CN111863276B - Hand-foot-and-mouth disease prediction method using fine-grained data, electronic device, and medium - Google Patents
Hand-foot-and-mouth disease prediction method using fine-grained data, electronic device, and medium Download PDFInfo
- Publication number
- CN111863276B CN111863276B CN202010704454.2A CN202010704454A CN111863276B CN 111863276 B CN111863276 B CN 111863276B CN 202010704454 A CN202010704454 A CN 202010704454A CN 111863276 B CN111863276 B CN 111863276B
- Authority
- CN
- China
- Prior art keywords
- data
- time
- granularity
- fine
- equal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 208000020061 Hand, Foot and Mouth Disease Diseases 0.000 title claims abstract description 98
- 208000025713 Hand-foot-and-mouth disease Diseases 0.000 title claims abstract description 98
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 238000012549 training Methods 0.000 claims abstract description 13
- 230000002776 aggregation Effects 0.000 claims abstract description 10
- 238000004220 aggregation Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims description 45
- 230000015654 memory Effects 0.000 claims description 20
- 238000010606 normalization Methods 0.000 claims description 15
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 201000010099 disease Diseases 0.000 claims description 10
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 230000000875 corresponding effect Effects 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 230000002596 correlated effect Effects 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 238000005728 strengthening Methods 0.000 claims description 3
- 230000002441 reversible effect Effects 0.000 claims description 2
- 230000002708 enhancing effect Effects 0.000 claims 2
- 230000009466 transformation Effects 0.000 claims 1
- 235000019580 granularity Nutrition 0.000 description 10
- 230000005180 public health Effects 0.000 description 6
- 238000003860 storage Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000004931 aggregating effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 208000035473 Communicable disease Diseases 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000006806 disease prevention Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000003313 weakening effect Effects 0.000 description 1
Images
Classifications
-
- 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
-
- 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
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- 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
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a hand-foot-and-mouth disease prediction method and device using fine-grained data, electronic equipment and a computer readable medium. The method comprises the following steps: acquiring historical case data of the hand-foot-and-mouth disease; preprocessing historical case data, and counting the historical case data into time sequence data of two different time intervals; according to time aggregation, the time sequence data of the two different time intervals are obtained, multivariate time sequence data are obtained, and the multivariate time sequence data are converted into supervised data; training a hand-foot-and-mouth disease prediction model according to supervised data; and inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the number of the patients with the hand-foot-and-mouth disease predicted in real time. According to the scheme, the time sequence data of historical case data at different granularity time intervals are counted, the accuracy of predicting the number of the hand-foot-and-mouth disease patients based on equal time granularity is improved by combining the time sequence data with finer granularity, extra data assistance is not needed, and the prediction efficiency is improved.
Description
Technical Field
The application relates to the technical field of public health prediction, in particular to a hand-foot-and-mouth disease prediction method and device using fine-grained data, electronic equipment and a computer readable medium.
Background
With the acceleration of the global economy integration process, the economy and communication activities are increased, the crowd moves more and more frequently, a favorable environment is provided for the transmission and outbreak of diseases, and the public health problem is more and more severe. Meanwhile, society and natural environment change, and the possibility of outbreak of public health incidents is increased due to the increase of public health incidents such as environmental pollution and natural disasters.
The hand-foot-and-mouth disease is one of infectious diseases with the highest incidence rate, and affects public health safety worldwide. Early prediction plays an important role in early warning and decision support for prevention and treatment of infectious diseases. In addition, the management of hand-foot-and-mouth disease is also a concern for governments, medical institutions, and the general public.
The traditional method is based on the observed number of the people with the same time interval when predicting the number of the people with the hand-foot-and-mouth disease in a certain time interval in the future, for example, the number of the people with the same period in the past years is used for predicting the number of the people with the same period in the present year, but the prediction of the traditional method is not good enough.
Disclosure of Invention
An object of the present application is to provide a hand-foot-and-mouth disease prediction method and apparatus using fine-grained data, an electronic device, and a computer-readable medium.
The application provides a hand-foot-and-mouth disease prediction method using fine-grained data, which comprises the following steps:
s1, acquiring historical case data of hand-foot-and-mouth disease;
s2, preprocessing the historical case data, and counting the historical case data into time sequence data of two different time intervals;
s3, aggregating the time sequence data of the two different time intervals according to time to obtain multivariate time sequence data, and converting the multivariate time sequence data into supervised data;
s4, training a hand-foot-and-mouth disease prediction model according to the supervised data;
and S5, inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the number of the disease patients of the hand-foot-and-mouth disease predicted in real time.
