CN109065168A - A method of disease risks assessment is carried out based on space-time class statistic - Google Patents
A method of disease risks assessment is carried out based on space-time class statistic Download PDFInfo
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- CN109065168A CN109065168A CN201810995255.4A CN201810995255A CN109065168A CN 109065168 A CN109065168 A CN 109065168A CN 201810995255 A CN201810995255 A CN 201810995255A CN 109065168 A CN109065168 A CN 109065168A
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- 201000010099 disease Diseases 0.000 title claims abstract description 42
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000015271 coagulation Effects 0.000 claims abstract description 10
- 238000005345 coagulation Methods 0.000 claims abstract description 10
- 230000000694 effects Effects 0.000 claims description 20
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 230000008080 stochastic effect Effects 0.000 claims description 3
- 230000001550 time effect Effects 0.000 claims description 3
- 238000012502 risk assessment Methods 0.000 claims description 2
- 235000006629 Prosopis spicigera Nutrition 0.000 description 2
- 240000000037 Prosopis spicigera Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 229940086226 cold spot Drugs 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
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- 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
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Abstract
The invention discloses a kind of methods for carrying out disease risks assessment based on space-time class statistic, belong to spatio-temporal event clustering method field.The present invention collects disease data first and generates disease database；Then according to data in database, the data to region each in database and in the time cycle add auxiliary data；It is handled again by space-time coagulation type clustering algorithm and mixing Poisson log-linear model；Risk is judged finally by decision rule.The present invention is compared with prior art, the present invention is handled data using space-time coagulation type clustering algorithm and mixing Poisson log-linear model, and processing result is judged by decision rule, influence of the excess smoothness to estimated result for reducing disease risks figure, improves the accuracy of estimated result.
Description
Technical field
The present invention relates to a kind of methods for carrying out disease risks assessment based on space-time class statistic, belong to spatio-temporal event cluster
Analysis method field.
Background technique
In today's society, the inspection and its prevention of various diseases, space-time class statistic method is often used as all kinds of diseases
In the early stage risk assessment of disease outburst, many researchers are excavated in the data of magnanimity by this method is hidden in data behind
Relevance the disease risks figure of area unit data, the risk are obtained by the interpretation for relevance between these data
Figure be usually by estimating with the smooth Poisson mixed model of local space, however, the model has a defect that, office
Portion's discontinuity point is not modeled usually, and heat or cold-spot area cluster are shielded, and is led to the excess smoothness of disease risks figure, is caused pre-
The decline of alert accuracy.
Summary of the invention
For overcome the deficiencies in the prior art, it is an object of the invention to one kind carries out disease wind based on space-time class statistic
The method nearly assessed.The present invention is predominantly to improve space-time in the result accuracy rate for promoting Disease Warning Mechanism space-time Statistical Clustering Analysis
The accuracy of Statistical Clustering Analysis result, using space-time coagulation type clustering algorithm and mixed Poisson log-linear model to disease number
According to processing is carried out to promote the accuracy of disease risks assessment.
The technical solution adopted by the present invention is that: a method of disease risks assessment, packet are carried out based on space-time class statistic
Include following steps:
Step1: it collects disease data and generates disease database；
Step2: data in database are obtained, and add auxiliary to each region in database and the data in the time cycle
Data；
Step3: it to data treated in Step2, is handled using space-time coagulation type clustering algorithm；
Step4: it to data processed in Step3, is analyzed using mixing Poisson log-linear model；
Step5: risk is judged using decision rule to the analysis result in Step4；
Step6: disease risks are estimated according to the differentiation result in Step5.
Further, in the step Step2, the auxiliary data of addition is from the disease with similar space-time Risk mode
Disease.
Further, in the step Step3, the specific implementation step of space-time coagulation type clustering algorithm is:
S1: one initial clustering configuration of construction, Ch={ Ch(1),…,Ch(nT) }, wherein h=nT, each zone time section
AitIt is a kind of individual space-time cluster.
S2: the distance between cluster in h × h matrix is calculated, elements A is included at least in clusteritAnd AjsIn a secondary member
Element, AitAnd AjsIt is spatial neighbor area at same time (i~jandt=s), or areal adjacent time point (i~jand |
T-s |=1) element；
S3: two space-time units with minimum range cluster are merged, a new cluster structure C is formedh-1；
S4: repeat S2 and S3 step；
S5: when all space time units are incorporated in a new space-time cluster, algorithm terminates；
Further, in the step Step4, the existing fixed cluster effect of mixing Poisson log-linear model, also have with
Machine clusters effect, according to given space-time cluster configuration Ck={ Ck(1),…,Ck(k) } it, is selected based on model selection criteria best
Cluster structure.
Further, the fixed cluster effect are as follows:
Oit|rit~Poisson (Eitrit) fori=1 ..., n；T=1 ..., T,
Wherein ξiAnd γtIt is room and time structure stochastic effects, β1,…,βkIt is the fixation with each space-time cluster correlation
Effect, LCAR priori will assume as three-dimensional effect ξ, and RW1 priori will assume that for time effect γ, N (0,10) priori is fixed effect
Answer βjs。
Further, the stochastic clustering effect are as follows:
log(rit)=α+ξi+γt+δj(it)
Wherein, subindex j (it) indicates cluster Ck(j) region-chronomere AitAffiliated region.
