CN108320810A - Disease abnormal deviation data examination method and device, computer installation and storage medium - Google Patents
Disease abnormal deviation data examination method and device, computer installation and storage medium Download PDFInfo
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- CN108320810A CN108320810A CN201810321079.6A CN201810321079A CN108320810A CN 108320810 A CN108320810 A CN 108320810A CN 201810321079 A CN201810321079 A CN 201810321079A CN 108320810 A CN108320810 A CN 108320810A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
Abstract
A kind of disease abnormal deviation data examination method, the method includes：Obtain time series data X, the X=[x of disease surveillance0, x1, x2..., xt]；Access time window size w calculates random time point i the first quantile Q1 and the second quantile Q2 of disease surveillance data in the corresponding time windows of the time point i；Calculate the difference D of the second quantile Q2 and the first quantile Q1；Calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound T2；By the corresponding disease surveillance data x of time point iiIt is compared with the upper bound T1 and the lower bound T2, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound T2, then the corresponding disease surveillance data x of the time point iiFor exceptional value.The present invention also provides a kind of disease anomaly data detection device, computer installation and readable storage medium storing program for executing.The disease surveillance data exception detection of efficiently and accurately may be implemented in the present invention.
Description
Technical field
The present invention relates to technical field of data processing, and in particular to a kind of disease abnormal deviation data examination method and device, meter
Calculation machine device and computer readable storage medium.
Background technology
With the acceleration of global economic integration, economy increases with exchange activity, and crowd's flowing is increasingly frequent, is
The propagation of disease provides favorable environment with outburst, and public health health problem is more and more severeer.Meanwhile social and natural ring
Variation also occurs for border, and environmental pollution, natural calamity etc. influence increasing for public health event and also increase emerging public health
The possibility of event outburst.
How to detect disease abnormal data, is defended so as to which the burst of EARLY RECOGNITION to disease popularity or outburst is public
It makes trouble part, takes corresponding control measure as early as possible, loss caused by public health emergency is preferably minimized, is become urgently
It solves the problems, such as.
Existing method for detecting abnormality, such as zscore method for detecting abnormality, the abnormality detection side Grubbs (Grubbs)
Method requires that data meet normal distribution, and actually many times cannot be satisfied this requirement.For traditional quartile
Method can use all data, and data remote in the past are low to the reference value of current data, are as a result easier to occur inclined
Difference.
Invention content
In view of the foregoing, it is necessary to propose a kind of disease abnormal deviation data examination method and device, computer installation and meter
The disease surveillance data exception detection of efficiently and accurately may be implemented in calculation machine readable storage medium storing program for executing.
The first aspect of the application provides a kind of disease abnormal deviation data examination method, the method includes：
Obtain time series data X, the X=[x of disease surveillance0,x1,x2,…,xt], wherein x0,x1,x2,…,xtFor correspondence
In time point 0,1,2 ..., the disease surveillance data of t；
Access time window size w, for random time point i, i=w, w+1 ..., it is corresponding to calculate the time point i by t
The corresponding time window of first quantile Q1 of disease surveillance data and the second quantile Q2 in time window, the time point i it is big
Small is w, and the first quantile Q1 is less than the second quantile Q2；
Calculate the difference D of the second quantile Q2 and the first quantile Q1；
Calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound T2, wherein the upper bound T1
=Q2+k*D, the lower bound T2=Q1-k*D, k are adjustable parameter；
By the corresponding disease surveillance data x of time point iiIt is compared with the upper bound T1 and the lower bound T2, if the time
The corresponding disease surveillance data x of point iiMore than the upper bound T1 or it is less than the lower bound T2, then the time point i is corresponding
Disease surveillance data xiFor exceptional value.
In alternatively possible realization method, the first quantile Q1 is 0.25 quantile, the second quantile Q2
It is 0.75 quantile, the k is in [1.5,3] section value.
In alternatively possible realization method, the method further includes：
Obtain the exception that other disease abnormal deviation data examination methods are detected the time series data
Value；
The exceptional value that the disease abnormal deviation data examination method is obtained and other diseases anomaly data detection side
The exceptional value that method obtains is compared；
The exceptional value obtained according to the disease abnormal deviation data examination method and other disease anomaly data detections
The comparison result for the exceptional value that method obtains obtains final exceptional value.
In alternatively possible realization method, the method further includes：
Disease anomaly data detection is carried out to area and regional subordinate hospital respectively, the area for obtaining being directed to area is abnormal
Value and for regional subordinate hospital regional subordinate hospital exceptional value；
Compare the regional exceptional value and regional subordinate hospital exceptional value, according to the regional exceptional value and described
The comparison result of regional subordinate hospital exceptional value obtains final exceptional value.
In alternatively possible realization method, the disease surveillance data include the medical number, consultation rate, morbidity of disease
Number, incidence.
In alternatively possible realization method, the time series data for obtaining disease surveillance includes：
The disease surveillance network being made of multiple monitoring points is established in predeterminable area, and disease prison is obtained from the monitoring point
Measured data constitutes the time series data by the disease surveillance data.
In alternatively possible realization method, the monitoring point includes the medical institutions for meeting the number of presetting or scale, learns
School and mechanism of nursery schools and childcare centres, pharmacy.
The second aspect of the application provides a kind of disease anomaly data detection device, and described device includes：
Acquiring unit, time series data X, the X=[x for obtaining disease surveillance0,x1,x2,…,xt], wherein x0,
x1,x2,…,xtTo correspond to time point 0,1,2 ...,tDisease surveillance data；
Computing unit is used for access time window size w, for random time point i, i=w, w+1 ..., t, described in calculating
First quantile Q1 of disease surveillance data and the second quantile Q2, i pairs of the time point in the corresponding time windows of time point i
The size for the time window answered is w, and the first quantile Q1 is less than the second quantile Q2；
The computing unit is additionally operable to calculate the difference D of the second quantile Q2 and the first quantile Q1；
The computing unit is additionally operable to calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound
T2, wherein the upper bound T1=Q2+k*D, the lower bound T2=Q1-k*D, k are adjustable parameter；
First comparing unit is used for the corresponding disease surveillance data x of time point iiWith the upper bound T1 and the lower bound
T2 is compared, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound T2, then
The corresponding disease surveillance data x of the time point iiFor exceptional value.
The third aspect of the application provides a kind of computer installation, and the computer installation includes processor, the processing
Device is for executing the computer program stored in memory to realize the disease abnormal deviation data examination method.
The fourth aspect of the application provides a kind of computer readable storage medium, on the computer readable storage medium
It is stored with computer program, the computer program realizes the disease abnormal deviation data examination method when being executed by processor.