A second aspect of the present application provides a hand-foot-and-mouth disease prediction apparatus using fine-grained data, including:
the acquisition module is used for acquiring historical case data of the hand-foot-and-mouth disease;
the preprocessing module is used for preprocessing the historical case data and counting the historical case data into two kinds of time sequence data with different time intervals;
the aggregation module is used for aggregating the time sequence data of the two different time intervals according to time to obtain multivariate time sequence data and converting the multivariate time sequence data into supervised data;
the model training module is used for training a hand-foot-and-mouth disease prediction model according to the supervised data;
and the prediction module is used for inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the number of the real-time predicted hand-foot-and-mouth disease patients.
A third aspect of the present application provides an electronic device comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program when executing the computer program to perform the method of the first aspect of the application.
A fourth aspect of the present application provides a computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of the first aspect of the present application.
Compared with the prior art, the hand-foot-and-mouth disease prediction method, the hand-foot-and-mouth disease prediction device, the electronic equipment and the medium which utilize fine-grained data obtain historical case data of the hand-foot-and-mouth disease; preprocessing the historical case data, and counting the historical case data into time sequence data of two different time intervals; aggregating the time sequence data of the two different time intervals according to time to obtain multivariate time sequence data, and converting the multivariate time sequence data into supervised data; training a hand-foot-and-mouth disease prediction model according to the supervised data; and inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the number of the patients with the hand-foot-and-mouth disease predicted in real time. According to the scheme, the time sequence data of historical case data with different granularity time intervals are counted to train the prediction model, the accuracy of predicting the number of the hand-foot-and-mouth disease patients based on equal time granularity is improved through the prediction model and by combining with the time sequence data with finer granularity, extra data assistance is not needed, and the prediction efficiency is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a flow diagram of a hand-foot-and-mouth disease prediction method utilizing fine-grained data provided by some embodiments of the present application;
FIG. 2 illustrates a flow chart of a particular hand-foot-and-mouth disease prediction method utilizing fine-grained data provided by some embodiments of the present application;
FIG. 3 illustrates a schematic diagram of a hand-foot-and-mouth disease prediction apparatus utilizing fine-grained data provided by some embodiments of the present application;
fig. 4 illustrates a schematic diagram of an electronic device provided by some embodiments of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be 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 scope of the disclosure to those skilled in the art.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.
In addition, the terms "first" and "second", etc. are used to distinguish different objects, rather than to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Due to the fact that latent periods exist in diseases, most of the existing hand-foot-and-mouth disease prediction models cannot accurately predict the outbreak periods of the hand-foot-and-mouth diseases, and therefore the potential outbreak number can be mined from fine-grained data.
Therefore, historical disease cases of the hand-foot-and-mouth disease are collected, time intervals equal to the predicted target and time intervals finer than the predicted target are set, time sequences of the number of the disease people with two time granularities as units are counted and obtained, the time sequences of two different time intervals are aggregated, time sequence data are converted into supervised data, the supervised data are used for training a time sequence neural network fusing the two data, and finally the trained model is used for providing more accurate prediction than the time sequences only using and predicting the target and the like.
Specifically, the embodiments of the present application provide a hand-foot-and-mouth disease prediction method and apparatus using fine-grained data, an electronic device, and a computer readable medium, which are described below with reference to the accompanying drawings.
Referring to fig. 1, which illustrates a flowchart of a hand-foot-and-mouth disease prediction method using fine-grained data according to some embodiments of the present application, as shown in the figure, the hand-foot-and-mouth disease prediction method using fine-grained data may include the following steps:
step S101: acquiring historical case data of the hand-foot-and-mouth disease;
step S102: preprocessing the historical case data, and counting the historical case data into time sequence data of two different time intervals;
step S103: aggregating the time sequence data of the two different time intervals according to time to obtain multivariate time sequence data, and converting the multivariate time sequence data into supervised data;
step S104: training a hand-foot-and-mouth disease prediction model according to the supervised data;
step S105: and inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the number of the patients with the hand-foot-and-mouth disease predicted in real time.
Specifically, in step S101, the historical case data of the hand-foot-and-mouth disease may include the number of patients and the time of the patients. For example, the number of attacks per month, or the number of attacks per quarter, or the number of attacks per year.
In step S102, preliminary data cleaning is performed on the historical case data, zero values are filtered, and invalid samples are removed. And carrying out normalization processing on the cleaned data, and compressing the numerical value of the historical case data to a determined interval to obtain the normalized data. The normalized data is then summed into time series data for two different time intervals. Here, the time series data of two different time intervals may be counted first, and then the normalization process is performed, which is not limited in the present application.
Wherein, the two different time intervals may be: the first time interval is the same as the target time interval, and the time sequence data counted according to the first time interval is called equal-granularity time sequence data; the second time interval is smaller than the target time interval, and the time sequence data counted according to the second time interval is called fine-grained time sequence data;
for example, if the target time interval is one year, then the first time interval is one year, the second time interval may be set to one month, and the second time interval is a finer granularity setting of the first time interval.
