CN111798988B - Risk area prediction method and device, electronic equipment and computer readable medium - Google Patents
Risk area prediction method and device, electronic equipment and computer readable medium Download PDFInfo
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- CN111798988B CN111798988B CN202010646189.7A CN202010646189A CN111798988B CN 111798988 B CN111798988 B CN 111798988B CN 202010646189 A CN202010646189 A CN 202010646189A CN 111798988 B CN111798988 B CN 111798988B
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
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Abstract
The disclosure relates to a risk region prediction method, a risk region prediction device, an electronic device and a computer readable medium. The method comprises the following steps: acquiring medical data of a plurality of patients; determining a risk patient of the plurality of patients from the medical data; determining trajectory information of a patient at risk; determining a total risk value of the target area in each preset period within a preset time length according to the track information and the medical data of the risk patients; determining a risk index of the target area in a preset time length according to the total risk value of the target area in each preset period in the preset time length; and if the risk index of the target area in the preset time length is greater than the preset risk threshold corresponding to the preset time length, determining that the target area is a risk area. The risk area prediction method, the risk area prediction device, the electronic equipment and the computer readable medium can predict a potential high risk area before an infectious disease outbreak, so that prevention and control are timely carried out at the initial stage of an epidemic situation, and the large-area spread of the epidemic situation is avoided.
Description
Technical Field
The present disclosure relates to the field of public health technologies, and in particular, to a risk area prediction method, an apparatus, an electronic device, and a computer-readable medium.
Background
At present, most of the existing prevention and control technologies for infectious diseases determine the region where the infected patients gather as a possible propagation risk region in a remedial manner under the condition that the infection of the disease is determined, and give travel guidance to local residents. However, infectious diseases are extremely prevalent, leading to an epidemic. The above scheme cannot predict the epidemic situation timely and effectively, so as to prevent and control the epidemic situation in advance.
The above information disclosed in the background section is only for enhancement of understanding of the background of the present disclosure, and thus it may include information that does not constitute related art known to those of ordinary skill in the art.
Disclosure of Invention
In view of this, embodiments of the present disclosure provide a risk area prediction method, an apparatus, an electronic device, and a computer readable medium, which can predict a potential high risk area before an infection outbreak, so as to prevent and control an epidemic situation in an early stage in time, and avoid a large area spread of the epidemic situation.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a risk area prediction method is provided, which includes: acquiring medical data of a plurality of patients; determining a risk patient of the plurality of patients from the medical data; determining trajectory information for the at-risk patient; determining a total risk value of the target area in each preset period within a preset time length according to the track information of the risk patients and the medical data; determining a risk index of the target area in the preset time according to the total risk value of the target area in each preset period in the preset time; and if the risk index of the target area in the preset time length is larger than a preset risk threshold corresponding to the preset time length, determining that the target area is a risk area.
In an exemplary embodiment of the present disclosure, determining at-risk patients of the plurality of patients from the medical data comprises: and matching in the medical data according to at least one preset symptom to obtain the successfully matched risk patient and the successfully matched symptom set of the risk patient.
In an exemplary embodiment of the present disclosure, the track information includes track address information and track time information; wherein, according to the track information of the patient at risk and the medical data, determining the total risk value of the target area in each preset period within a preset time comprises the following steps: determining symptom time information of the at-risk patient from the medical data; determining a first weight of the at-risk patient according to the symptom set matched successfully by the at-risk patient; determining a second weight of the at-risk patient corresponding to the target region according to the trajectory time information and the symptom time information of the at-risk patient; determining a risk factor of the at-risk patient corresponding to the target area according to the first weight and the second weight; and determining the total risk value of the target area in each preset period in the preset duration according to the risk factors of the target area corresponding to the risk patients in each preset period in the preset duration.
In an exemplary embodiment of the disclosure, determining a total risk value of the target region for each preset period within a preset time length according to the trajectory information of the at-risk patient and the medical data includes: determining the number of the risk patients in the target area in each preset period within the preset duration according to the track information of the risk patients and the medical data; and determining the total risk value of the target area in each preset period within the preset time length according to the number of the risk patients.