The present invention obtains time series data X, the X=[x of disease surveillance0,x1,x2,…,xt], wherein x0,x1,x2,…,
xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；Access time window size w, for random time point i, i=
W, w+1 ..., t calculate the first quantile Q1 of disease surveillance data and second point of position in the corresponding time windows of the time point i
The size of the corresponding time windows of number Q2, the time point i is w, and the first quantile Q1 is less than the second quantile Q2；
Calculate the difference D of the second quantile Q2 and the first quantile Q1；Calculate the corresponding disease surveillance of the time point i
Data xiUpper bound T1 and lower bound T2, wherein the upper bound T1=Q2+k*D, the lower bound T2=Q1-k*D, k be adjustable ginseng
Number；By the corresponding disease surveillance data x of time point iiIt is compared with the upper bound T1 and the lower bound T2, if i pairs of time point
The disease surveillance data x answerediMore than the upper bound T1 or it is less than the lower bound T2, then the corresponding disease prisons of the time point i
Measured data xiFor exceptional value.
For time series data, more neighbouring data are to currently more having reference value, and reference value more remote is more
It is low.The present invention considers the characteristics of time series, by neighbouring historical data distribution as a reference to being carried out to current data different
Normal judgement can obtain preferable testing result.Also, the present invention does not require data distribution, it is not required that is normal state
Distribution, can directly carry out abnormality detection the disease surveillance data in short-term, overcome and data in time window is required to meet
The limitation of normal distribution has higher usability for the abnormality detection of disease surveillance data.Therefore, the present invention realizes
The disease surveillance data exception detection of efficiently and accurately.
Description of the drawings
Fig. 1 is the flow chart for the disease abnormal deviation data examination method that the embodiment of the present invention one provides.
Fig. 2 is the structure chart of disease anomaly data detection device provided by Embodiment 2 of the present invention.
Fig. 3 is the schematic diagram for the computer installation that the embodiment of the present invention four provides.
Specific implementation mode
To better understand the objects, features and advantages of the present invention, below in conjunction with the accompanying drawings and specific real
Applying example, the present invention will be described in detail.It should be noted that in the absence of conflict, embodiments herein and implementation
Feature in example can be combined with each other.
Many details are elaborated in the following description to facilitate a thorough understanding of the present invention, described embodiment
Only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field is common
The every other embodiment that technical staff is obtained without making creative work belongs to what the present invention protected
Range.
Unless otherwise defined, all of technologies and scientific terms used here by the article and the technical field for belonging to the present invention
The normally understood meaning of technical staff it is identical.Used term is intended merely to retouch in the description of the invention herein
State the purpose of specific embodiment, it is not intended that in the limitation present invention.
Preferably, disease abnormal deviation data examination method of the invention is applied in one or more computer installation.Institute
State computer installation be it is a kind of can be automatic to carry out numerical computations and/or information processing according to the instruction for being previously set or storing
Equipment, hardware includes but not limited to microprocessor, application-specific integrated circuit (Application Specific
Integrated Circuit, ASIC), programmable gate array (Field- Programmable Gate Array, FPGA),
Digital processing unit (Digital Signal Processor, DSP), embedded device etc..
The computer installation can be that the calculating such as desktop PC, notebook, palm PC and cloud server are set
It is standby.The computer installation can with user by modes such as keyboard, mouse, remote controler, touch tablet or voice-operated devices into pedestrian
Machine interacts.
Embodiment one
Fig. 1 is the flow chart for the disease abnormal deviation data examination method that the embodiment of the present invention one provides.The disease exception number
It is applied to computer installation according to detection method.Exception in the disease abnormal deviation data examination method detection disease surveillance data
Value, so as to which EARLY RECOGNITION to disease popularity or the public health emergency of outburst, takes corresponding control to arrange as early as possible
It applies, loss caused by public health emergency is preferably minimized.
As shown in Figure 1, the disease abnormal deviation data examination method specifically includes following steps：
Step 101, time series data X, the X=[x of disease surveillance are obtained0,x1,x2,…,xt], wherein x0,x1,
x2,…,xtFor time point 0,1,2 ..., the disease surveillance data of t.
The disease surveillance data may include the monitoring of the diseases such as influenza, hand-foot-and-mouth disease, measles, mumps
Data.
The disease surveillance network being made of multiple monitoring points can be established in predeterminable area (such as provinces and cities, area), from institute
It states monitoring point and obtains disease surveillance data, the time series data of disease surveillance is made of the disease surveillance data.It can select
It selects medical institutions, school and mechanism of nursery schools and childcare centres, pharmacy etc. and is used as monitoring point, disease surveillance is carried out to corresponding target group respectively
And data acquisition.The place for meeting preset condition can be selected as monitoring point.The preset condition may include number, rule
Mould etc..For example, select number of student reach preset quantity school and mechanism of nursery schools and childcare centres as monitoring point.For another example, scale is selected
(such as being counted using daily sales) reaches the pharmacy of default scale as monitoring point.For another example, scale is selected (such as to see a doctor with day
Demographics) reach the hospital of default scale as monitoring point.
The disease surveillance data of different time constitute the time series data of disease surveillance.For example, can will be single with day
The collected disease surveillance data in position constitute the time series data of disease surveillance.Alternatively, can will be collected as unit of week
Disease surveillance data constitute disease surveillance time series data.
Medical institutions' (including mainly hospital) are the places that can most capture disease and break out omen in early days, are to carry out disease prison
The first choice of survey.Can go to a doctor situation according to patient, obtain disease surveillance data.
A part of disease people can voluntarily go pharmacy's purchase medicine to alleviate early symptom, therefore, can be according to the drug pin of pharmacy
Situation is sold, disease surveillance data are obtained.
The people at highest risk and the important link during transmission that Children and teenager is disease, should also reinforce
Monitoring to the crowd.School and mechanism of nursery schools and childcare centres are to monitor the preferable place of Children and teenager disease incidence situation.It can root
According to the situation of asking for leave of the Children and teenager of school and mechanism of nursery schools and childcare centres, disease surveillance data are obtained.
Therefore, medical institutions, school and mechanism of nursery schools and childcare centres, this three classes place of pharmacy is mainly selected to carry out disease in the present invention
The acquisition of monitoring data.Certainly, the above-mentioned selection to data source can not limit and increase in a further embodiment or replace
It changes other and pays close attention to the data source of crowd or place as monitoring.For example, hotel can be included in disease surveillance range, obtain
Hotel is taken to move in the disease surveillance data of personnel.