Setting the number of the first time intervals to be M, then [ y ] can be used 1 ，y 2 ，…，y M ]Representing isograininess time series data, the number of the attack people in the upcoming time interval is represented as
By setting the first time interval to comprise N second time intervals, x can be used 1 ，x 2 ，…，x M ]Representing equal-granularity time series data, wherein
Satisfy the requirement of
Wherein x t，i Representing the ith fine-grained data within the equal-grained time t.
Due to the significant difference in data range over different times, the present invention normalizes the data to [0,1] using Min-Max, the formula for normalization is as follows:
wherein,
time series data representing a certain granularity, d' represents the time series data after normalization, and min (-) and max (-) represent the minimum value and the maximum value of the input vector, respectively.
In step S103:
to capture time series characteristics from time series, the present invention converts the time series data into supervised data. Given a time series of data, the first several variables are used as input variables of the model and the latter variables are used as output of the model during the conversion process.
Given the granularity time series data [ y of the number of the sick people 1 ，y 2 ，…，y M ]The constant variable T is set to influence the number of the time intervals of the number of the persons who suffer from the disease in the next time interval. The time series conversion into supervised data is:
given aTime series data [ x ] of ith fine-grained time 1，i ，x 2，i ，…，x M，i ]The constant variable T is set to influence the number of the time intervals of the number of the persons who suffer from the disease in the next time interval. The time series conversion into supervised data is:
wherein i belongs to {1, …, N }.
Carrying out data aggregation on the time sequence data of two different time intervals according to time to obtain multivariate time sequence data, and converting the multivariate time sequence data into supervised data, wherein the expression is as follows:
in step S104, a hand-foot-and-mouth disease prediction model is trained according to the supervised data, and please refer to fig. 2, which shows a flowchart of a specific hand-foot-and-mouth disease prediction method using fine-grained data according to some embodiments of the present application:
as shown, the hand-foot-and-mouth disease prediction model includes: the system comprises an input layer, an equal-granularity sequence processing unit, a fine-granularity sequence processing unit, a merging layer, a full-connection layer and an output layer;
the input layer is used for inputting the supervised data, the equal-granularity sequence processing unit is used for processing equal-granularity time series data, the fine-granularity sequence processing unit is used for processing fine-granularity time series data, then a merging layer and a full-connection layer are used for mutually correlating the data output by the equal-granularity sequence processing unit and the data output by the fine-granularity sequence processing unit to generate an initial prediction result, and the output layer is used for carrying out reverse normalization on the initial prediction result to generate a final output prediction result.
The specific treatment process is as follows:
i) the expression part of the equal-granularity sequence processing unit comprises: a first GRU layer and a first thread layer. The first GRU layer is used for processing input equal-granularity time sequence data and outputting a hidden state of the input equal-granularity time sequence data. The first thread layer merges the hidden states and outputs the predicted values of the equal-granularity sequence processing units.
Conversion of GRU 1 Expressed, its formula is as follows:
h t ＝f 1 (h t-1 ，[y 1 ，y 2 ，…，y T ])，
wherein,
is a hidden state, n represents the number of hidden states; h is t-1 Indicating the previous hidden state. The equal-granularity time sequence data is used for generating a predicted value, and the predicted value can be converted through a thread layer, wherein the formula is as follows:
c y ＝w y h t +b y ，
wherein
Is a predicted value of the equal-granularity data side,
in order to be a weight parameter, the weight parameter,
is the bias term.
II) the representation part of the equal-granularity sequence processing unit comprises: a second thread layer, a softmax layer, a second GRU, and a third thread layer. The second thread layer carries out linear conversion on the input fine-grained data, the softmax layer is used for strengthening fine-grained data input at important moments, the second GRU layer is used for describing time sequence characteristics, and the third thread layer is used for merging hidden states and outputting predicted values of fine-grained components.
Highlighting periodic events by linear weighting is a key method to enhance the extraction of input information. The linear weights of the input elements can be summarized as follows:
u＝∑w u *[x 1 ，x 2 ，…，x T ]+b u ，
wherein,
Representing the corresponding fine-grained data in the input T equal-grained time interval,
is a weight, b u Is the term of the deviation in the sense that,
is a linearized output.
After linear weighting, there is data that is weakly or not correlated with the predicted target time. In order to ensure that the model can extract potential information of all input elements, softmax is used for weakening the influence of weakly correlated or uncorrelated data and increasing the weight of correlated data, and the formula is as follows:
p＝softmax(e)，
wherein,
representing the layer output. For any element in p are:
wherein exp (. Cndot.) represents an exponential function, e j，t Representing the elements in e.
The second GRU layer is used to dynamically extract the timing characteristics in p, which is expressed as follows:
h′ t ＝f 2 (h′ t-1 ，p)
wherein,
is a hidden state; h' t-1 Indicating the previous hidden state. And generating a predicted value by using the fine-grained time series data, and converting the predicted value through a thread layer, wherein the formula is as follows:
c e ＝w e h′ t +b e ，
wherein
Is a predicted value of the fine-grained data side,
is a weight, b e Is a bias term.