In an exemplary embodiment of the present disclosure, determining a risk index of the target area within the preset duration according to the total risk value of the target area within each preset period within the preset duration includes: determining a risk sub-index corresponding to each preset period of the target area in the preset duration according to the mean value and the standard deviation corresponding to the target area and the total risk value of the target area in each preset period in the preset duration; determining the number of cycles of a preset cycle with the risk sub-index larger than a preset risk sub-index threshold according to the risk sub-index corresponding to each preset cycle in the preset duration; and determining the risk index of the target area in the preset time length according to the risk sub-index corresponding to each preset period in the preset time length and the number of the periods.
In an exemplary embodiment of the present disclosure, determining, according to the mean and the standard deviation corresponding to the target area and the total risk value of the target area in each preset period in the preset duration, a sub-risk index corresponding to each preset period in the preset duration of the target area includes: performing difference operation on the total risk value of the target area in each preset period in a preset time length and the mean value corresponding to the target area to obtain a difference result; and performing division operation on the difference result and the standard deviation corresponding to the target area, and determining a modulus as the risk sub-index corresponding to each preset period of the target area in the preset time length.
In an exemplary embodiment of the present disclosure, determining trajectory information of the at-risk patient includes: acquiring demographic information of the risk patients to determine track information of the risk patients according to work place information and residence place information in the demographic information; and/or acquiring target equipment bound with the identity of the risk patient, acquiring historical address information uploaded by the target equipment, and determining the track information of the risk patient according to the historical address information.
In an exemplary embodiment of the present disclosure, the method further comprises: determining the grade of the risk area according to the multiple that the risk index of the target area in the preset time length is larger than a preset risk threshold corresponding to the preset time length; and carrying out early warning display of different colors on the risk area on a map according to the grade of the risk area.
According to an aspect of the present disclosure, a risk region prediction apparatus is provided, the apparatus including: a medical data acquisition module configured to acquire medical data of a plurality of patients; a risk patient determination module configured to determine a risk patient of the plurality of patients from the medical data; a trajectory information determination module configured to determine trajectory information for the at-risk patient; the total risk value determining module is configured to determine a total risk value of the target area in each preset period within a preset time length according to the track information of the risk patients and the medical data; a risk index determination module configured to determine a risk index of the target area within the preset duration according to the total risk value of the target area within each preset period within the preset duration; a risk area determination module configured to determine that the target area is a risk area if the risk index of the target area within the preset duration is greater than a preset risk threshold corresponding to the preset duration.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as described above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as set forth above.
According to the risk region prediction method, the risk region prediction device, the electronic equipment and the computer readable medium, historical track information of risk patients is counted, a total risk value of each target region in each preset period can be determined, and the total risk value in each preset period can visually reflect risk change conditions. Furthermore, the total risk value of the target area in each preset period can be analyzed, the risk index of the target area in the preset duration is obtained through mining, the potential risk area is predicted based on the risk index, the target area with the risk index larger than the preset risk threshold is determined to be the risk area, and guidance can be provided for epidemic situation prevention and control work in the early stage of epidemic situation.
According to some exemplary embodiments of the disclosure, risk patients are predicted through a plurality of preset symptoms in medical data, so that the danger of epidemic situation is found before the infectious disease outbreak, effective prevention and control in the initial stage of the epidemic situation are facilitated, and the large-area spread of the epidemic situation is avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flow diagram illustrating a method of risk area prediction according to an exemplary embodiment;
FIG. 2 is a flow diagram illustrating a method of risk area prediction according to an exemplary embodiment;
FIG. 3 is a flow diagram illustrating a method of risk area prediction according to an exemplary embodiment;
FIG. 4 is a flow diagram illustrating a method of risk area prediction according to an exemplary embodiment;
FIG. 5 is a flow diagram illustrating a method of risk area prediction according to an exemplary embodiment;
FIG. 6 is a block diagram illustrating a risk area prediction device according to an exemplary embodiment;
FIG. 7 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device to implement embodiments of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
Fig. 1 is a flow chart illustrating a method of risk area prediction according to an exemplary embodiment. The risk area prediction method provided by the embodiments of the present disclosure may be executed by any electronic device with computing processing capability, such as a user terminal and/or a server, and in the following embodiments, a server execution method is taken as an example for illustration, but the present disclosure is not limited thereto. The risk area prediction method 10 provided by the embodiment of the present disclosure may include steps S102 to S108.