As needed, the disease surveillance data that any type monitoring point (such as medical institutions) acquires can be taken to constitute disease
The time series data of disease monitoring.For example, the time series of the disease surveillance data composition disease surveillance of hospital's acquisition can be taken
Data.Alternatively, the time series data of disease surveillance can be constituted in conjunction with the disease surveillance data of multiclass monitoring point acquisition.Example
Such as, using the disease surveillance data that pharmacy participates in as supplement, disease can be constituted based on the disease surveillance data of hospital's acquisition
The time series data of disease monitoring.
Disease surveillance data may include the medical number, consultation rate, the illness data such as number, incidence of falling ill of disease.Example
Such as, the daily medical number that disease (such as influenza) can be obtained from medical institutions (such as hospital), by disease (such as influenza)
Daily medical number is used as disease surveillance data.For another example, the daily morbidity of the disease (such as influenza) of student can be obtained from school
Number, using the daily morbidity number of disease (such as influenza) as disease surveillance data.
It should be noted that time point 0 and time point t indicate the initial time of time series data and terminate the time, and
It is non-to the temporal restriction of time series data.It can be using random time point as time point 0.
Step 102, access time window size w, for each time point i in time point w to time point t, described in calculating
First quantile Q1 of disease surveillance data and the second quantile Q2, i pairs of the time point in the corresponding time windows of time point i
The size for the time window answered is w, and the first quantile Q1 is less than the second quantile Q2.
Time window a period of time neighbouring before being given point in time.For example, set time window size w as 4, given time
Point is t=10, and time window is exactly t=6 to t=9 this periods；Given point in time is t=11, and time window is exactly t=7 to t=
11 this periods；The rest may be inferred.
The value of time point i is the length because of the time window obtained in this period from t=0 to t=w-1 since w
Degree be less than w, w not enough disease surveillance data come calculate disease surveillance data the first quantile Q1 and second point of position
Number Q2.
Time window size w is adjustable parameter, can be adjusted according to actual conditions.
In one embodiment, the disease surveillance data are that (such as the daily of disease is gone to a doctor for daily disease surveillance data
Number), the time window size w can be taken as 7 (i.e. one weeks), thus calculate the disease surveillance data of given point in time the last week
The first quantile Q1 and the second quantile Q2.
In another embodiment, the disease surveillance data be weekly disease surveillance data (such as disease weekly
Examine number), the time window size w can be taken as 3 (i.e. three weeks), thus calculate 3 weeks before given point in time disease surveillance data
The first quantile Q1 and the second quantile Q2.
In one embodiment, the first quantile Q1 can be 0.25 quantile, and the second quantile Q2 can be
0.75 quantile.
In another embodiment, the first quantile Q1 can be 0.2 quantile, and the second quantile Q2 can be with
It is 0.7 quantile.
Step 103, the difference D of the second quantile Q2 and the first quantile Q1 are calculated.
For example, the second quantile Q2 is 3, the first quantile Q1 is 1, then the second quantile Q2 and institute
The difference D for stating the first quantile Q1 is 3-1=2.
For another example, the second quantile Q2 be 50, the first quantile Q1 be 30, then the second quantile Q2 with
The difference D of the first quantile Q1 is 50-30=20.
Step 104, the corresponding disease surveillance data x of the time point i are calculatediUpper bound T1 and lower bound T2, wherein institute
Upper bound T1=Q2+k*D is stated, the lower bound T2=Q1-k*D, k are adjustable parameter.
In one embodiment, the first quantile Q1 is 0.25 quantile, and the second quantile Q2 is 0.75 point of position
Number, corresponding k can be in [1.5,3] section value, such as value is 2.
Step 105, by the corresponding disease surveillance data x of time point iiCompared with the upper bound T1 and the lower bound T2
Compared with if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound T2, then the time
The corresponding disease surveillance data x of point iiFor exceptional value.
For example, 9 corresponding disease surveillance data x of time point9It is 5, corresponding upper bound T1 is 10, the corresponding lower bound T2
Be 7, then disease surveillance data x9For exceptional value.
For another example, 8 corresponding disease surveillance data x of time point8It is 6, corresponding upper bound T1 is 9, and the corresponding lower bound T2 is
57, then disease surveillance data x8For non-exceptional value.
The disease abnormal deviation data examination method of embodiment one obtains time series data X, the X=[x of disease surveillance0,x1,
x2,…,xt], wherein x0,x1,x2,…,xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；Access time window
Size w, for random time point i, i=w, w+1 ..., t calculates disease surveillance number in the corresponding time windows of the time point i
According to the first quantile Q1 and the second quantile Q2, the size of the corresponding time window of the time point i is w, first point of position
Number Q1 is less than the second quantile Q2；Calculate the difference D of the second quantile Q2 and the first quantile Q1；It calculates
The corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound T2, wherein the upper bound T1=Q2+k*D, institute
It is adjustable parameter to state lower bound T2=Q1-k*D, k；By the corresponding disease surveillance data x of time point iiWith the upper bound T1 and described
Lower bound T2 is compared, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound
T2, then corresponding disease surveillance data x of the time point iiFor exceptional value.
For time series data, more neighbouring data are to currently more having reference value, and reference value more remote is more
It is low.Embodiment one considers the characteristics of time series, by neighbouring historical data distribution as a reference to being carried out to current data
Abnormal judgement, can obtain preferable testing result.Also, embodiment a pair of data distribution does not require, it is not required that is
Normal distribution can directly carry out abnormality detection the disease surveillance data in short-term, overcome and require data in time window
The limitation for meeting normal distribution has higher usability for the abnormality detection of disease surveillance data.Therefore, embodiment
One realizes the disease surveillance data exception detection of efficiently and accurately.
In another embodiment, (disease of embodiment one can be different from conjunction with other disease abnormal deviation data examination methods
Sick abnormal deviation data examination method, such as Grubbs detection methods) carry out disease anomaly data detection.Specifically, embodiment one
Disease abnormal deviation data examination method can also include the following steps：Other disease abnormal deviation data examination methods are obtained to described
The exceptional value that time series data is detected；The exception that the disease abnormal deviation data examination method of embodiment one is obtained
Value is compared with the exceptional value that other disease abnormal deviation data examination methods obtain；According to the disease exception number of embodiment one
The comparison result for the exceptional value that the exceptional value obtained according to detection method is obtained with other disease abnormal deviation data examination methods obtains
Final exceptional value.Other disease abnormal deviation data examination methods may include one kind, can also include a variety of.Each other
Disease abnormal deviation data examination method can obtain one group of corresponding exceptional value.