III) merging the output representation data of the two processing units (the equal-granularity sequence processing unit and the fine-granularity sequence processing unit) in the merging layer, combining the equal-granularity time sequence data and the fine-granularity time sequence data through a full connection layer to correlate the output of the two sides (the two processing units), and outputting a prediction result combining the fine granularity and the equal granularity through the output layer. This step can be summarized as follows:
wherein
Is a merged vector that is output from both sides,
it is the weight of these outputs that is,
is the predicted number of outpatients for the next time interval,
is a bias term.
Performing inverse normalization operation on the output prediction result, wherein the calculation formula is as follows:
d＝d′*(max(d)-min(d))+min(d)，
the inverse normalization formula is applied in the method to correct the final prediction result generated by the prediction model.
V) the hand-foot-and-mouth disease prediction model adopts Mean square error Mean Squared Error (MSE) as a loss function, namely an objective function, of the prediction model. And (5) minimizing the loss of the target function through iterative training, and establishing an optimal prediction model.
Specifically, the target function is set as MSE, and the time window size is set as T and the GRU hidden state parameter n. The prediction model is trained from the data generated in steps S103 and S104 and the corresponding prediction model. And reducing the output of the prediction model into the predicted number of the sick people by using an inverse normalization function. And adjusting the model parameters T and n according to the output result to obtain an optimal model.
In step S105, the real-time collected case data of the hand-foot-and-mouth disease is input into the trained hand-foot-and-mouth disease prediction model, and the number of the people with the hand-foot-and-mouth disease is obtained through real-time prediction.
As shown in fig. 2, after the output of the prediction model is inversely normalized, the prediction result is displayed to public health related personnel for prevention of diseases.
The hand-foot-and-mouth disease prediction method using the fine-grained data can be used for a client, and in the embodiment of the application, the client can comprise hardware and can also comprise software. When the client includes hardware, it may be various electronic devices having a display screen and supporting information interaction, for example, and may include, but not be limited to, a smart phone, a tablet computer, a laptop portable computer, a desktop computer, and the like. When the client includes software, it may be installed in the electronic device, and it may be implemented as a plurality of software or software modules, or as a single software or software module. And is not particularly limited herein.
Compared with the prior art, the hand-foot-and-mouth disease prediction method utilizing fine-grained data provided by the invention has the following advantages:
1. without external data sources
According to the hand-foot-and-mouth disease prediction method, the historical morbidity data of the hand-foot-and-mouth disease are counted by setting the time interval equal to the predicted target and the time interval finer than the time interval of the predicted target, and the accuracy of the predicted target based on the equal time granularity is improved by utilizing the time sequence data finer than the time interval of the predicted target. From a data perspective, the invention does not need additional data assistance; from the perspective of an algorithm, the invention captures fine-grained morbidity to judge future trends.
2. Finding crisis from details
The data used by the method is only the hand-foot-and-mouth disease case data. The essence of the invention is to count the number of infected persons from a finer-grained time period to predict the outbreak situation in a longer time period in the future, and to make the situation small and large. Because of the latency of the disease, it is possible to mine the potential outbreak population from fine-grained data.
In the above embodiment, a hand-foot-and-mouth disease prediction method using fine-grained data is provided, and correspondingly, the application also provides a hand-foot-and-mouth disease prediction device using fine-grained data. The hand-foot-and-mouth disease prediction device using fine-grained data provided by the embodiment of the application can implement the hand-foot-and-mouth disease prediction method using fine-grained data, and the hand-foot-and-mouth disease prediction device using fine-grained data can be implemented in a software, hardware or software and hardware combined mode. For example, the hand-foot-and-mouth disease prediction apparatus using fine-grained data may comprise integrated or separate functional modules or units to perform the corresponding steps of the above-described methods. Please refer to fig. 3, which illustrates a schematic diagram of an apparatus for predicting hand-foot-and-mouth disease using fine-grained data according to some embodiments of the present application. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
As shown in fig. 3, the hand-foot-and-mouth disease prediction apparatus 10 using fine-grained data may include:
the acquisition module 101 is used for acquiring historical case data of the hand-foot-and-mouth disease;
the preprocessing module 102 is configured to preprocess the historical case data, and count the historical case data into two kinds of time sequence data with different time intervals;
the aggregation module 103 is configured to aggregate the time sequence data of the two different time intervals according to time to obtain multivariate time sequence data, and convert the multivariate time sequence data into supervised data;
the model training module 104 is used for training a hand-foot-and-mouth disease prediction model according to the supervised data;
the prediction module 105 is used for inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the real-time predicted number of the patients with the hand-foot-and-mouth disease.
In some implementations of embodiments of the present application, the preprocessing module 102 is specifically configured to: and cleaning the historical case data, and normalizing the cleaned data.
