According to the numerical fluctuation inducing factors of the inducing indicators, the environment and execution events of the real-time patients before the visit are inquired and the pre-visit factors are statistically constructed, where the environment is represented by environmental parameters, such as temperature, humidity or oxygen content, etc.; the execution events are represented by the type of food eaten, etc.; if the numerical fluctuation inducing factors are consistent with any factor of the pre-visit factors, and the consistency is accompanied by a continuous increase in duration, the corresponding inducing indicators will be determined, and the current preset disease type will be set as the confirmed disease type; on the contrary, if the numerical fluctuation inducing factors are not consistent with any factor of the pre-visit factors, or the consistency is accompanied by a short duration, the corresponding inducing indicators will be re-screened and tested; After the disease type is confirmed, the influencing indicators of the confirmed basic type are analyzed, and the influencing indicators are compared with the abnormal indicators detected in real time. The indicators with overlapping types are set as affected indicators, and vice versa, the indicators with non-overlapping types are set as non-affected indicators. The real-time value floating speed of the affected indicator and the span amount of the real-time value exceeding the red line value at the current floating speed are obtained. If the real-time value floating speed of the affected indicator exceeds the speed threshold, and the span amount of the real-time value exceeding the red line value at the current floating speed exceeds the span threshold, an emergency warning is issued to the currently treated child, the currently affected indicator and the predisposing indicator are synchronously controlled, and the unaffected indicator is continuously monitored. If there is any floating, it is immediately controlled; If the floating speed of the real-time value of the affected indicator does not exceed the speed threshold, or the span of the real-time value exceeding the red line value at the current floating speed does not exceed the span threshold, a preliminary warning will be issued to the currently treated child, the currently affected indicator will be used as the monitoring object, and the predisposing indicator will be controlled. According to the control of the predisposing indicator, it will be judged whether the floating of the affected indicator is in a beneficial state, so as to facilitate decision-making and control of the disease; At the same time, the frequency of the accompanying symptoms is continuously monitored during the treatment period. That is, if the continuous frequency of the accompanying symptoms decreases when any indicator is controlled during the treatment period, the corresponding indicator will be marked as an associated accompanying indicator for synchronous control; After completing the clinical data collection decision, a real-time disease prediction signal is generated and sent to the real-time disease prediction unit. After receiving the real-time disease prediction signal, the real-time disease prediction unit compares the disease type in the clinical visit record of the current period to infer whether the current patient is suffering from an epidemic disease, so as to facilitate targeted control and conduct targeted management and monitoring of the patient, thereby ensuring the real-time treatment efficiency of the patient and being able to issue early warnings for current epidemic diseases. The speed of shortening the interval between the frequency of occurrence of the current determined disease type of the children who visit the clinic in real time in the current period and the frequency of occurrence of the epidemic disease type set in the current period is obtained, where the epidemic disease type is represented by the large number of occurrences of the disease type of the children who visit the clinic in the current period and the continuous increase. At the same time, the numerical deviation value corresponding to the probability of repeated treatment of the determined disease type of the children who visit the clinic in real time in the current period and the probability of repeated treatment of the epidemic disease type is obtained. The speed of shortening the interval between the frequency of occurrence of the current determined disease type of the children who visit the clinic in real time in the current period and the frequency of occurrence of the epidemic disease type set in the current period, and the numerical deviation value corresponding to the probability of repeated treatment of the determined disease type of the children who visit the clinic in real time in the current period and the probability of repeated treatment of the epidemic disease type are marked as disease frequency information and disease probability information, respectively, and compared with the interval shortening speed threshold and the treatment repetition probability threshold, respectively: If the interval value between the frequency of occurrence of the current determined disease type of the patient who visits the hospital in real time in the current period and the frequency of occurrence of the epidemic disease type set in the current period is shortened at a rate that exceeds the interval shortening rate threshold, or the corresponding numerical deviation value between the probability of repeated treatment of the determined disease type of the patient who visits the hospital in real time in the current period and the probability of repeated treatment of the epidemic disease type does not exceed the threshold of the probability of repeated treatment, then it is inferred that the determined disease type of the patient who visits the hospital in real time is an epidemic disease, and a targeted treatment signal is generated and sent to the data collection center. After receiving the targeted treatment signal, the data collection center modifies the treatment method for the patient and fits it to the treatment method for epidemic diseases, thereby reducing the probability of secondary occurrence; If the shortening speed of the interval between the frequency of occurrence of the current confirmed disease type of the patient in real time during the current period and the frequency of occurrence of the epidemic disease type set for the current period does not exceed the interval shortening speed threshold, and the corresponding numerical deviation value between the probability of repeated treatment of the confirmed disease type of the patient in real time during the current period and the probability of repeated treatment of the epidemic disease type exceeds the treatment repeated probability threshold, it is inferred that the confirmed disease type of the patient in real time is not an epidemic disease, and a normal treatment signal is generated and sent to the data collection center; after receiving the normal treatment signal, the data collection center will treat the patient according to the treatment plan that matches its own indicators; After the real-time disease prediction is completed, a data storage analysis signal is generated and sent to the data storage analysis unit. After receiving the data storage analysis signal, the data storage analysis unit performs data storage analysis on the current clinical visit record, infers the impact of the current epidemic disease on the child through the storage data analysis, and effectively infers whether the current diagnosis method can fundamentally cure the epidemic disease. According to the real-time cure situation, the cure method can be adjusted in a targeted manner to ensure the effective control of the epidemic disease. According to the clinical data, disease prevention and control and effective disease diagnosis can be effectively carried out; The collected information of clinically treated children is stored and backed up, and the progress of subsequent treatment is recorded. The corresponding span value ratio of the real-time increase span of the number of new children with the same disease type in the stored data and the decrease span of the probability of re-visiting the treated children is obtained, where the value ratio only collects the ratio of the values, and does not consider the impact of inconsistent value units. The increase rate of the number of non-identical disease types with different spans of the same abnormal indicator value in the stored data is obtained, and the corresponding span value ratio of the real-time increase span of the number of new children with the same disease type in the stored data and the decrease span of the probability of re-visiting the treated children, and the increase rate of the number of non-identical disease types with different spans of the same abnormal indicator value in the stored data are marked as disease span data and disease type data, respectively, and compared with the span value ratio threshold and the quantity increase rate threshold, respectively: If the span value ratio of the real-time increase in the number of newly added children with the same type of disease in the stored data and the corresponding span value ratio of the decrease in the probability of the treated children visiting the doctor again exceeds the span value ratio threshold, or the increase rate of the number of different disease types with different spans of the same abnormal indicator value in the stored data exceeds the number increase rate threshold, it is inferred that the clinical children corresponding to the stored data have a high risk of disease prevention and control, and a high-risk prevention and control signal is generated and sent to the data collection center. After receiving the high-risk prevention and control signal, the data collection center will issue a prevention and control warning for the current area, and screen for inducing factors based on the symptoms of the children visiting the doctor in the stored data and the real-time clinical symptoms of the children, and take timely measures to reduce the incidence rate; If the span value ratio of the real-time increase in the number of new children with the same type of disease in the stored data and the decrease in the probability of re-visiting the treated children does not exceed the span value ratio threshold, and the increase rate of the number of different disease types with different spans of the same abnormal indicator value in the stored data does not exceed the number increase rate threshold, it is inferred that the clinical children corresponding to the stored data do not have a high risk of disease prevention and control, and a low risk signal for prevention and control is generated and sent to the data collection center; See also Figure 2 As shown, a pediatric clinical data collection and management method, the specific method steps are as follows: Clinical data collection: collect clinical data of children who visit the clinic in real time; Clinical data collection and decision-making: obtaining the clinical symptoms of the children in real time and making treatment decisions based on symptom analysis of the children; Real-time disease prediction: after completing the clinical data collection decision, compare the disease type in the clinical visit record of the current period, collect disease frequency information and disease probability information, and infer whether the disease type of the child visiting the hospital in real time is an epidemic disease based on the information comparison; Data storage and analysis, after completing real-time disease prediction, data storage and analysis of current clinical visit records are performed to obtain disease span data and disease type data. Based on data analysis, it is inferred whether the clinical children corresponding to the stored data are at high risk of disease prevention and control.