For example, the disease abnormal deviation data examination method using embodiment one carries out the time series data of disease surveillance
Disease anomaly data detection obtains the first exceptional value, using different from the disease abnormal deviation data examination method of embodiment one
Two disease abnormal deviation data examination methods (such as Grubbs methods of inspection) carry out disease to the time series data of the disease surveillance
Sick anomaly data detection obtains the second exceptional value, first exceptional value and it is described to second it is abnormal be worth to it is final
Exceptional value.If first exceptional value with it is described consistent to the second exceptional value, with first exceptional value/abnormal to second
Value is as final exceptional value；Otherwise, if first exceptional value and described inconsistent to the second exceptional value, give up described
First exceptional value and second exceptional value.
For another example, the time series data of disease surveillance is carried out using the disease abnormal deviation data examination method of embodiment one
Disease anomaly data detection obtains the first exceptional value, using different from the disease abnormal deviation data examination method of embodiment one
Two disease abnormal deviation data examination methods (such as Grubbs detection methods) carry out disease to the time series data of the disease surveillance
Sick anomaly data detection obtains the second exceptional value, using the third different from the disease abnormal deviation data examination method of embodiment one
Disease abnormal deviation data examination method (such as Bayesian detection method) carries out disease to the time series data of the disease surveillance
Anomaly data detection obtains third exceptional value, first exceptional value, second exceptional value and the third exceptional value
Obtain final exceptional value.If at least two in first exceptional value, second exceptional value and the third exceptional value
Exceptional value is consistent, then using consistent exceptional value as final exceptional value.
On the basis of the disease abnormal deviation data examination method of embodiment one, in conjunction with other disease anomaly data detections
Method carries out disease anomaly data detection, can obtain more accurate disease anomaly data detection result.
In another embodiment, the disease abnormal deviation data examination method of embodiment one may be used respectively to area and ground
Subordinate hospital of area carries out disease anomaly data detection, obtains for the testing result (i.e. regional exceptional value) in area and for ground
The testing result (i.e. regional subordinate hospital exceptional value) of subordinate hospital of area, the area exceptional value and the regional subordinate
Hospital's exceptional value obtains final different according to the comparison result of the regional exceptional value and regional subordinate hospital exceptional value
Constant value.If consistent with for the regional testing result of subordinate hospital for the testing result in area, with the detection for area
As a result/testing result of regional subordinate hospital is directed to as final testing result；Otherwise, if for regional testing result
It is inconsistent with the testing result for regional subordinate hospital, then give up the testing result for area and under area
Belong to the testing result of hospital.
Wherein, it is the time series number according to the disease surveillance in the area to carry out disease anomaly data detection to area
Regional exceptional value is obtained according to (such as the disease control department in area be collected into from each regional subordinate hospital disease surveillance data),
It is the time series according to the disease surveillance of the regional subordinate hospital to carry out disease anomaly data detection to regional subordinate hospital
Data obtain the exceptional value of the regional subordinate hospital.
Specifically, according to the disease abnormal deviation data examination method of embodiment one, the disease prison out of regional certain period
Exceptional value is found out in measured data (such as case load), from the disease surveillance data in subordinate hospital of this area same period
Exceptional value is found out, the intersection of two dimensions is taken to be used as final testing result.
For example, area is in 2014-3-3,2014-3-4,2014-3-5,2014-3-6 case loads are respectively 160,250,
170,180, subordinate hospital of this area (such as the disease adds up the most hospital of physician office visits under this area) is in this period
Case load is respectively 130,180,125,140.From the point of view of the distribution of regional case load, occur in this day 2014-3-4 abnormal sick
Number of cases, while subordinate hospital abnormal case load also occurs in this day, thus comprehensive judgement this day occur disease go to a doctor it is different
Normal phenomenon.
For another example, in 2014-3-3,2014-3-4,2014-3-5,2014-3-6 case loads are respectively 160,210 in area,
170,180, subordinate hospital of this area (such as the disease adds up the most hospital of physician office visits under this area) is in disease this period
Number of cases is respectively 130,140,125,140.From the point of view of the distribution of regional case load, in 2014-3-4, there is abnormal case in this day
Number, but there is not abnormal case load in this day in subordinate hospital.This is because there is very multiple hospitals under area, area is at this
One day medical case load is by the summation of the case load of all hospitals of subordinate, and there are one slightly in this day for Partial Hospitals
The growth of degree is not exception in hospital's dimension, but it is all increase by a small margin after summation in regional dimension with regard to table
It is now abnormal.
It is different that disease is carried out to area and regional subordinate hospital using the disease abnormal deviation data examination method of embodiment one respectively
Regular data detects, and final detection is obtained according to for regional testing result and for the testing result of regional subordinate hospital
As a result, more accurate disease anomaly data detection result can be obtained.
Embodiment two
Fig. 2 is the structure chart of disease anomaly data detection device provided by Embodiment 2 of the present invention.It is described as shown in Fig. 2
Disease anomaly data detection device 10 may include：Acquiring unit 201, computing unit 202, the first comparing unit 203.
Acquiring unit 201, time series data X, the X=[x for obtaining disease surveillance0,x1,x2,…,xt], wherein
x0,x1,x2,…,xtFor time point 0,1,2 ..., the disease surveillance data of t.
The disease surveillance data may include the monitoring of the diseases such as influenza, hand-foot-and-mouth disease, measles, mumps
Data.
The disease surveillance network being made of multiple monitoring points can be established in predeterminable area (such as provinces and cities, area), from institute
It states monitoring point and obtains disease surveillance data, the time series data of disease surveillance is made of the disease surveillance data.It can select
It selects medical institutions, school and mechanism of nursery schools and childcare centres, pharmacy etc. and is used as monitoring point, disease surveillance is carried out to corresponding target group respectively
And data acquisition.The place for meeting preset condition can be selected as monitoring point.The preset condition may include number, rule
Mould etc..For example, select number of student reach preset quantity school and mechanism of nursery schools and childcare centres as monitoring point.For another example, scale is selected
(such as being counted using daily sales) reaches the pharmacy of default scale as monitoring point.For another example, scale is selected (such as to see a doctor with day
Demographics) reach the hospital of default scale as monitoring point.
The disease surveillance data of different time constitute the time series data of disease surveillance.For example, can will be single with day
The collected disease surveillance data in position constitute the time series data of disease surveillance.Alternatively, can will be collected as unit of week
Disease surveillance data constitute disease surveillance time series data.
Medical institutions' (including mainly hospital) are the places that can most capture disease and break out omen in early days, are to carry out disease prison
The first choice of survey.Can go to a doctor situation according to patient, obtain disease surveillance data.
A part of disease people can voluntarily go pharmacy's purchase medicine to alleviate early symptom, therefore, can be according to the drug pin of pharmacy
Situation is sold, disease surveillance data are obtained.