In some implementations of the embodiments of the present application, of the two different time intervals, a first time interval is the same as the target time interval, and time series data counted according to the first time interval is referred to as equal-granularity time series data; the second time interval is smaller than the target time interval, and the time series data counted according to the second time interval is called fine-grained time series data.
The hand-foot-and-mouth disease prediction device 10 using fine-grained data provided by the embodiment of the application has the same beneficial effects as the hand-foot-and-mouth disease prediction method using fine-grained data provided by the previous embodiment of the application based on the same inventive concept.
The embodiment of the present application further provides an electronic device corresponding to the hand-foot-and-mouth disease prediction method using fine-grained data provided in the foregoing embodiment, where the electronic device may be an electronic device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, and the like, so as to execute the hand-foot-and-mouth disease prediction method using fine-grained data.
Referring to fig. 4, a schematic diagram of an electronic device provided in some embodiments of the present application is shown. As shown in fig. 4, the electronic device 20 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the computer program to execute the hand-foot-and-mouth disease prediction method using fine-grained data provided by any one of the foregoing embodiments of the present application.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic equipment provided by the embodiment of the application and the hand-foot-and-mouth disease prediction method using fine-grained data provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
The present application further provides a computer readable medium corresponding to the hand-foot-and-mouth disease prediction method using fine-grained data provided in the foregoing embodiments, and a computer program (i.e., a program product) is stored on the computer readable medium, and when the computer program is executed by a processor, the hand-foot-and-mouth disease prediction method using fine-grained data provided in any foregoing embodiments is executed.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memories (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the hand-foot-and-mouth disease prediction method using fine-grained data provided by the embodiment of the present application have the same inventive concept, and have the same beneficial effects as the method adopted, run, or implemented by the application program stored in the computer-readable storage medium.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification.
Claims (6)
1. A hand-foot-and-mouth disease prediction method using fine-grained data is characterized by comprising the following steps:
s1, acquiring historical case data of hand-foot-and-mouth disease;
s2, preprocessing the historical case data, and counting the historical case data into two time sequence data with different time intervals;
s3, performing data aggregation on the time sequence data of the two different time intervals according to time to obtain multivariate time sequence data, and converting the multivariate time sequence data into supervised data;
s4, training a hand-foot-and-mouth disease prediction model according to the supervised data;
s5, inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the number of the real-time predicted patients with the hand-foot-and-mouth disease;
in the two different time intervals in the step S2, the first time interval is the same as the target time interval, and the time series data counted according to the first time interval is called as equal-granularity time series data; the second time interval is smaller than the target time interval, and the time sequence data counted according to the second time interval is called fine-grained time sequence data;
setting the number of the first time intervals to be M, using [ y 1 ,y 2 ,…,y M ]Representing equal-granularity time series data;
setting the first time interval to contain N second time intervals, using [ x ] 1 ,x 2 ,…,x M ]The fine-grained data corresponding to the input M equal-grained time interval is represented arbitrarily
Satisfy the requirement of
Wherein x t,i Indicating equal granularity within time tThe ith fine-grained data of (1);
the preprocessing step in step S2 includes:
carrying out data cleaning on the historical case data, and carrying out normalization processing on the cleaned data;
the step S3 includes:
setting a constant variable T as the number of time intervals influencing the number of the persons who suffer from the diseases in the next time interval, converting the equal-granularity time series data, and expressing as follows:
setting time series data [ x ] of ith fine-grained moment 1,i ,x 2,i ,…,x M,i ]After the time series data is converted, the time series data is expressed as:
wherein, i belongs to {1, …, N };
carrying out data aggregation on the time sequence data of two different time intervals according to time to obtain multivariate time sequence data, and converting the multivariate time sequence data into supervised data, wherein the expression is as follows:
the specific treatment process is as follows:
i) the presentation component of the equal-granularity sequence processing unit comprises: a first GRU layer and a first thread layer; the first GRU layer is used for processing input equal-granularity time sequence data and outputting a hidden state of the input equal-granularity time sequence data; the first thread layer merges the hidden states and outputs the predicted value of the equal-granularity sequence processing unit;
conversion of GRU 1 Expressed, its formula is as follows:
h t ＝f 1 (h t-1 ,[y 1 ,y 2 ,…,y T ])，