[0026] Additionally, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned pediatric clinical data collection and management method.

[0027] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0028] Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory, the non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory, and the volatile memory may include random access memory (RAM) or external cache memory.

[0029] By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0030] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0031] When the present invention is in use, the clinical data collection and decision-making unit obtains the clinical symptoms of the real-time visiting children, and makes treatment decisions through symptom analysis of the visiting children; after completing the clinical data collection and decision-making, the real-time disease prediction unit compares the disease types in the clinical visit records of the current period, collects disease frequency information and disease probability information, and infers whether the determined disease type of the real-time visiting children is an epidemic disease based on the information comparison; after completing the real-time disease prediction, the data storage and analysis unit performs data storage analysis on the current clinical visit records, obtains disease span data and disease type data, and infers whether the clinical children corresponding to the stored data are at high risk of disease prevention and control based on the data analysis.

[0032] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A pediatric clinical data collection and management system, characterized in that: It includes a data collection center, wherein the data collection center is communicatively connected to a clinical data collection decision unit, a real-time disease prediction unit, and a data storage and analysis unit; The clinical data collection and decision-making unit obtains the clinical symptoms of the children in real time and makes treatment decisions based on the symptom analysis of the children. After completing the clinical data collection decision, the real-time disease prediction unit compares the disease types in the clinical visit records of the current period, collects disease frequency information and disease probability information, and infers whether the disease type of the real-time visit patient is an epidemic disease based on the information comparison; After completing the real-time disease prediction, the data storage and analysis unit performs data storage and analysis on the current clinical visit records, obtains disease span data and disease type data, and infers whether the clinical child corresponding to the stored data is at high risk of disease prevention and control based on the data analysis.