As shown in fig. 1, in step S102, medical data of a plurality of patients is acquired.
In embodiments of the present disclosure, the medical data may include, for example, but is not limited to: data or backup data in a database system of a medical institution, data uploaded by terminal equipment, and the like.
In step S104, a risk patient of the plurality of patients is determined from the medical data.
In the disclosed embodiments, the at-risk patient may be a patient with symptoms of an infectious disease. And matching can be carried out according to the preset symptoms and the medical data, and the patient successfully matched is determined as the risk patient.
In step S106, trajectory information of the at-risk patient is determined.
In embodiments of the present disclosure, trajectory information over a preset historical time period may be determined based on the identity of the at-risk patient. The trajectory information is used to identify the movement trajectory of the at risk patient over a period of time.
In an exemplary embodiment, in step S106, demographic information of the risk patient may be acquired to determine trajectory information of the risk patient according to the workplace information and the residence place information in the demographic information; and/or acquiring target equipment bound with the identity of the risk patient, acquiring historical address information uploaded by the target equipment, and determining the track information of the risk patient according to the historical address information. .
The demographic information is a scientific general term for researching population development, and the regularity and quantitative relationship between population and social, economic and ecological environments and other interrelations and the application thereof. Demographic information may include indicators of space, age, gender, culture, occupation, income, fertility rate, and the like. When acquiring the demographic information of the risk patient, the demographic information of the risk patient can be acquired according to the matching result by matching in the database comprising the demographic information according to the unique identifier of the risk patient. The historical track information can be obtained by extracting the information (such as address information and work address information) in the demographic information.
In step S108, a total risk value of the target area in each preset period within a preset duration is determined according to the trajectory information and the medical data of the at-risk patient.
In the embodiment of the present disclosure, the target area may also be a city, a rural area, or a region of a city. For example, the target area may be obtained by meshing a map. The grid size during grid division can be adjusted according to the actual situation and the specific application scenario requirements, and the technical scheme of the present disclosure is not particularly limited to this.
In the embodiment of the disclosure, a plurality of events of the risk patient can be determined according to the historical track information of the risk patient, and each event comprises an event occurrence time and an event occurrence place. Counting a plurality of events of the risk patients according to the event occurrence place and the event occurrence time to obtain the risk patients in each preset period of each target area; and determining the risk patients of each target area in each preset period as the risk patients of the target area in each preset period. For another example, the risk patients present in the target area during each preset period may also be calculated according to the geographical location where the risk patients stay.
After the risk patients of each target area in each preset period are obtained, the deduplication operation can be performed to improve the accuracy of the statistical result. For example, the patient identification of the at-risk patient in each preset period in each target area can be obtained, and the at-risk patients with the same patient identification can be rejected to achieve the purpose of deduplication.
In step S110, a risk index of the target area within the preset duration is determined according to the total risk value of the target area within the preset duration for each preset period.
In embodiments of the present disclosure, a mean and a standard deviation of the target region may be determined; and analyzing the risk patients existing in the target region in each preset period within a preset time length based on the mean value and the standard deviation, and determining the risk index of each region. The mean and standard deviation describe the mean and standard deviation of the risk index for each zone over a preset time period.
When the mean value and the standard deviation of the target area are determined, calculating a risk index according to the risk of the patient in the target area in each preset period, and calculating the mean value and the standard deviation of the target area according to the risk index; the preset mean and standard deviation of the target area can also be directly obtained. The preset mean and standard deviation may be determined, for example, based on manual experience, or may be obtained by performing statistical analysis on historical data of the target region.
In step S112, if the risk index of the target area within the preset duration is greater than the preset risk threshold corresponding to the preset duration, the target area is determined to be a risk area.