Further, in the step Step5, decision rule are as follows: if posterior probability is greater than 0.95 (P (δj(it)> 0 | O)
> 0.95), space-time cluster is considered as a high risk cluster；If posterior probability is less than 0.05 (P (δj(it)> 0 | O) <
0.05), then space-time cluster is regarded as a low-risk cluster.
The beneficial effects of the present invention are: the present invention proposes a kind of side for carrying out disease risks assessment based on space-time class statistic
Method.Disease data handle being promoted using space-time coagulation type clustering algorithm and mixed Poisson log-linear model
The accuracy of disease risks assessment.
Detailed description of the invention
Fig. 1 is flow diagram of the present invention.
Specific embodiment
With reference to the accompanying drawings and detailed description, the present invention is further illustrated
Embodiment 1: as shown in Figure 1, a kind of method that disease risks assessment is carried out based on space-time class statistic, including it is as follows
Step:
Step1: it collects disease data and generates disease database；
Step2: data in database are obtained, and add auxiliary to each region in database and the data in the time cycle
Data；
Step3: it to data treated in Step2, is handled using space-time coagulation type clustering algorithm；
Step4: it to data processed in Step3, is analyzed using mixing Poisson log-linear model；
Step5: risk is judged using decision rule to the analysis result in Step4；
Step6: disease risks are estimated according to the differentiation result in Step5.
Further, in the step Step2, the auxiliary data of addition is from the disease with similar space-time Risk mode
Disease.
Further, in the step Step3, the specific implementation step of space-time coagulation type clustering algorithm is:
S1: one initial clustering configuration of construction, Ch={ Ch(1),…,Ch(nT) }, wherein h=nT, each zone time section
AitIt is a kind of individual space-time cluster；
S2: the distance between cluster in h × h matrix is calculated, elements A is included at least in clusteritAnd AjsIn a secondary member
Element, AitAnd AjsIt is spatial neighbor area at same time (i~jandt=s), or areal adjacent time point (i~jand |
T-s |=1) element；
S3: two space-time units with minimum range cluster are merged, a new cluster structure C is formedh-1；
S4: repeat S2 and S3 step；
S5: when all space time units are incorporated in a new space-time cluster, algorithm terminates.
Further, in the step Step4, the existing fixed cluster effect of mixing Poisson log-linear model, also have with
Machine clusters effect, according to given space-time cluster configuration Ck={ Ck(1),…,Ck(k) } it, is selected based on model selection criteria best
Cluster structure.
Further, the fixed cluster effect are as follows:
Oit|rit~Poisson (Eitrit) fori=1 ..., n；T=1 ..., T,
Wherein ξiAnd γtIt is room and time structure stochastic effects, β1,…,βkIt is the fixation with each space-time cluster correlation
Effect, LCAR priori will assume as three-dimensional effect ξ, and RW1 priori will assume that for time effect γ, N (0,10) priori is fixed effect
Answer βjs。
Further, the stochastic clustering effect are as follows:
log(rit)=α+ξi+γt+δj(it)
Wherein, subindex j (it) indicates cluster Ck(j) region-chronomere AitAffiliated region.
Further, in the step Step5, decision rule are as follows: if posterior probability is greater than 0.95 (P (δj(it)> 0 | O)
> 0.95), space-time cluster is considered as a high risk cluster；If posterior probability is less than 0.05 (P (δj(it)> 0 | O) <
0.05), then space-time cluster is regarded as a low-risk cluster
In conjunction with attached drawing, the embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (7)
1. a kind of method for carrying out disease risks assessment based on space-time class statistic, characterized by the following steps:
Step1: it collects disease data and generates disease database；
Step2: data in database are obtained, and add supplementary number to each region in database and the data in the time cycle
According to；
Step3: it to data treated in Step2, is handled using space-time coagulation type clustering algorithm；
Step4: it to data processed in Step3, is analyzed using mixing Poisson log-linear model；
Step5: risk is judged using decision rule to the analysis result in Step4；
Step6: disease risks are estimated according to the differentiation result in Step5.
2. a kind of method for carrying out disease risks assessment based on space-time class statistic according to claim 1, feature
Be: in the step Step2, the auxiliary data of addition is from the disease with similar space-time Risk mode.
3. a kind of method for carrying out disease risks assessment based on space-time class statistic according to claim 1, feature
Be: in the step Step3, the specific implementation step of space-time coagulation type clustering algorithm is:
S1: one initial clustering configuration of construction, Ch={ Ch(1) ..., Ch(nT) }, wherein h=nT, each zone time section Ait
It is a kind of individual space-time cluster；
S2: the distance between cluster in h × h matrix is calculated, elements A is included at least in clusteritAnd AjsIn an accessory element, Ait
And AjsSpatial neighbor area same time (i~j and t=s) or areal adjacent time point (i~j and | t-s
|=1) element；
S3: two space-time units with minimum range cluster are merged, a new cluster structure C is formedh-1；
S4: repeat S2 and S3 step；
S5: when all space time units are incorporated in a new space-time cluster, algorithm terminates.