wherein,
is a hidden state, n represents the number of hidden states; h is t-1 Representing a previous time hidden state; the equal-granularity time sequence data is used for generating a predicted value, and the predicted value can be converted through a thread layer, wherein the formula is as follows:
c y ＝w y h t +b y ，
wherein
Is a predicted value of the equal-granularity data side,
in order to be a weight parameter, the weight parameter,
is a bias term;
II) the representation part of the equal-granularity sequence processing unit comprises: the system comprises a second thread layer, a softmax layer, a second GRU and a third thread layer; the second thread layer carries out linear conversion on input fine-grained data, the softmax layer is used for strengthening fine-grained data input at important moments, the second GRU layer is used for describing time sequence characteristics, and the third thread layer is used for merging hidden states and outputting predicted values of fine-grained components;
highlighting periodic events by linear weighting is a key method for enhancing input information extraction; the linear weights of the input elements can be summarized as follows:
u＝∑w u *[x 1 ,x 2 ,…,x T ]+b u ，
wherein,
representing the corresponding fine-grained data in the input T equal-grained time interval,
is a weight, b u Is the term of the deviation in the sense that,
is a linearized output;
after linear weighting, the softmax layer is utilized to weaken the influence of weakly correlated or uncorrelated data and improve the weight of correlated data, and the formula is as follows:
p＝softmax(e)，
wherein,
representing softmax layer output; for any element in p are:
wherein exp (·) represents an exponential function;
the second GRU layer is used to dynamically extract the timing characteristics in p, which is formulated as follows:
h' t ＝f 2 (h' t-1 ,p)
wherein,
is a hidden state; h' t-1 Representing a previous time hidden state; the fine-grained time series data are used for generating a predicted value, and conversion can be carried out through a thread layer, and the formula is as follows:
c e ＝w e h' t +b e ，
wherein
Is a predicted value of the fine-grained data side,
is a weight, b e Is a bias term;
III) merging the output representation data of the medium-granularity sequence processing unit and the fine-granularity sequence processing unit in the merging layer, then combining the equal-granularity time sequence data and the fine-granularity time sequence data through a full connection layer to associate the output of the two processing units, and outputting a prediction result combining the fine granularity and the equal granularity through an output layer; this step can be summarized as follows:
wherein
Is a merged vector that is output from both sides,
it is the weight of these outputs that is,
is the predicted number of outpatients for the next time interval,
is a bias term;
performing inverse normalization operation on the output prediction result, wherein the calculation formula is as follows:
d＝d'*(max(d)-min(d))+min(d)，
the inverse normalization formula is applied in the method to correct the final prediction result generated by the prediction model.
2. The method of claim 1, wherein the hand-foot-and-mouth disease prediction model comprises: the system comprises an input layer, an equal-granularity sequence processing unit, a fine-granularity sequence processing unit, a merging layer, a full-connection layer and an output layer;
the input layer is used for inputting the supervised data, the equal-granularity sequence processing unit is used for processing equal-granularity time series data, the fine-granularity sequence processing unit is used for processing fine-granularity time series data, then a merging layer and a full-connection layer are used for mutually correlating the data output by the equal-granularity sequence processing unit and the data output by the fine-granularity sequence processing unit to generate an initial prediction result, and the output layer is used for carrying out reverse normalization on the initial prediction result to generate a final output prediction result.
3. The method according to claim 2, characterized by using the mean square error as a loss function of the hand-foot-and-mouth disease prediction model.
4. An apparatus for predicting hand-foot-and-mouth disease using fine-grained data, comprising:
the acquisition module is used for acquiring historical case data of the hand-foot-and-mouth disease;
the preprocessing module is used for preprocessing the historical case data and counting the historical case data into two time sequence data with different time intervals;
the aggregation module is used for carrying out data aggregation on the time sequence data of the two different time intervals according to time to obtain multivariate time sequence data and converting the multivariate time sequence data into supervised data;
the model training module is used for training a hand-foot-and-mouth disease prediction model according to the supervised data;
the prediction module is used for inputting the real-time collected case data of the hand-foot-and-mouth disease into the trained hand-foot-and-mouth disease prediction model to obtain the number of the real-time predicted patients of the hand-foot-and-mouth disease;
the preprocessing module is also used for calling the time sequence data counted according to the first time interval as equal-granularity time sequence data in two different time intervals, wherein the first time interval is the same as the target time interval; the second time interval is smaller than the target time interval, and the time sequence data counted according to the second time interval is called fine-grained time sequence data;
setting the number of the first time intervals to be M, using [ y 1 ,y 2 ,…,y M ]Indicating equal grainDegree time sequence data;