According to the risk area prediction method provided by the embodiment of the disclosure, historical track information of a risk patient is counted, a total risk value of each target area in each preset period can be determined, and the total risk value in each preset period visually reflects risk change conditions. Furthermore, the total risk value of the target area in each preset period can be analyzed, the risk index of the target area in a preset time length is obtained through mining, the potential risk area is predicted based on the risk index, the target area with the risk index larger than a preset risk threshold is determined as the risk area, and guidance can be provided for epidemic situation prevention and control work in the early stage of the epidemic situation.
In an exemplary embodiment, in step S102, when medical data of a plurality of patients is acquired, newly added case data within a preset time period in the medical data may be acquired; and/or receiving reported case data sent by the equipment end within a preset time length. In step S104, when determining the at-risk patient, the patient may be determined as the at-risk patient by performing symptom matching on the newly added case data and/or the reported case data according to at least one preset symptom.
The reported case data received by the device side may be, for example, reported case data uploaded by a doctor user operating the device side. The equipment side may be an equipment side of a medical facility. The reported case data received by the equipment terminal can also be reported case data uploaded by the operation of the equipment terminal by a patient user. The reported case data may include patient basic information and patient case information. The preset symptoms may, for example, include: fever, cough, expectoration, pharyngalgia, hypodynamia, dyspnea, diarrhea, vomiting, poor appetite, headache, muscular soreness, etc., but the technical scheme of the present disclosure is not particularly limited thereto.
In the embodiment, the risk patients are predicted by a plurality of preset symptoms in the medical data, so that the danger of the epidemic situation is found before the infectious disease outbreak, effective prevention and control are facilitated at the initial stage of the epidemic situation, and the large-area spread of the epidemic situation is avoided.
In an exemplary embodiment, in step S108, the number of the at-risk patients in the target area in each preset period in the preset time length may be determined according to the track information of the at-risk patients and the medical data; and determining the total risk value of the target area in each preset period within the preset time length according to the number of the risk patients. In the embodiment, the number of the patients with risk in each preset period in each target area is determined as the total risk value, so that the risk change condition of the target area in each preset period can be visually reflected.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 2 is a flow chart illustrating a method of risk area prediction according to an exemplary embodiment.
As shown in fig. 2, a risk area prediction method of an embodiment of the present disclosure may include the following steps.
In this embodiment, the track information includes track address information and track time information. The track time information is the time information of the stay of the risk patient in the track address information.
In step S202, matching is performed in the medical data according to at least one preset symptom, and a risk patient who is successfully matched and a symptom set successfully matched by the risk patient are obtained.
In an embodiment of the present disclosure, the symptom set includes one or more symptoms in the medical data of the at-risk patient that match the predetermined symptoms. For example, a symptom set may be, for example, but not limited to, "fever," "cough," "fever, cough," and the like.
In step S204, symptom time information of the at-risk patient is determined from the medical data.
In the disclosed embodiment, the symptom time information may be a specific time when the risk patient is suffering from the above symptoms.
In step S206, a first weight of the at-risk patient is determined according to the successfully matched symptom set of the at-risk patient.
In the embodiment of the present disclosure, different symptom sets may correspond to different weight values. For example, the weight value for symptom set "fever" may be, for example, 0.7, the weight value for symptom "cough" may be, for example, 0.6, and the weight value for symptom set "fever, cough" may be, for example, 1.2. Wherein the weight value of the successfully matched symptom set of the at-risk patient may be determined as the first weight of the at-risk patient.
In step S208, a second weight corresponding to each trajectory address information target region of the at-risk patient is determined according to the trajectory time information and the symptom time information of the at-risk patient.
In the embodiment of the disclosure, the difference operation may be performed on the trajectory time information and the symptom time information of the risk patient, and the nonlinear operation or linear operation may be performed on the difference operation result of the trajectory time information and the symptom time information, so as to determine the second weight corresponding to each trajectory address information of the risk patient. For example, if a patient a at risk stays at X, Y, and Z for the first day, the symptom time information of the patient a at risk stays at Z for the second day, it can be determined that the patient at risk has a second weight of 0 with the target area X, 0.3 with the target area Y, and 0.8 with the target area Z.
In step S210, a risk factor of the risk patient corresponding to the target area is determined according to the first weight and the second weight.