2. A pediatric clinical data collection and management system according to claim 1, characterized in that: The clinical data collection decision-making unit process is as follows: Obtain the clinical symptoms of the children who are visiting the hospital in real time, and obtain the duration of the clinical symptoms and the accompanying symptoms based on the information of the children; collect the characteristic indicators of the children based on the real-time clinical symptoms, and collect the numerical fluctuation of the characteristic indicators during the duration of the clinical symptoms, and divide the characteristic indicators into abnormal indicators and normal indicators based on the floating values; Based on the historical medical records and the floating trend of real-time abnormal indicators, the real-time disease type of the child is inferred and set as the preset disease type. Based on the historical clinical treatment records of the preset disease type, the characteristic indicators that cause the indicator fluctuations of the preset disease type and the characteristic indicator parameters that fluctuate before the disease occurs are obtained, and are marked as influencing indicators and predisposing indicators respectively.

3. A pediatric clinical data collection and management system according to claim 2, characterized in that: According to the numerical fluctuation of the predisposing factors of the predisposing index, the real-time children were asked about their environment and execution events before the visit, and the pre-visit factors were statistically constructed; If the inducing factor of the value fluctuation is consistent with any of the factors before the consultation, and the consistency is accompanied by a continuous increase in duration, the corresponding inducing index will be determined, and the current preset disease type will be set as the confirmed disease type; If the factors causing the fluctuation in values ​​are not consistent with any of the factors before the visit, or if the consistency lasts for a short period of time, the corresponding inducing indicators will be re-screened and tested.

4. A pediatric clinical data collection and management system according to claim 3, characterized in that: After the disease type is confirmed, the influencing indicators of the confirmed basic type are analyzed, and the influencing indicators are compared with the abnormal indicators of the real-time test. The indicators with overlapping types are set as affected indicators, and vice versa, the indicators with non-overlapping types are set as unaffected indicators; the real-time numerical floating speed of the affected indicators and the span amount of the real-time value exceeding the red line value at the current floating speed are obtained. If the real-time numerical floating speed of the affected indicators exceeds the speed threshold, and the span amount of the real-time value exceeding the red line value at the current floating speed exceeds the span threshold, an emergency warning is issued to the currently treated child; If the floating speed of the real-time value that has affected the indicator does not exceed the speed threshold, or the span of the real-time value exceeding the red line value at the current floating speed does not exceed the span threshold, a preliminary warning will be issued to the child currently receiving treatment.

5. A pediatric clinical data collection and management system according to claim 4, characterized in that: During emergency warning, the currently affected indicators and predisposing indicators are controlled synchronously, and the unaffected indicators are continuously monitored. If there is any fluctuation, it is immediately controlled. During preliminary warning, the currently affected indicators are monitored, and the predisposing indicators are controlled. According to the control of the predisposing indicators, it is judged whether the fluctuation of the affected indicators is in a beneficial state, so as to facilitate decision-making and control of the symptoms. At the same time, the frequency of the accompanying symptoms is continuously monitored during the treatment period. That is, if the continuous frequency of the accompanying symptoms decreases when any indicator is controlled during the treatment period, the corresponding indicator will be marked as an associated accompanying indicator for synchronous control.

6. A pediatric clinical data collection and management system according to claim 1, characterized in that: Disease frequency information and disease probability information include the speed at which the interval between the frequency of occurrence of the currently determined disease type in children who visit the hospital in real time during the current period and the frequency of occurrence of the epidemic disease type set for the current period is shortened, and the numerical deviation value corresponding to the probability of repeated treatment of the determined disease type in children who visit the hospital in real time during the current period and the probability of repeated treatment of the epidemic disease type.

7. A pediatric clinical data collection and management system according to claim 6, characterized in that: If the disease frequency information exceeds the interval shortening speed threshold, or the disease probability information does not exceed the treatment repetition probability threshold, a treatment signal is generated; if the disease frequency information does not exceed the interval shortening speed threshold, and the disease probability information exceeds the treatment repetition probability threshold, a normal treatment signal is generated.

8. A pediatric clinical data collection and management system according to claim 1, characterized in that: The disease span data and disease type data are respectively the corresponding span value ratio of the real-time increase span of the number of newly added children with the same disease type in the stored data to the decrease span of the probability of the treated children visiting the doctor again, and the increase speed of the number of different disease types with different spans of the same abnormal indicator value in the stored data; If the disease span data exceeds the span value ratio threshold, or the disease type data exceeds the quantity increase rate threshold, a high-risk prevention and control signal is generated; If the disease span data does not exceed the span value ratio threshold, and the disease type data does not exceed the quantity increase rate threshold, a low-risk prevention and control signal is generated.

9. A pediatric clinical data collection and management method, characterized in that: A pediatric clinical data collection and management system as described in any one of claims 1 to 8 above.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a pediatric clinical data collection and management method as described in claim 9 is implemented.

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