4. a kind of method for carrying out disease risks assessment based on space-time class statistic according to claim 1, feature
Be: in the step Step4, the existing fixed cluster effect of mixing Poisson log-linear model also has stochastic clustering effect, root
According to given space-time cluster configuration Ck={ Ck(1) ..., Ck(k) } best cluster structure, is selected based on model selection criteria.
5. a kind of method for carrying out disease risks assessment based on space-time class statistic according to claim 4, feature
It is: the fixed cluster effect are as follows:
Oit|rit~Poisson (Eitrit) for i=1 ..., n；T=1 ..., T,
Wherein ξiAnd γtIt is room and time structure stochastic effects, β1..., βkIt is to be imitated with the fixed of each space-time cluster correlation
It answers, LCAR priori will assume as three-dimensional effect ξ, and RW1 priori will assume that for time effect γ, N (0,10) priori is fixed effect
βjs。
6. a kind of method for carrying out disease risks assessment based on space-time class statistic according to claim 4, feature
It is: the stochastic clustering effect are as follows:
log(rit)=α+ξi+γt+δj(it)
Wherein, subindex j (it) indicates cluster Ck(j) zone-time unit AitAffiliated region.
7. according to claim 1 according to claim 4 a kind of based on space-time class statistic progress disease
The method of risk assessment, it is characterised in that: in the step Step5, decision rule are as follows: if posterior probability is greater than 0.95 (P
(δj(it)> 0 | O) > 0.95), space-time cluster is considered as a high risk cluster；If posterior probability is less than 0.05 (P
(6j(it)> 0 | O) < 0.05), then space-time cluster is regarded as a low-risk cluster.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112331342A (en) * | 2020-10-27 | 2021-02-05 | 昆明理工大学 | Disease risk grade evaluation method based on gridding covariate factors |
| CN113626670A (en) * | 2021-07-13 | 2021-11-09 | 北京格灵深瞳信息技术股份有限公司 | Object clustering method and device based on time-space relationship and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930163A (en) * | 2012-11-01 | 2013-02-13 | 北京理工大学 | Method for judging 2 type diabetes mellitus risk state |
| US20170319278A1 (en) * | 2014-11-14 | 2017-11-09 | The Johns Hopkins University | Systems and methods for atrial fibrillation treatment and risk assessment |
| CN107767960A (en) * | 2017-09-13 | 2018-03-06 | 温州悦康信息技术有限公司 | Data processing method, device and the electronic equipment of clinical detection project |
| CN108064263A (en) * | 2014-09-30 | 2018-05-22 | 深圳华大基因研究院 | Biomarker for rheumatoid arthritis and application thereof |
| CN108095685A (en) * | 2016-11-23 | 2018-06-01 | 中国科学院昆明动物研究所 | Application of the positive negative action ratio in assessment health and medical diagnosis on disease in human microorganism's interaction network |
-
2018
- 2018-08-29 CN CN201810995255.4A patent/CN109065168B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930163A (en) * | 2012-11-01 | 2013-02-13 | 北京理工大学 | Method for judging 2 type diabetes mellitus risk state |
| CN108064263A (en) * | 2014-09-30 | 2018-05-22 | 深圳华大基因研究院 | Biomarker for rheumatoid arthritis and application thereof |
| US20170319278A1 (en) * | 2014-11-14 | 2017-11-09 | The Johns Hopkins University | Systems and methods for atrial fibrillation treatment and risk assessment |
| CN108095685A (en) * | 2016-11-23 | 2018-06-01 | 中国科学院昆明动物研究所 | Application of the positive negative action ratio in assessment health and medical diagnosis on disease in human microorganism's interaction network |
| CN107767960A (en) * | 2017-09-13 | 2018-03-06 | 温州悦康信息技术有限公司 | Data processing method, device and the electronic equipment of clinical detection project |
Non-Patent Citations (2)
| Title |
|---|
| RACHEL M.FEWSTER ET AL.: "analysis or population trends for farmland birds using generalized additive models", 《ECOLOGICAL SOCIETY OF AMERICA》 * |
| 刁玉涛 等: "APC泊松对数线性模型及其在肿瘤流行病研究中的应用", 《现代预防医学》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112331342A (en) * | 2020-10-27 | 2021-02-05 | 昆明理工大学 | Disease risk grade evaluation method based on gridding covariate factors |
| CN113626670A (en) * | 2021-07-13 | 2021-11-09 | 北京格灵深瞳信息技术股份有限公司 | Object clustering method and device based on time-space relationship and electronic equipment |
| CN113626670B (en) * | 2021-07-13 | 2023-01-24 | 北京格灵深瞳信息技术股份有限公司 | Object clustering method and device based on time-space relationship and electronic equipment |
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