The people at highest risk and the important link during transmission that Children and teenager is disease, should also reinforce
Monitoring to the crowd.School and mechanism of nursery schools and childcare centres are to monitor the preferable place of Children and teenager disease incidence situation.It can root
According to the situation of asking for leave of the Children and teenager of school and mechanism of nursery schools and childcare centres, disease surveillance data are obtained.
Therefore, medical institutions, school and mechanism of nursery schools and childcare centres, this three classes place of pharmacy is mainly selected to carry out disease in the present invention
The acquisition of monitoring data.Certainly, the above-mentioned selection to data source can not limit and increase in a further embodiment or replace
It changes other and pays close attention to the data source of crowd or place as monitoring.For example, hotel can be included in disease surveillance range, obtain
Hotel is taken to move in the disease surveillance data of personnel.
As needed, the disease surveillance data that any type monitoring point (such as medical institutions) acquires can be taken to constitute disease
The time series data of disease monitoring.For example, the time series of the disease surveillance data composition disease surveillance of hospital's acquisition can be taken
Data.Alternatively, the time series data of disease surveillance can be constituted in conjunction with the disease surveillance data of multiclass monitoring point acquisition.Example
Such as, using the disease surveillance data that pharmacy participates in as supplement, disease can be constituted based on the disease surveillance data of hospital's acquisition
The time series data of disease monitoring.
Disease surveillance data may include the medical number, consultation rate, the illness data such as number, incidence of falling ill of disease.Example
Such as, the daily medical number that disease (such as influenza) can be obtained from medical institutions (such as hospital), by disease (such as influenza)
Daily medical number is used as disease surveillance data.For another example, the daily morbidity of the disease (such as influenza) of student can be obtained from school
Number, using the daily morbidity number of disease (such as influenza) as disease surveillance data.
It should be noted that time point 0 and time point t indicate the initial time of time series data and terminate the time, and
It is non-to the temporal restriction of time series data.It can be using random time point as time point 0.
Computing unit 202 is used for access time window size w, for each time point i in time point w to time point t,
The the first quantile Q1 and the second quantile Q2 for calculating disease surveillance data in the corresponding time windows of the time point i, when described
Between the corresponding time windows of point i size be w, the first quantile Q1 be less than the second quantile Q2.
Time window a period of time neighbouring before being given point in time.For example, set time window size w as 4, given time
Point is t=10, and time window is exactly t=6 to t=9 this periods；Given point in time is t=11, and time window is exactly t=7 to t=
11 this periods；The rest may be inferred.
The value of time point i is the length because of the time window obtained in this period from t=0 to t=w-1 since w
Degree be less than w, w not enough disease surveillance data come calculate disease surveillance data the first quantile Q1 and second point of position
Number Q2.
Time window size w is adjustable parameter, can be adjusted according to actual conditions.
In one embodiment, the disease surveillance data are that (such as the daily of disease is gone to a doctor for daily disease surveillance data
Number), the time window size w can be taken as 7 (i.e. one weeks), thus calculate the disease surveillance data of given point in time the last week
The first quantile Q1 and the second quantile Q2.
In another embodiment, the disease surveillance data be weekly disease surveillance data (such as disease weekly
Examine number), the time window size w can be taken as 3 (i.e. three weeks), thus calculate 3 weeks before given point in time disease surveillance data
The first quantile Q1 and the second quantile Q2.
In one embodiment, the first quantile Q1 can be 0.25 quantile, and the second quantile Q2 can be
0.75 quantile.
In another embodiment, the first quantile Q1 can be 0.2 quantile, and the second quantile Q2 can be with
It is 0.7 quantile.
Computing unit 202 is additionally operable to calculate the difference D of the second quantile Q2 and the first quantile Q1.
For example, the second quantile Q2 is 3, the first quantile Q1 is 1, then the second quantile Q2 and institute
The difference D for stating the first quantile Q1 is 3-1=2.
For another example, the second quantile Q2 be 50, the first quantile Q1 be 30, then the second quantile Q2 with
The difference D of the first quantile Q1 is 50-30=20.
Computing unit 202 is additionally operable to calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound
T2, wherein the upper bound T1=Q2+k*D, the lower bound T2=Q1-k*D, k are adjustable parameter.
In one embodiment, the first quantile Q1 is 0.25 quantile, and the second quantile Q2 is 0.75 point of position
Number, corresponding k can be in [1.5,3] section value, such as value is 2.
First comparing unit 203 is used for the corresponding disease surveillance data x of time point iiWith the upper bound T1 and described
Lower bound T2 is compared, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound
T2, then corresponding disease surveillance data x of the time point iiFor exceptional value.
For example, 9 corresponding disease surveillance data x of time point9It is 5, corresponding upper bound T1 is 10, the corresponding lower bound T2
Be 7, then disease surveillance data x9For exceptional value.
For another example, 8 corresponding disease surveillance data x8 of time point is 6, and corresponding upper bound T1 is 9, and the corresponding lower bound T2 is
57, then disease surveillance data x8For non-exceptional value.
The disease anomaly data detection device of embodiment two obtains time series data X, the X=[x of disease surveillance0,x1,
x2,…,xt], wherein x0,x1,x2,…,xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；Access time window
Size w, for random time point i, i=w, w+1 ..., t calculates disease surveillance number in the corresponding time windows of the time point i
According to the first quantile Q1 and the second quantile Q2, the size of the corresponding time window of the time point i is w, first point of position
Number Q1 is less than the second quantile Q2；Calculate the difference D of the second quantile Q2 and the first quantile Q1；It calculates
The corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound T2, wherein the upper bound T1=Q2+k*D, institute
It is adjustable parameter to state lower bound T2=Q1-k*D, k；By the corresponding disease surveillance data x of time point iiWith the upper bound T1 and described
Lower bound T2 is compared, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound
T2, then corresponding disease surveillance data x of the time point iiFor exceptional value.
For time series data, more neighbouring data are to currently more having reference value, and reference value more remote is more
It is low.Embodiment two considers the characteristics of time series, by neighbouring historical data distribution as a reference to being carried out to current data
Abnormal judgement, can obtain preferable testing result.Also, embodiment two does not require data distribution, it is not required that is
Normal distribution can directly carry out abnormality detection the disease surveillance data in short-term, overcome and require data in time window
The limitation for meeting normal distribution has higher usability for the abnormality detection of disease surveillance data.Therefore, embodiment
Two realize the disease surveillance data exception detection of efficiently and accurately.