In an embodiment of the present disclosure, a sum of the first weight and the second weight may be determined as a risk factor for the at-risk patient corresponding to the target area.
In step S212, a total risk value of the target area in each preset period in the preset duration is determined according to risk factors of the target area and patients at risk in each preset period in the preset duration.
In the embodiment of the present disclosure, the risk factors corresponding to the target area and the risk patients existing in the target area in each preset period within the preset duration may be summed to obtain the total risk value of the target area in each preset period within the preset duration.
In this embodiment, determining the first weight of the at-risk patient from the set of symptoms can reflect the rate of contribution of the at-risk patient to the risk of the infectious disease. And determining a second weight corresponding to the risk patient and each track address information according to the track time information and the symptom time information, and comparing the time of the risk patient producing symptoms with the stay time on each track address information to accurately reflect the risk contribution rate of the event of the risk patient producing symptoms to each track address information. And determining the corresponding target weight of each target region in each preset period within a preset time length according to the first weight and the second weight, and accurately determining the target weight of each target region according to the symptom severity and the symptom occurrence time of the risk patients so as to accurately reflect the risk degree of each target region.
FIG. 3 is a flowchart illustrating a method of risk area prediction according to an exemplary embodiment.
As shown in fig. 3, in the embodiment of the present disclosure, the step S108 may further include the following steps.
In step S302, a risk sub-index corresponding to each preset period within the preset duration of the target region is determined according to the mean value and the standard deviation corresponding to the target region and the total risk value of the target region within each preset period within the preset duration.
In the embodiment of the present disclosure, the mean and the standard deviation may be determined, for example, according to manual experience, and may also be set by performing statistical analysis on historical data of the target area, which is not particularly limited in the technical solution of the present disclosure.
In step S304, the number of cycles of the preset period in which the risk sub-index is greater than the preset risk sub-index threshold is determined according to the risk sub-index corresponding to each preset period within the preset duration.
In step S306, the risk index of the target area in the preset duration is determined according to the risk sub-index corresponding to each preset period in the preset duration and the number of periods.
In an exemplary embodiment, when the risk sub-index is n and the number of cycles is l, the risk index of the target area may be determined according to a sum or a weighted sum of the risk sub-index and the number of cycles.
In the embodiment of the disclosure, the patients with risks in each preset period in the target area are further calculated through the mean value and the standard deviation corresponding to the target area, the obtained risk sub-indexes and the number of periods can be subjected to fusion analysis, and the risk index of the target area is obtained, so that the prediction of the potential risk area is realized, and guidance is provided for the epidemic situation development prevention and control work in the early stage of the epidemic situation.
Fig. 4 is a flow chart illustrating a method of risk area prediction according to an exemplary embodiment.
As shown in fig. 4, in the embodiment of the present disclosure, the step S302 may include the following steps.
In step S402, a difference operation is performed between the total risk value of the target area in each preset period within a preset time and the mean value corresponding to the target area, so as to obtain a difference result.
In the embodiment of the disclosure, when the total risk value of the target area in each preset period is a, and the mean value corresponding to the target area is μ, performing a difference operation on the total risk value of the target area in each preset period and the mean value corresponding to the target area can be performed according to the following formula:
b＝a-μ (1)
wherein the difference result is b.
In step S404, a division operation is performed on the difference result and the standard deviation corresponding to the target area, and a modulus is determined as a risk sub-index corresponding to each preset period of the target area within a preset duration.
In the embodiment of the present disclosure, when the standard deviation corresponding to the target region is σ, dividing the difference result by the standard deviation corresponding to the target region may be performed according to the following formula:
n＝b％σ (2)
wherein n is the Risk sub-index.
In an exemplary embodiment, in step S404, the quotient value may also be determined as a risk sub-index corresponding to each preset period.
In this embodiment, the total risk value of the target area in each preset period is subjected to the dismantling calculation according to the mean value and the standard deviation corresponding to the target area, and the obtained risk sub-index can reflect the sudden increase degree of the total risk value in each preset period relative to the normal situation. Thereby providing reliable data support for subsequent prediction of the potential risk area.
Fig. 5 is a flow chart illustrating a method of risk area prediction according to an exemplary embodiment.
As shown in fig. 5, a risk area prediction method of an embodiment of the present disclosure may include the following steps.
In step S502, a grade of the risk area is determined according to a multiple that a risk index of the target area within a preset time length is greater than a preset risk threshold corresponding to the preset time length.
In step S504, the risk area is displayed with different color warnings on the map according to the grade of the risk area.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 6 is a block diagram illustrating a risk area prediction device according to an example embodiment. The risk area prediction apparatus 60 provided in the embodiment of the present disclosure may include: medical data acquisition module 602, risk patient determination module 604, trajectory information determination module 606, total risk value determination module 608, risk index determination module 610, and risk area determination module 612.
The medical data acquisition module 602 may be configured to acquire medical data for a plurality of patients.
The at-risk patient determination module 604 may be configured to determine at-risk patients of a plurality of patients based on the medical data.
Trajectory information determination module 606 may be configured to determine trajectory information for at-risk patients.
The total risk value determination module 608 may be configured to determine a total risk value for the target area for each predetermined period within a predetermined duration based on the trajectory information and the medical data of the at-risk patient.
The risk index determining module 610 may be configured to determine the risk index of the target area within the preset duration according to the total risk value of the target area within the preset duration for each preset period.
The risk area determining module 612 may be configured to determine that the target area is a risk area if the risk index of the target area within the preset duration is greater than a preset risk threshold corresponding to the preset duration.
According to the risk area prediction device provided by the embodiment of the disclosure, historical track information of risk patients is counted, a total risk value of each target area in each preset period can be determined, and the total risk value in each preset period visually reflects risk change conditions. Furthermore, the total risk value of the target area in each preset period can be analyzed, the risk index of the target area in a preset time length is obtained through mining, the potential risk area is predicted based on the risk index, the target area with the risk index larger than a preset risk threshold is determined as the risk area, and guidance can be provided for epidemic situation prevention and control work in the early stage of the epidemic situation.
In an exemplary embodiment, the at-risk patient determination module 604 may be configured to perform matching in the medical data according to at least one preset symptom, obtaining a successfully matched at-risk patient and a successfully matched symptom set of the at-risk patient.
In an exemplary embodiment, the track information includes track address information and track time information. The total risk value determination module 608 may include a symptom time element, a first weighting element, a second weighting element, a risk factor element, and a total risk value element. Wherein the symptom time unit is configurable to determine symptom time information for the at-risk patient based on the medical data. The first weighting unit may be configured to determine a first weight for the at-risk patient based on the successfully matched symptom set for the at-risk patient. The second weighting unit may be configured to determine a second weight of the at-risk patient corresponding to the target region based on the trajectory time information and the symptom time information of the at-risk patient. The risk factor unit may be configured to determine a risk factor for the at-risk patient corresponding to the target region based on the first weight and the second weight. The risk total value unit can be configured to determine a risk total value of the target area in each preset period in the preset time length according to risk factors of risk patients existing in the target area in each preset period in the preset time length and corresponding to the target area.
In an exemplary embodiment, the total risk value determination module 608 may include a risk people number unit and a total risk value unit. The risk number unit can be configured to determine the number of the risk patients in the target area in each preset period within a preset time according to the track information and the medical data of the risk patients. The total risk value unit can be configured to determine the total risk value of the target area in each preset period within a preset time length according to the number of the risk patients.
In an exemplary embodiment, the risk index determination module 610 may include a risk sub-index determination unit, a cycle number determination unit, and a risk index determination sub-unit. The risk sub-index determining unit may be configured to determine a risk sub-index corresponding to each preset period of the target area within a preset time length according to a mean value and a standard deviation corresponding to the target area and a total risk value of the target area within each preset period within the preset time length. The cycle number determining unit may be configured to determine, according to the risk sub-index corresponding to each preset cycle within the preset duration, the cycle number of the preset cycle in which the risk sub-index is greater than the preset risk sub-index threshold. The risk index determining subunit may be configured to determine the risk index of the target area within the preset duration according to the risk sub-index corresponding to each preset period within the preset duration and the number of periods.
In an exemplary embodiment, the risk sub-index determination unit may include a first difference sub-unit, a first modulus sub-unit. The first difference subunit can be configured to perform difference operation on the number of the patients with risk in each preset period in the preset time period of the target area and the mean value corresponding to the target area to obtain a difference result. The first module value subunit may be configured to perform division on the difference result and the standard deviation corresponding to the target area, and determine the module value as a risk sub-index corresponding to each preset period of the target area within a preset time duration.
In an exemplary embodiment, the risk patient determination module 604 may include additional data units and/or reporting data units, and symptom matching units. Wherein the new case data unit may be configured to acquire new case data within a preset time period in the medical data. The reported data unit can be configured to acquire reported case data sent by the equipment terminal within a preset time length. The symptom matching unit can be configured to perform symptom matching on the newly added case data and/or the reported case data according to at least one preset symptom, and determine the patient corresponding to the successfully matched case as the risk patient.
In an exemplary embodiment, the trajectory information determination module 606 may include a demographic information unit and/or a historical address information unit. The demographic information unit can be configured to acquire the demographic information of the risk patient so as to determine the track information of the risk patient according to the working place information and the living place information in the demographic information. The historical address information unit can be configured to acquire the target device bound with the identity of the risk patient, acquire the historical address information uploaded by the target device, and determine the historical track information of the risk patient according to the historical address information.
In an exemplary embodiment, the risk region prediction apparatus may further include: a risk level determination module and a region display module. The risk level determination module can be configured to determine the level of the risk area according to a multiple that the risk index of the target area in the preset time length is greater than a preset risk threshold corresponding to the preset time length. The area display module may be configured to perform early warning display of different colors on the risk area on the map according to the grade of the risk area. In the embodiment, the early warning display of different colors can be carried out on the target area of the target area according to the grade of the target area, and the risk area can be visually displayed.
FIG. 7 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device implementing an embodiment of the present disclosure. It should be noted that the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the application scope of the embodiment of the present disclosure.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for system operation are also stored. The CPU 701, ROM702, and RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, the processes described below with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules and/or units and/or sub-units described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described modules and/or units and/or sub-units may also be disposed in a processor. Wherein the names of such modules and/or units and/or sub-units in some cases do not constitute a limitation on the modules and/or units and/or sub-units themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiment; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method as described in the embodiments below. For example, the electronic device may implement the steps shown in fig. 1 or fig. 2 or fig. 3 or fig. 4 or fig. 5.
It should be noted that although in the above detailed description several modules or units or sub-units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units or sub-units described above may be embodied in one module or unit or sub-unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units or sub-units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (9)
1. A method for risk area prediction, comprising:
acquiring medical data of a plurality of patients;
determining a risk patient of the plurality of patients from the medical data;
determining trajectory information for the at-risk patient;
determining a total risk value of the target area in each preset period within a preset duration according to the track information of the risk patients and the medical data, wherein the total risk value is used for reflecting the risk change condition of the target area in each preset period;
determining a risk index of the target area in the preset time according to the total risk value of the target area in each preset period in the preset time;
if the risk index of the target area in the preset time length is larger than a preset risk threshold corresponding to the preset time length, determining that the target area is a risk area;
wherein determining at-risk patients of the plurality of patients from the medical data comprises: matching in the medical data according to at least one preset symptom to obtain the successfully matched risk patient and the successfully matched symptom set of the risk patient;
the track information comprises track address information and track time information; determining a total risk value of the target region in each preset period within a preset time according to the track information of the risk patients and the medical data comprises:
determining symptom time information of the at-risk patient from the medical data;
determining a first weight of the at-risk patient according to the symptom set matched by the at-risk patient successfully;
determining a second weight of the at-risk patient corresponding to the target region according to the trajectory time information and the symptom time information of the at-risk patient;
determining a risk factor of the at-risk patient corresponding to the target area according to the first weight and the second weight;
and determining the total risk value of the target area in each preset period in the preset duration according to the risk factors of the target area corresponding to the risk patients in each preset period in the preset duration.
2. The method of claim 1, wherein determining a total risk value for the target region for each preset period within a preset time period based on the trajectory information of the at-risk patient and the medical data comprises:
determining the number of the risk patients in the target area in each preset period in the preset time according to the track information of the risk patients and the medical data;
and determining the total risk value of the target area in each preset period within the preset time length according to the number of the risk patients.
3. The method according to claim 1 or 2, wherein determining the risk index of the target area within the preset time period according to the total risk value of the target area within each preset period within the preset time period comprises:
determining a risk sub-index corresponding to the target area in each preset period in the preset duration according to the mean value and the standard deviation corresponding to the target area and the total risk value of the target area in each preset period in the preset duration;
determining the number of the periods of the preset periods with the risk sub-index larger than a preset risk sub-index threshold value according to the risk sub-index corresponding to each preset period in the preset duration;
and determining the risk index of the target area in the preset time length according to the risk sub-index corresponding to each preset period in the preset time length and the number of the periods.
4. The method of claim 3, wherein determining the risk sub-index of the target area for each preset period within the preset duration according to the mean and the standard deviation corresponding to the target area and the total risk value of the target area for each preset period within the preset duration comprises:
performing difference operation on the total risk value of the target area in each preset period in a preset time length and the mean value corresponding to the target area to obtain a difference result;
and performing division operation on the difference result and the standard deviation corresponding to the target area, and determining a modulus as the risk sub-index corresponding to each preset period of the target area in the preset time length.
5. The method of claim 1, wherein determining trajectory information for the at-risk patient comprises:
acquiring demographic information of the risk patients to determine track information of the risk patients according to work place information and residence place information in the demographic information; and/or
And acquiring target equipment bound with the identity of the risk patient, acquiring historical address information uploaded by the target equipment, and determining the track information of the risk patient according to the historical address information.
6. The method of claim 1, further comprising:
determining the grade of the risk area according to the multiple that the risk index of the target area in the preset time length is larger than a preset risk threshold corresponding to the preset time length;
and carrying out early warning display of different colors on the risk area on a map according to the grade of the risk area.
7. A risk region prediction apparatus, comprising:
a medical data acquisition module configured to acquire medical data of a plurality of patients;
a risk patient determination module configured to determine a risk patient of the plurality of patients from the medical data;
a trajectory information determination module configured to determine trajectory information for the at-risk patient;
the risk total value determining module is configured to determine a risk total value of the target area in each preset period within a preset time length according to the track information of the risk patient and the medical data, wherein the risk total value is used for reflecting the risk change condition of the target area in each preset period;
a risk index determination module configured to determine a risk index of the target area within the preset duration according to the total risk value of the target area within each preset period within the preset duration;
a risk area determination module configured to determine that the target area is a risk area if the risk index of the target area within the preset duration is greater than a preset risk threshold corresponding to the preset duration;
wherein the at-risk patient determination module is further configured to perform matching in the medical data according to at least one preset symptom, and obtain the successfully matched at-risk patient and a successfully matched symptom set of the at-risk patient;
the track information comprises track address information and track time information; the risk index determination module is further configured to: determining symptom time information of the at-risk patient from the medical data; determining a first weight of the at-risk patient according to the symptom set matched successfully by the at-risk patient; determining a second weight of the at-risk patient corresponding to the target area according to the trajectory time information and the symptom time information of the at-risk patient; determining a risk factor of the at-risk patient corresponding to the target area according to the first weight and the second weight; and determining the total risk value of the target area in each preset period in the preset duration according to the risk factors of the target area corresponding to the risk patients in each preset period in the preset duration.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
9. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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| CN113963413A (en) * | 2021-10-27 | 2022-01-21 | 平安国际智慧城市科技股份有限公司 | Epidemic situation investigation method and device based on artificial intelligence, electronic equipment and medium |
| CN113707338B (en) * | 2021-10-28 | 2022-08-30 | 南方科技大学 | Scenic spot epidemic situation risk prediction and current limiting method, device, equipment and storage medium |
| US20230135367A1 (en) * | 2021-11-02 | 2023-05-04 | International Business Machines Corporation | Determining infection risk levels |
| CN114613506B (en) * | 2022-03-02 | 2022-11-15 | 杭州杏林信息科技有限公司 | Path prediction control method and device based on big data and storage medium |
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