setting the first time interval to contain N second time intervals, using [ x ] 1 ,x 2 ,…,x M ]The fine-grained data corresponding to the input M equal-grained time interval is represented arbitrarily
Satisfy the requirement of
Wherein x t,i Representing the ith fine-grained data within the equal-grained time t;
the historical case data processing device is used for cleaning the historical case data and carrying out normalization processing on the cleaned data;
and the aggregation module is also used for setting a constant variable T as the number of time intervals influencing the number of the people suffering from the diseases in the next time interval, and converting the equal-granularity time sequence data to be expressed as:
setting time series data [ x ] of ith fine-grained moment 1,i ,x 2,i ,…,x M,i ]After the time series data is converted, the time series data is expressed as:
wherein, i belongs to {1, …, N };
carrying out data aggregation on the time sequence data of two different time intervals according to time to obtain multivariate time sequence data, and converting the multivariate time sequence data into supervised data, wherein the expression is as follows:
the specific treatment process is as follows:
i) the presentation component of the equal-granularity sequence processing unit comprises: a first GRU layer and a first thread layer; the first GRU layer is used for processing input equal-granularity time sequence data and outputting a hidden state of the input equal-granularity time sequence data; the first thread layer merges the hidden states and outputs the predicted value of the equal-granularity sequence processing unit;
conversion of GRU 1 Expressed, its formula is as follows:
h t ＝f 1 (h t-1 ,[y 1 ,y 2 ,…,y T ])，
wherein,
is a hidden state, n represents the number of hidden states; h is t-1 Representing a previous time hidden state; the equal-granularity time sequence data is used for generating a predicted value, and the predicted value can be converted through a thread layer, wherein the formula is as follows:
c y ＝w y h t +b y ，
wherein
Is a predicted value of the equal-granularity data side,
in order to be a weight parameter, the weight parameter,
is a bias term;
II) the representation part of the equal-granularity sequence processing unit comprises: the system comprises a second thread layer, a softmax layer, a second GRU and a third thread layer; the second thread layer carries out linear transformation on the input fine-grained data, the softmax layer is used for strengthening fine-grained data input at an important moment, the second GRU layer is used for describing time sequence characteristics, and the third thread layer is used for merging hidden states and outputting a predicted value of a fine-grained component;
highlighting periodic events by linear weighting is a key method for enhancing input information extraction; the linear weights of the input elements can be summarized as follows:
u＝∑w u *[x 1 ,x 2 ,…,x T ]+b u ，
wherein,
representing the corresponding fine-grained data in the input T equal-grained time interval,
is a weight, b u Is the term of the deviation in the sense that,
is a linearized output;
after linear weighting, the softmax layer is utilized to weaken the influence of weakly correlated or uncorrelated data and improve the weight of correlated data, and the formula is as follows:
p＝softmax(e)，
wherein,
representing softmax layer output; for any element in p are:
wherein exp (·) represents an exponential function;
the second GRU layer is used to dynamically extract the timing characteristics in p, which is formulated as follows:
h' t ＝f 2 (h' t-1 ,p)
wherein,
is a hidden state; h' t-1 Representing a previous time hidden state; the fine-grained time series data are used for generating a predicted value, and conversion can be carried out through a thread layer, and the formula is as follows:
c e ＝w e h' t +b e ，
wherein
Is a predicted value of the fine-grained data side,
is a weight, b e Is a bias term;
III) merging the output representation data of the two processing units (the equal-granularity sequence processing unit and the fine-granularity sequence processing unit) in the merging layer, combining the equal-granularity time sequence data and the fine-granularity time sequence data through a full connection layer to correlate the output of the two sides (the two processing units), and outputting a prediction result combining the fine granularity and the equal granularity through an output layer; this step can be summarized as follows:
wherein
Is a merged vector that is output from both sides,
it is the weight of these outputs that is,
is the predicted number of outpatients for the next time interval,
is a bias term;
performing inverse normalization operation on the output prediction result, wherein the calculation formula is as follows:
d＝d'*(max(d)-min(d))+min(d)，
the anti-normalization formula is applied in the method to correct the final prediction result generated by the prediction model.
5. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method according to any of claims 1 to 3.
6. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010704454.2A CN111863276B (en) | 2020-07-21 | 2020-07-21 | Hand-foot-and-mouth disease prediction method using fine-grained data, electronic device, and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010704454.2A CN111863276B (en) | 2020-07-21 | 2020-07-21 | Hand-foot-and-mouth disease prediction method using fine-grained data, electronic device, and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111863276A CN111863276A (en) | 2020-10-30 |
| CN111863276B true CN111863276B (en) | 2023-02-14 |
Family
ID=73000779
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010704454.2A Active CN111863276B (en) | 2020-07-21 | 2020-07-21 | Hand-foot-and-mouth disease prediction method using fine-grained data, electronic device, and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111863276B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112331349B (en) * | 2020-11-03 | 2023-04-07 | 四川大学华西医院 | Cerebral apoplexy relapse monitoring system |