It in another embodiment, can be in conjunction with other disease anomaly data detection device (other disease abnormal datas
Detection device uses the disease abnormal deviation data examination method different from embodiment one) carry out disease anomaly data detection.Specifically
The disease anomaly data detection device on ground, embodiment two can also include：Detection unit is abnormal for obtaining other diseases
The exceptional value that data detection device is detected the time series data；Second comparing unit is used for embodiment
The exceptional value that two disease anomaly data detection device obtains obtains different with other disease anomaly data detection devices
Constant value is compared, the exceptional value and other diseases obtained according to the disease anomaly data detection device of embodiment two
The comparison result for the exceptional value that anomaly data detection device obtains obtains final exceptional value.
For example, the disease anomaly data detection device using embodiment two carries out the time series data of disease surveillance
Disease anomaly data detection obtains the first exceptional value, using other disease anomaly data detection devices to the disease surveillance
Time series data carry out disease anomaly data detection obtain the second exceptional value, the second comparing unit more described first is different
Constant value and described abnormal it is worth to final exceptional value to second.If first exceptional value and described to the second exceptional value one
It causes, then using first exceptional value/to the second exceptional value as final exceptional value；Otherwise, if first exceptional value and institute
It states inconsistent to the second exceptional value, then gives up first exceptional value and second exceptional value.
For another example, the time series data of disease surveillance is carried out using the disease anomaly data detection device of embodiment two
Disease anomaly data detection obtains the first exceptional value, using the second disease anomaly data detection device (the second disease exception number
According to detection device for example, by using Grubbs detection methods) disease exception number is carried out to the time series data of the disease surveillance
The second exceptional value is obtained according to detection, using third disease anomaly data detection device (third disease anomaly data detection device
For example, by using this detection method of leaf) it disease anomaly data detection is carried out to the time series data of the disease surveillance obtains the
Three exceptional values, the second comparing unit first exceptional value, second exceptional value and the third are worth to most extremely
Whole exceptional value.If at least two exceptional values in first exceptional value, second exceptional value and the third exceptional value
Unanimously, then using consistent exceptional value as final exceptional value.
On the basis of the disease anomaly data detection device of embodiment one, in conjunction with other disease anomaly data detections
Device carries out disease anomaly data detection, can obtain more accurate disease anomaly data detection result.
In another embodiment, the disease anomaly data detection device of embodiment two may be used respectively to area and ground
Subordinate hospital of area carries out disease anomaly data detection, obtains for the testing result (i.e. regional exceptional value) in area and for ground
The testing result (i.e. regional subordinate hospital exceptional value) of subordinate hospital of area.The disease anomaly data detection device further includes
Three comparing units, for the regional exceptional value and regional subordinate hospital exceptional value, according to the regional exceptional value
Final exceptional value is obtained with the comparison result of the regional subordinate hospital exceptional value.If for the testing result and needle in area
It is consistent to the testing result of regional subordinate hospital, then with the testing result for area/for the detection knot of regional subordinate hospital
Fruit is as final testing result；Otherwise, if the testing result for area and the testing result for regional subordinate hospital
It is inconsistent, then give up the testing result for area and the testing result for regional subordinate hospital.
Wherein, it is the time series number according to the disease surveillance in the area to carry out disease anomaly data detection to area
Regional exceptional value is obtained according to (such as the disease control department in area be collected into from each regional subordinate hospital disease surveillance data),
It is the time series according to the disease surveillance of the regional subordinate hospital to carry out disease anomaly data detection to regional subordinate hospital
Data obtain the exceptional value of the regional subordinate hospital.
Specifically, disease surveillance number of the disease anomaly data detection device of embodiment two out of regional certain period
According to exceptional value is found out in (such as case load), found out from the disease surveillance data in subordinate hospital of this area same period
Exceptional value takes the intersection of two dimensions to be used as final testing result.
For example, area is in 2014-3-3,2014-3-4,2014-3-5,2014-3-6 case loads are respectively 160,250,
170,180, subordinate hospital of this area (such as the disease adds up the most hospital of physician office visits under this area) is in this period
Case load is respectively 130,180,125,140.From the point of view of the distribution of regional case load, occur in this day 2014-3-4 abnormal sick
Number of cases, while subordinate hospital abnormal case load also occurs in this day, thus comprehensive judgement this day occur disease go to a doctor it is different
Normal phenomenon.
For another example, in 2014-3-3,2014-3-4,2014-3-5,2014-3-6 case loads are respectively 160,210 in area,
170,180, subordinate hospital of this area (such as the disease adds up the most hospital of physician office visits under this area) is in disease this period
Number of cases is respectively 130,140,125,140.From the point of view of the distribution of regional case load, in 2014-3-4, there is abnormal case in this day
Number, but there is not abnormal case load in this day in subordinate hospital.This is because there is very multiple hospitals under area, area is at this
One day medical case load is by the summation of the case load of all hospitals of subordinate, and there are one slightly in this day for Partial Hospitals
The growth of degree is not exception in hospital's dimension, but it is all increase by a small margin after summation in regional dimension with regard to table
It is now abnormal.
It is different that disease is carried out to area and regional subordinate hospital using the disease anomaly data detection device of embodiment two respectively
Regular data detects, and final detection is obtained according to for regional testing result and for the testing result of regional subordinate hospital
As a result, more accurate disease anomaly data detection result can be obtained.
Embodiment three
The present embodiment provides a kind of computer readable storage medium, calculating is stored on the computer readable storage medium
Machine program, the computer program realize the step in above-mentioned disease abnormal deviation data examination method embodiment when being executed by processor,
Such as step 101-105 shown in FIG. 1：
Step 101, time series data X, the X=[x of disease surveillance are obtained0,x1,x2,…,xt], wherein x0,x1,
x2,…,xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；
Step 102, access time window size w, for random time point i, i=w, w+1 ..., t calculates the time point
When first quantile Q1 of disease surveillance data and the second quantile Q2 in the corresponding time windows of i, the time point i are corresponding
Between window size be w, the first quantile Q1 be less than the second quantile Q2；
Step 103, the difference D of the second quantile Q2 and the first quantile Q1 are calculated；
Step 104, the corresponding disease surveillance data x of the time point i are calculatediUpper bound T1 and lower bound T2, wherein institute
Upper bound T1=Q2+k*D is stated, the lower bound T2=Q1-k*D, k are adjustable parameter；
Step 105, by the corresponding disease surveillance data x of time point iiCompared with the upper bound T1 and the lower bound T2
Compared with if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound T2, then the time
The corresponding disease surveillance data x of point iiFor exceptional value.
Alternatively, realizing the work(of each module/unit in above-mentioned apparatus embodiment when the computer program is executed by processor
Can, such as the unit 201-203 in Fig. 2：
Acquiring unit 201, time series data X, the X=[x for obtaining disease surveillance0,x1,x2,…,xt], wherein
x0,x1,x2,…,xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；
Computing unit 202 is used for access time window size w, and for random time point i, i=w, w+ 1 ..., t calculates institute
State the first quantile Q1 and the second quantile Q2, the time point i of disease surveillance data in the corresponding time windows of time point i
The size of corresponding time window is w, and the first quantile Q1 is less than the second quantile Q2；
Computing unit 202 is additionally operable to calculate the difference D of the second quantile Q2 and the first quantile Q1；
Computing unit 202 is additionally operable to calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound
T2, wherein the upper bound T1=Q2+k*D, the lower bound T2=Q1-k*D, k are adjustable parameter；
First comparing unit 203 is used for the corresponding disease surveillance data x of time point iiWith the upper bound T1 and described
Lower bound T2 is compared, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound
T2, then corresponding disease surveillance data x of the time point iiFor exceptional value.
Example IV
Fig. 3 is the schematic diagram for the computer installation that the embodiment of the present invention four provides.The computer installation 1 includes storage
Device 20, processor 30 and it is stored in the computer program 40 that can be run in the memory 20 and on the processor 30,
Such as disease anomaly data detection program.The processor 30 realizes that above-mentioned disease is abnormal when executing the computer program 40
Step in data detection method embodiment, such as step 101-105 shown in FIG. 1：
Step 101, time series data X, the X=[x of disease surveillance are obtained0,x1,x2,…,xt], wherein x0,x1,
x2,…,xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；
Step 102, access time window size w, for random time point i, i=w, w+1 ..., t calculates the time point
When first quantile Q1 of disease surveillance data and the second quantile Q2 in the corresponding time windows of i, the time point i are corresponding
Between window size be w, the first quantile Q1 be less than the second quantile Q2；
Step 103, the difference D of the second quantile Q2 and the first quantile Q1 are calculated；
Step 104, the corresponding disease surveillance data x of the time point i are calculatediUpper bound T1 and lower bound T2, wherein institute
Upper bound T1=Q2+k*D is stated, the lower bound T2=Q1-k*D, k are adjustable parameter；
Step 105, by the corresponding disease surveillance data x of time point iiCompared with the upper bound T1 and the lower bound T2
Compared with if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound T2, then the time
The corresponding disease surveillance data x of point iiFor exceptional value.
Alternatively, realizing each module in above-mentioned apparatus embodiment/mono- when the processor 30 executes the computer program 40
The function of member, such as the unit 201-203 in Fig. 2：
Acquiring unit 201, time series data X, the X=[x for obtaining disease surveillance0,x1,x2,…,xt], wherein
x0,x1,x2,…,xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；
Computing unit 202 is used for access time window size w, and for random time point i, i=w, w+ 1 ..., t calculates institute
State the first quantile Q1 and the second quantile Q2, the time point i of disease surveillance data in the corresponding time windows of time point i
The size of corresponding time window is w, and the first quantile Q1 is less than the second quantile Q2；
Computing unit 202 is additionally operable to calculate the difference D of the second quantile Q2 and the first quantile Q1；
Computing unit 202 is additionally operable to calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound
T2, wherein the upper bound T1=Q2+k*D, the lower bound T2=Q1-k*D, k are adjustable parameter；
First comparing unit 203 is used for the corresponding disease surveillance data x of time point iiWith the upper bound T1 and described
Lower bound T2 is compared, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound
T2, then corresponding disease surveillance data x of the time point iiFor exceptional value.
Illustratively, the computer program 40 can be divided into one or more module/units, it is one or
Multiple module/the units of person are stored in the memory 20, and are executed by the processor 30, to complete the present invention.It is described
One or more module/units can be the series of computation machine program instruction section that can complete specific function, which uses
In describing implementation procedure of the computer program 40 in the computer installation 1.For example, the computer program 40 can
Be divided into Fig. 2 acquiring unit 201, computing unit 202, the first comparing unit 203, each unit concrete function referring to
Embodiment two.
The computer installation 1 can be the calculating such as desktop PC, notebook, palm PC and cloud server
Equipment.It will be understood by those skilled in the art that the schematic diagram 3 is only the example of computer installation 1, do not constitute to meter
The restriction of calculation machine device 1 may include either combining certain components or different portions than illustrating more or fewer components
Part, such as the computer installation 1 can also include input-output equipment, network access equipment, bus etc..
Alleged processor 30 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), application-specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic device
Part, discrete hardware components etc..General processor can be microprocessor or the processor 30 can also be any conventional place
Device etc. is managed, the processor 30 is the control centre of the computer installation 1, is entirely counted using various interfaces and connection
The various pieces of calculation machine device 1.
The memory 20 can be used for storing the computer program 40 and/or module/unit, and the processor 30 is logical
It crosses operation or executes the computer program and/or module/unit being stored in the memory 20, and call and be stored in
Data in reservoir 20 realize the various functions of the computer installation 1.The memory 20 can include mainly storage program
Area and storage data field, wherein storing program area can storage program area, needed at least one function application program (such as
Sound-playing function, image player function etc.) etc.；Storage data field can be stored to be created according to using for computer installation 1
Data (such as audio data, phone directory etc.) etc..In addition, memory 20 may include high-speed random access memory, may be used also
To include nonvolatile memory, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card,
SMC), secure digital (Secure Digital, SD) block, flash card (Flash Card), at least one disk memory,
Flush memory device or other volatile solid-state parts.
If the integrated module/unit of the computer installation 1 is realized in the form of SFU software functional unit and as independence
Product sale or in use, can be stored in a computer read/write memory medium.Based on this understanding, this hair
All or part of flow in bright realization above-described embodiment method can also instruct relevant hardware by computer program
It completes, the computer program can be stored in a computer readable storage medium, the computer program is by processor
When execution, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the computer program includes computer program code,
The computer program code can be source code form, object identification code form, executable file or certain intermediate forms etc..
The computer-readable medium may include：Any entity or device, record that the computer program code can be carried are situated between
It is matter, USB flash disk, mobile hard disk, magnetic disc, CD, computer storage, read-only memory (ROM, Read-Only Memory), random
Access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It needs
It is noted that the content that the computer-readable medium includes can be according to legislation and patent practice in jurisdiction
It is required that increase and decrease appropriate is carried out, such as in certain jurisdictions, according to legislation and patent practice, computer-readable medium is not
Including electric carrier signal and telecommunication signal.
In several embodiments provided by the present invention, it should be understood that disclosed computer installation and method, it can
To realize by another way.For example, computer installation embodiment described above is only schematical, for example, institute
The division of unit is stated, only a kind of division of logic function, formula that in actual implementation, there may be another division manner.
In addition, each functional unit in each embodiment of the present invention can be integrated in same treatment unit, it can also
That each unit physically exists alone, can also two or more units be integrated in same unit.Above-mentioned integrated list
The form that hardware had both may be used in member is realized, can also be realized in the form of hardware adds software function module.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, nothing
By from the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by institute
Attached claim rather than above description limit, it is intended that will fall within the meaning and scope of the equivalent requirements of the claims
All changes include within the present invention.Any reference numeral in claim should not be considered as to the involved right of limitation
It is required that.Furthermore, it is to be understood that one word of " comprising " is not excluded for other units or step, odd number is not excluded for plural number.Computer installation right is wanted
The multiple units or computer installation for asking middle statement can also be by the same units or computer installation by software or firmly
Part is realized.The first, the second equal words are used to indicate names, and are not represented any particular order.
Finally it should be noted that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although reference
Preferred embodiment describes the invention in detail, it will be understood by those of ordinary skill in the art that, it can be to the present invention's
Technical solution is modified or equivalent replacement, without departing from the spirit of the technical scheme of the invention and range.
Claims (10)
1. a kind of disease abnormal deviation data examination method, which is characterized in that the method includes：
Obtain time series data X, the X=[x of disease surveillance0,x1,x2,…,xt], wherein x0,x1,x2,…,xtWhen to correspond to
Between put 0,1,2 ..., the disease surveillance data of t；
Access time window size w, for random time point i, i=w, w+1 ..., t calculates the corresponding time windows of the time point i
The size of the corresponding time window of first quantile Q1 of interior disease surveillance data and the second quantile Q2, the time point i is w,
The first quantile Q1 is less than the second quantile Q2；
Calculate the difference D of the second quantile Q2 and the first quantile Q1；
Calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound T2, wherein the upper bound T1=Q2+k*
D, the lower bound T2=Q1-k*D, k are adjustable parameter；
By the corresponding disease surveillance data x of time point iiIt is compared with the upper bound T1 and the lower bound T2, if i pairs of time point
The disease surveillance data x answerediMore than the upper bound T1 or it is less than the lower bound T2, then the corresponding disease prisons of the time point i
Measured data xiFor exceptional value.
2. the method as described in claim 1, which is characterized in that the first quantile Q1 is 0.25 quantile, described second
Quantile Q2 is 0.75 quantile, and the k is in [1.5,3] section value.
3. the method as described in claim 1, which is characterized in that the method further includes：
Obtain the exceptional value that other disease abnormal deviation data examination methods are detected the time series data；
The exceptional value that the disease abnormal deviation data examination method obtains is obtained with other disease abnormal deviation data examination methods
To exceptional value be compared；
The exceptional value obtained according to the disease abnormal deviation data examination method and other disease abnormal deviation data examination methods
The comparison result of obtained exceptional value obtains final exceptional value.
4. the method as described in claim 1, which is characterized in that the method further includes：
Disease anomaly data detection is carried out to area and regional subordinate hospital respectively, obtains the regional exceptional value and needle for area
To the regional subordinate hospital exceptional value of regional subordinate hospital；
Compare the regional exceptional value and regional subordinate hospital exceptional value, according under the regional exceptional value and the area
The comparison result for belonging to hospital's exceptional value obtains final exceptional value.
5. the method as described in any one of claim 1-4, which is characterized in that the disease surveillance data include disease just
Examine number, consultation rate, morbidity number, incidence.
6. the method as described in any one of claim 1-4, which is characterized in that the time series number for obtaining disease surveillance
According to including：
The disease surveillance network being made of multiple monitoring points is established in predeterminable area, and disease surveillance number is obtained from the monitoring point
According to constituting the time series data by the disease surveillance data.
7. method as claimed in claim 6, which is characterized in that the monitoring point includes the medical treatment for meeting the number of presetting or scale
Mechanism, school and mechanism of nursery schools and childcare centres, pharmacy.
8. a kind of disease anomaly data detection device, which is characterized in that described device includes：
Acquiring unit, time series data X, the X=[x for obtaining disease surveillance0,x1,x2,…,xt], wherein x0,x1,
x2,…,xtTo correspond to time point 0,1,2 ..., the disease surveillance data of t；
Computing unit is used for access time window size w, and for random time point i, i=w, w+1 ..., t calculates the time point
First quantile Q1 of disease surveillance data and the second quantile Q2 in the corresponding time windows of i, the time point the i corresponding time
The size of window is w, and the first quantile Q1 is less than the second quantile Q2；
The computing unit is additionally operable to calculate the difference D of the second quantile Q2 and the first quantile Q1；
The computing unit is additionally operable to calculate the corresponding disease surveillance data x of the time point iiUpper bound T1 and lower bound T2,
In, the upper bound T1=Q2+k*D, the lower bound T2=Q1-k*D, k are adjustable parameter；
First comparing unit is used for the corresponding disease surveillance data x of time point iiIt is carried out with the upper bound T1 and lower bound T2
Compare, if the corresponding disease surveillance data x of time point iiMore than the upper bound T1 or it is less than the lower bound T2, then the time
The corresponding disease surveillance data x of point iiFor exceptional value.
9. a kind of computer installation, it is characterised in that：The computer installation includes processor, and the processor is deposited for executing
The computer program stored in reservoir is to realize the disease abnormal deviation data examination method as described in any one of claim 1-7.
10. a kind of computer readable storage medium, computer program, feature are stored on the computer readable storage medium
It is：Realize that disease abnormal data is examined as described in any one of claim 1-7 when the computer program is executed by processor
Survey method.
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| CN201810321079.6A CN108320810A (en) | 2018-04-11 | 2018-04-11 | Disease abnormal deviation data examination method and device, computer installation and storage medium |
| PCT/CN2018/099653 WO2019196282A1 (en) | 2018-04-11 | 2018-08-09 | Method and device for detecting abnormal data of diseases, computer device, and storage medium |
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| WO2019196282A1 (en) * | 2018-04-11 | 2019-10-17 | 平安科技（深圳）有限公司 | Method and device for detecting abnormal data of diseases, computer device, and storage medium |
| CN110851503A (en) * | 2019-11-19 | 2020-02-28 | 天津开心生活科技有限公司 | Medical mode conversion identification method and device, electronic equipment and storage medium |
| CN111367747A (en) * | 2018-12-25 | 2020-07-03 | 中国移动通信集团浙江有限公司 | Index abnormal change detection early warning device based on time labeling |
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