| CN112562861B (en) * | 2020-11-19 | 2022-09-09 | 集美大学 | Method and device for training infectious disease prediction model |
| CN113223721B (en) * | 2021-03-23 | 2022-07-12 | 杭州电子科技大学 | Novel prediction control model for coronavirus pneumonia |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108280443A (en) * | 2018-02-23 | 2018-07-13 | 深圳市唯特视科技有限公司 | A kind of action identification method based on deep feature extraction asynchronous fusion network |
| CN109859854A (en) * | 2018-12-17 | 2019-06-07 | 中国科学院深圳先进技术研究院 | Prediction Method of Communicable Disease, device, electronic equipment and computer-readable medium |
| CN110070923A (en) * | 2019-03-07 | 2019-07-30 | 浙江大学 | A kind of residual hydrogenation model and method for building up based on semi-supervised depth GRU |
| CN111400366A (en) * | 2020-02-27 | 2020-07-10 | 西安交通大学 | Interactive outpatient quantity prediction visual analysis method and system based on Catboost model |
| CN111415752A (en) * | 2020-03-01 | 2020-07-14 | 集美大学 | Hand-foot-and-mouth disease prediction method integrating meteorological factors and search indexes |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180082036A1 (en) * | 2016-09-22 | 2018-03-22 | General Electric Company | Systems And Methods Of Medical Device Data Collection And Processing |
-
2020
- 2020-07-21 CN CN202010704454.2A patent/CN111863276B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108280443A (en) * | 2018-02-23 | 2018-07-13 | 深圳市唯特视科技有限公司 | A kind of action identification method based on deep feature extraction asynchronous fusion network |
| CN109859854A (en) * | 2018-12-17 | 2019-06-07 | 中国科学院深圳先进技术研究院 | Prediction Method of Communicable Disease, device, electronic equipment and computer-readable medium |
| CN110070923A (en) * | 2019-03-07 | 2019-07-30 | 浙江大学 | A kind of residual hydrogenation model and method for building up based on semi-supervised depth GRU |
| CN111400366A (en) * | 2020-02-27 | 2020-07-10 | 西安交通大学 | Interactive outpatient quantity prediction visual analysis method and system based on Catboost model |
| CN111415752A (en) * | 2020-03-01 | 2020-07-14 | 集美大学 | Hand-foot-and-mouth disease prediction method integrating meteorological factors and search indexes |
Non-Patent Citations (4)
| Title |
|---|
| ARIMA模型在佛山市高明区手足口病预测中的应用;张金奖 等;《中国公共卫生管理》;20180831;第34卷(第4期);第529-533页 * |
| Dilated Recurrent Neural Network for Epidemiological Predictions;Jianxiang Luo 等;《2019 3rd International Conference on Electronic Information Technology and Computer Engineering》;20191020;第1728-1731页 * |
| Dual-grained representation for hand, foot, and mouth disease prediction within public health cyber-physical systems;Zhijin Wang;《SOFTWARE-PRACTICE & EXPERIENCE》;20201215;第51卷(第11期);第2290-2305页 * |
| 利用时间序列模型分析预测辽宁手足口病疫情趋势;王伶 等;《中国卫生统计》;20161031;第33卷(第5期);第847-849页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111863276A (en) | 2020-10-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111863276B (en) | Hand-foot-and-mouth disease prediction method using fine-grained data, electronic device, and medium | |
| Tsantekidis et al. | Using deep learning for price prediction by exploiting stationary limit order book features | |
| US11586880B2 (en) | System and method for multi-horizon time series forecasting with dynamic temporal context learning | |
| Reddy et al. | A deep neural networks based model for uninterrupted marine environment monitoring | |
| JP6844301B2 (en) | Methods and data processors to generate time series data sets for predictive analytics | |
| Liu et al. | Foreign exchange rates forecasting with convolutional neural network | |
| TW201946013A (en) | Credit risk prediction method and device based on LSTM (Long Short Term Memory) model | |
| CN111898675B (en) | Credit wind control model generation method and device, scoring card generation method, machine readable medium and equipment | |
| CN116029395B (en) | Pedestrian flow early warning method and device for business area, electronic equipment and storage medium | |
| JP2023547002A (en) | Identification method of K-line pattern and electronic equipment | |
| Leevy et al. | Investigating the relationship between time and predictive model maintenance | |
| Wibawa et al. | Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal | |
| Ghimire et al. | Efficient daily electricity demand prediction with hybrid deep-learning multi-algorithm approach | |
| Zuo et al. | Variable selection with second-generation p-values | |
| CN114167487B (en) | Seismic magnitude estimation method and device based on characteristic waveform | |
| CN115545103A (en) | Abnormal data identification method, label identification method and abnormal data identification device | |
| CN114118570A (en) | Service data prediction method and device, electronic equipment and storage medium | |
| Shahzadi et al. | A novel data driven approach for combating energy theft in urbanized smart grids using artificial intelligence | |
| Mendoza et al. | Market index price prediction using deep neural networks with a self-similarity approach | |
| CN117391466A (en) | Novel early warning method and system for contradictory dispute cases | |
| Hammer et al. | Joint tracking of multiple quantiles through conditional quantiles | |
| Khairuddin et al. | Comparative study on artificial intelligence techniques in crime forecasting | |
| CN116576504A (en) | Interpretable region thermal load prediction method, interpretable region thermal load prediction device, interpretable region thermal load prediction equipment and storage medium | |
| CN115908008A (en) | Stock trading amount prediction and model training method, apparatus, device and medium | |
| CN113537631B (en) | Medicine demand prediction method, device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |