WO2019196280A1 - Procédé et dispositif de prédiction de maladie, dispositif informatique et support d'informations lisible - Google Patents

Procédé et dispositif de prédiction de maladie, dispositif informatique et support d'informations lisible Download PDF

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WO2019196280A1
WO2019196280A1 PCT/CN2018/099612 CN2018099612W WO2019196280A1 WO 2019196280 A1 WO2019196280 A1 WO 2019196280A1 CN 2018099612 W CN2018099612 W CN 2018099612W WO 2019196280 A1 WO2019196280 A1 WO 2019196280A1
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data
weather
disease
layer
public opinion
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PCT/CN2018/099612
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English (en)
Chinese (zh)
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阮晓雯
徐亮
肖京
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • 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
    • 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

Definitions

  • the present application relates to the field of prediction technologies, and in particular, to a disease prediction method and apparatus, a computer apparatus, and a non-volatile readable storage medium.
  • disease prediction An important task in the early warning of public health emergencies is disease prediction, which predicts future disease surveillance data based on historical disease surveillance data (ie, patient data).
  • disease prediction With the development of machine learning technology, more and more machine learning methods are applied to disease prediction.
  • traditional machine learning applied to disease prediction often requires artificially defining feature sets, and then searching for the best feature combinations from the defined feature sets, and the effects are often not good enough, thus affecting the accuracy of disease prediction.
  • a first aspect of the present application provides a disease prediction method, the method comprising:
  • the disease monitoring data is time series data
  • weather data related to the disease monitoring data the weather data being time series data corresponding to the disease monitoring data
  • Pre-processing the disease monitoring data, weather data, and public opinion data Pre-processing the disease monitoring data, weather data, and public opinion data
  • the optimized multi-layer GRU model is input to obtain a disease prediction result at the predicted time point.
  • a second aspect of the present application provides a disease prediction apparatus, the apparatus comprising:
  • a first acquiring unit configured to acquire disease monitoring data, where the disease monitoring data is time series data
  • a second acquiring unit configured to acquire weather data related to the disease monitoring data, where the weather data is time series data corresponding to the disease monitoring data;
  • a third obtaining unit configured to acquire public opinion data related to the disease monitoring data, where the public opinion data is time series data corresponding to the disease monitoring data;
  • a pre-processing unit for pre-processing the disease monitoring data, weather data, and public opinion data
  • a building unit for constructing a multi-layer gated recursive unit neural network model that is, a multi-layer GRU model
  • An optimization unit configured to acquire training data and verification data from the pre-processed disease monitoring data, weather data, and public opinion data, and use the training data and the verification data to train and perform performance on the multi-layer GRU model Verification, obtaining an optimized multi-layer GRU model;
  • a prediction unit configured to obtain disease monitoring data, weather data, and public opinion data before the predicted time point from the pre-processed disease monitoring data, weather data, and public opinion data, and the disease monitoring data before the predicted time point,
  • the weather data and the public opinion data are input to the optimized multi-layer GRU model to obtain the disease prediction result at the predicted time point.
  • a third aspect of the present application provides a computer apparatus comprising a memory and a processor, the memory for storing at least one computer readable instruction, the processor for executing the at least one computer readable instruction Implement the following steps:
  • the disease monitoring data is time series data
  • weather data related to the disease monitoring data the weather data being time series data corresponding to the disease monitoring data
  • Pre-processing the disease monitoring data, weather data, and public opinion data Pre-processing the disease monitoring data, weather data, and public opinion data
  • the optimized multi-layer GRU model is input to obtain a disease prediction result at the predicted time point.
  • a fourth aspect of the present application provides a non-volatile readable storage medium storing at least one computer readable instruction when executed by a processor Implement the following steps:
  • the disease monitoring data is time series data
  • weather data related to the disease monitoring data the weather data being time series data corresponding to the disease monitoring data
  • Pre-processing the disease monitoring data, weather data, and public opinion data Pre-processing the disease monitoring data, weather data, and public opinion data
  • the optimized multi-layer GRU model is input to obtain a disease prediction result at the predicted time point.
  • the present application acquires disease monitoring data, which is time-series data; acquires weather data related to the disease monitoring data, the weather data is time-series data corresponding to the disease monitoring data; and acquiring the disease monitoring Data-related public opinion data, wherein the public opinion data is time-series data corresponding to the disease monitoring data; pre-processing the disease monitoring data, weather data, and public opinion data; constructing a multi-layer gated recursive unit neural network model, a multi-layer GRU model; obtaining training data and verification data from the pre-processed disease monitoring data, weather data, and public opinion data, and training the multi-layer GRU model using the training data and the verification data Performance verification, obtaining an optimized multi-layer GRU model; obtaining disease monitoring data, weather data, and public opinion data before the predicted time point from the pre-processed disease monitoring data, weather data, and public opinion data, and predicting the predicted time Disease monitoring data, weather data and public opinion data before the point are input into the optimized GRU multilayer model to obtain a prediction result predicted disease point
  • the present application predicts disease data through a multi-layer GRU model.
  • the GRU model can extract knowledge directly from the data, construct a feature vector that is favorable for prediction, and improve the prediction accuracy.
  • the present application adds weather data and public opinion data as influencing factors to the disease prediction, thereby improving the accuracy of disease prediction.
  • the GRU model used in this application has a simple structure and can be quickly optimized to speed up the entire disease prediction process. Therefore, the present application achieves rapid and high accuracy disease prediction.
  • FIG. 1 is a flowchart of a disease prediction method according to Embodiment 1 of the present application.
  • FIG. 2 is a detailed flowchart of acquiring weather data related to disease monitoring data in the disease prediction method provided in the second embodiment of the present application.
  • FIG. 3 is a structural diagram of a disease prediction apparatus according to Embodiment 3 of the present application.
  • FIG. 4 is a detailed structural diagram of a second acquisition unit in the disease prediction apparatus provided in Embodiment 4 of the present application.
  • FIG. 5 is a schematic diagram of a computer device according to Embodiment 5 of the present application.
  • the disease prediction method of the present application is applied to one or more computer devices.
  • the computer device is a device capable of automatically performing numerical calculation and/or information processing according to an instruction set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor and an application specific integrated circuit (ASIC). , Field-Programmable Gate Array (FPGA), Digital Signal Processor (DSP), embedded devices, etc.
  • ASIC application specific integrated circuit
  • FPGA Field-Programmable Gate Array
  • DSP Digital Signal Processor
  • embedded devices etc.
  • the computer device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote controller, a touch panel, or a voice control device.
  • FIG. 1 is a flowchart of a disease prediction method according to Embodiment 1 of the present application.
  • the disease prediction method is applied to a computer device.
  • the disease prediction method predicts disease monitoring data by using a gated recursive unit neural network model to obtain a high-accuracy disease prediction result.
  • the disease prediction method specifically includes the following steps:
  • step 101 disease monitoring data is acquired, and the disease monitoring data is time series data.
  • the disease monitoring data may include disease data for diseases such as influenza, hand, foot and mouth disease, measles, and mumps.
  • a disease monitoring network composed of a plurality of monitoring points may be established in a preset area (for example, a province, a city, a region), and disease monitoring data is acquired from the monitoring points, and the disease monitoring data constitutes time series data of disease monitoring.
  • Medical institutions, schools, child care institutions, pharmacies, etc. can be selected as monitoring points to conduct disease monitoring and data collection for the corresponding target population.
  • a place that meets the preset conditions can be selected as the monitoring point.
  • the preset condition may include a number of people, a scale, and the like. For example, select a school with a predetermined number of schools and child care institutions as monitoring points. Another example is to select a pharmacy that has reached the preset size (for example, by daily turnover) as a monitoring point. For another example, select a hospital (for example, the number of people who seek medical treatment in Japan) to reach a preset size as a monitoring point.
  • Disease monitoring data at different times constitute time series data for disease surveillance.
  • disease monitoring data collected on a daily basis can be used to form time series data for disease surveillance.
  • the disease monitoring data collected on a weekly basis may constitute time series data for disease monitoring.
  • Medical institutions (mainly including hospitals) are the best place to capture early warning signs of disease and are the first choice for disease surveillance.
  • Disease surveillance data can be obtained based on patient visits.
  • the disease monitoring data can be obtained according to the drug sales of the pharmacy.
  • the medical institution, the school, the child care institution, and the pharmacy are mainly selected for the collection of disease monitoring data.
  • the above selection of data sources does not limit the addition or replacement of other focused populations or sites in other implementations as a source of data for monitoring.
  • hotels can be included in the disease surveillance area to obtain disease surveillance data for hotel residents.
  • the disease monitoring data collected by any type of monitoring point can constitute time series data of disease monitoring.
  • the disease monitoring data collected by the hospital can be taken to constitute time series data of disease monitoring.
  • the disease monitoring data collected by the plurality of types of monitoring points can be combined to form time series data of disease monitoring.
  • the disease monitoring data collected by the hospital can be mainly used, supplemented by the disease monitoring data participated by the pharmacy, and constitute time series data of disease monitoring.
  • the disease monitoring data may include disease data such as the number of visits to the disease, the rate of visits, the number of cases, and the incidence rate.
  • disease data such as the number of visits to the disease, the rate of visits, the number of cases, and the incidence rate.
  • the number of daily visits to a disease eg, flu
  • a medical institution eg, a hospital
  • the number of daily visits of the disease eg, flu
  • the daily incidence of a student's disease eg, influenza
  • influenza can be obtained from the school, and the daily incidence of the disease (eg, influenza) can be used as disease monitoring data.
  • Step 102 Acquire weather data related to the disease monitoring data, where the weather data is time series data corresponding to the disease monitoring data.
  • Weather data related to disease surveillance data refers to weather data that affect disease surveillance data (ie disease disease data).
  • the influence of different weather data on the disease monitoring data may be analyzed in advance, and weather data having influence or influence on the disease monitoring data may be determined according to the analysis result.
  • the weather data may include humidity, temperature, air pressure, precipitation, water vapor pressure, wind speed, wind direction, and sunshine hours.
  • the weather data may include daily average temperature, average air pressure, maximum temperature, minimum temperature, average relative humidity, minimum relative humidity, precipitation, average wind speed, sunshine hours, and average water vapor pressure.
  • the weather data is the same as the time period corresponding to the disease monitoring data, and the weather data is the same as the statistical period (eg, daily, weekly) of the disease monitoring data.
  • the disease monitoring data is the number of daily visits from January to February 2018, and the weather data is daily weather data for January-February 2018.
  • the disease monitoring data is the number of weekly visits from January to December 2017, and the weather data is weekly weather data (eg, weekly average temperature) from January to December 2017.
  • the weather data can be captured from weather information websites (such as China Weather Network, Sina Weather, Sohu Weather, etc.) to improve the reliability of the weather data. It can be understood that the weather data can be captured from any webpage.
  • weather information websites such as China Weather Network, Sina Weather, Sohu Weather, etc.
  • Weather data for a predetermined area can be captured.
  • the predetermined area may include a province, a city, a region, and the like. For example, grab weather data from Shenzhen.
  • the predetermined time may include a year, a month, a day, and the like. For example, grab daily weather data for January-February 2018.
  • the weather data can be captured by a web crawler.
  • a web crawler is an application that automatically extracts the content of web page data. Web crawlers usually start with a URL (also called a seed URL) of one or several initial web pages, obtain the URL of the initial web page, and fetch the web page according to specific algorithms and strategies (such as depth-first search strategy). In the process, the new URL is continuously extracted from the current web page and placed in the corresponding queue until the stop condition is satisfied.
  • the URL is an abbreviation of Uniform Resource Locator, which is a uniform resource locator.
  • the weather data can be captured by using an open API interface of the weather information website (for example, an API interface opened by the China Weather Network).
  • the API is an abbreviation of application interface, which can realize mutual communication between computer software through an API interface.
  • the open API interface of the weather information website can return data in JSON format or XML format.
  • the weather data can be captured by a web crawler using an open API interface of the weather information website. See Figure 2 for the specific process of crawling the weather data through the web crawler using the open API interface of the weather information website.
  • Step 103 Acquire public opinion data related to the disease monitoring data, where the public opinion data is time series data corresponding to the disease monitoring data.
  • the public opinion data related to the disease surveillance data refers to the public opinion data reflecting the disease monitoring data.
  • a disease such as the flu
  • many people go online to search for disease-related words (such as flu, Tamiflu, high fever, etc.), which have a large search volume. increase.
  • disease-related content such as illness information, treatment information, etc.
  • news websites such as news, forums, blogs, and post bars increases. Therefore, disease prediction data can be used to assist in disease prediction.
  • the lyric data may include the number of searches for a particular word.
  • the number of searches for a particular word by a predetermined search engine can be counted (eg, a specific region pre-sets the number of daily searches by a search engine for a particular word).
  • the sensation data may also include the number of lyric information containing a particular word for a particular sensation website (eg, news, forums, blogs, post bars, etc.).
  • the specific word is a word related to the predicted disease, for example, the specific word is a word related to the disease symptom, and when the predicted disease is influenza, the specific word may include: sudden onset, high fever, chills, headache , weakness, inflammation of the throat, muscle soreness, dry cough, etc.
  • the specific words when the predicted disease is hand, foot and mouth, the specific words may include: mouth pain, anorexia, hypothermia, hand herpes, small mouth ulcers, and the like.
  • the time period corresponding to the disease monitoring data is the same, and the public opinion data is the same as the statistical period of the disease monitoring (eg, daily, weekly).
  • the disease monitoring data is the number of daily visits from January to February 2018, and the public opinion data is daily sensation data of January-February 2018 (for example, the number of search times for a specific word day).
  • the disease monitoring data is the number of weekly visits from January to December 2017, and the public opinion data is weekly sensation data of January-December 2017 (for example, a specific number of word searches).
  • steps 101-103 may be performed in any order or in parallel.
  • step 104 the disease monitoring data, the weather data, and the public opinion data are preprocessed.
  • Pre-processing of disease monitoring data, weather data, and public opinion data may include anomalous data processing.
  • Abnormal data processing of disease surveillance data, weather data and public opinion data is to correct abnormal data in the disease monitoring data, weather data and public opinion data, and improve the reliability and accuracy of disease prediction.
  • the abnormal data processing can include filling missing values in the disease monitoring data, weather data, and public opinion data.
  • the missing values can be filled by the mean or median of the data before and after the missing values, or the missing values can be filled by regression fitting.
  • the abnormal data processing may further include correcting abnormal values in the disease monitoring data, weather data, and public opinion data.
  • the outlier is a value that deviates significantly from other data. The outlier can be corrected by interpolation.
  • Pre-processing of disease monitoring data, weather data, and public opinion data may also include data format conversion of the disease monitoring data, weather data, and public opinion data.
  • disease surveillance data, weather data, and public opinion data are standardized so that disease surveillance data, weather data, and public opinion data have a consistent standard format to fit the input data as a GRU model.
  • Step 105 Construct a Gated Recurrent Unit Neural Network model, that is, a multi-layer GRU model.
  • the multi-layer GRU model includes two layers of GRU unit layers and one layer of fully connected layers, and the first layer of GRU unit layers is used to construct features for input data (eg, input data composed of disease monitoring data, weather data, and public opinion data)
  • Obtaining a first hidden layer unit where the second layer GRU unit layer is configured to combine the first hidden layer unit to obtain a second hidden layer unit, where the fully connected layer is used according to the second hidden layer
  • the unit obtains prediction results (eg, disease prediction results), and each GRU unit layer includes a reset gate and an update gate that controls the memory state of the GRU unit layer.
  • the GRU model is a time recurrent neural network model. Compared with the traditional Recurrent Neural Network (RNN) model, the GRU model stores information by constructing some gates at the GRU unit layer, so the gradient does not disappear quickly during the model training.
  • RNN Recurrent Neural Network
  • the multi-layer GRU model used in the method includes two layers of GRU unit layers and one layer of fully connected layers, and the first layer of GRU unit layers is used to construct features for input data (such as disease monitoring data, weather data, and input data composed of public opinion data).
  • Obtaining a first hidden layer unit wherein the second layer GRU unit layer is configured to combine the first hidden layer units to obtain a second hidden layer unit.
  • the fully connected layer obtains a predicted value according to the second hidden layer unit.
  • the first hidden layer unit is a local feature
  • the second hidden layer unit is a global feature. That is, the first layer GRU unit layer is used to extract local information, and the second layer GRU unit layer is used to combine global features to obtain global features, and the fully connected layer is used to obtain prediction results according to global features (eg, disease prediction results). .
  • the GRU unit layer includes an update gate z t and a reset gate r t .
  • the update gate z t is a logic gate that updates the hidden layer unit h t .
  • Reset gate r t decides to choose candidate hidden layer unit When to discard the previous hidden layer unit h t .
  • the update gate z t of the GRU unit layer, the reset gate r t , and the candidate hidden layer unit And the hidden layer unit h t is calculated as follows:
  • r t ⁇ (W r x t +U r h t-1 +b r ).
  • ⁇ is the Sigmoid activation function
  • tanh is the Tanh activation function
  • W z , U z , b z are the parameters of the update gate z t
  • W r , U r , b r are the parameters of the reset gate r t
  • W, U , b is a candidate hidden layer unit Parameters.
  • Step 106 Obtain training data and verification data from the pre-processed disease monitoring data, weather data, and public opinion data, and perform training and performance verification on the multi-layer GRU model by using the training data and the verification data.
  • the optimized multi-layer GRU model is obtained.
  • the time series data may be intercepted from the disease monitoring data, the weather data, and the public opinion data after the pre-processing to constitute the training data and the verification data.
  • the input data of the multi-layer GRU model is a vector of a preset dimension (for example, 1000 dimensions).
  • the pre-processed disease monitoring data, weather data and public opinion data corresponding to each time point may be constructed into a preset dimension vector from the intercepted time series data, and the vectors corresponding to the respective time points are sequentially input into the time sequence.
  • a multi-layer GRU model is used to train or verify the multi-layer GRU model.
  • first time series data for training the multi-layer GRU model For example, intercepting first time series data for training the multi-layer GRU model from the pre-processed disease monitoring data, weather data, and public opinion data; each time point from the intercepted first time series data Corresponding pre-processed disease monitoring data, weather data, and public opinion data construct a first vector of a preset dimension, and sequentially input the first vector corresponding to each time point into the multi-layer GRU model in time sequence, for The multi-layer GRU model is trained.
  • the pre-processed disease monitoring data, the weather data, and the public opinion data construct a second vector of a preset dimension, and sequentially input the second vector corresponding to each time point into the multi-layer GRU model in time sequence, for Multi-layer GRU model for verification.
  • the loss function of the multi-layer GRU model may be defined as a mean square error, and the parameters of the multi-layer GRU model are adjusted such that the mean square error takes a minimum value.
  • the training process can use the RMSprop algorithm.
  • RMSprop is an improved stochastic gradient descent algorithm.
  • the mean square error and RMSprop algorithm are prior art and will not be described here.
  • Step 107 Obtain disease monitoring data, weather data, and public opinion data before the predicted time point from the pre-processed disease monitoring data, weather data, and public opinion data, and use the disease monitoring data and weather data before the predicted time point. And the lyrical data is input into the optimized multi-layer GRU model to obtain a disease prediction result at the predicted time point.
  • the disease monitoring data, weather data, and public opinion data before the predicted time point are obtained as time series data.
  • the disease monitoring data, the weather data and the public opinion data before the predicted time point are obtained, and the pre-processed disease monitoring data, the weather data and the public opinion data corresponding to each time point are constructed into a third vector of a preset dimension. In a chronological order, the third vector corresponding to each time point is sequentially input to the multi-layer GRU model to perform disease prediction on the predicted time point.
  • the optimized multi-layer GRU model obtains the hidden layer units of the current time point through the input data of the current time point and the hidden layer unit of the previous time point, according to the current
  • the hidden layer unit at the time point obtains the predicted value of the current time point, and continuously recursively acquires the hidden layer unit of the next time point and the predicted value according to the chronological order until the predicted value of the given time point is obtained.
  • Example 1 predicts disease data by a multi-layer GRU model.
  • the GRU model can extract knowledge directly from the data, construct a feature vector that is favorable for prediction, and improve the prediction accuracy.
  • the weather data and the public opinion data are included as influence factors in the disease prediction, and the accuracy of the disease prediction is improved.
  • the GRU model compared with the disease prediction method based on LSTM (Long Short-term Memory) model, the GRU model has a simple structure and can be quickly optimized to speed up the entire disease prediction process. Therefore, the first embodiment achieves a fast and high accuracy rate of disease prediction.
  • FIG. 2 is a detailed flowchart of obtaining weather data related to disease monitoring data (ie, step 102 in FIG. 1) in the disease prediction method provided in the second embodiment of the present application.
  • the weather data can be captured by a web crawler using an open API interface of the weather information website. Referring to FIG. 2, the following steps may be specifically included:
  • Step 201 Generate a seed URL for the API interface of the weather information website and a subsequent URL.
  • the seed URL is the basis and premise for the web crawler to do everything.
  • the seed URL can be one or more.
  • the structural characteristics of the URL of the weather information website can be analyzed, and the subsequent URLs are obtained according to the structural characteristics of the URL.
  • Step 202 Send an HTTP request to an API interface of the weather information website, requesting access to the API interface.
  • the HTTP request can be sent to the API interface of the weather information website in GET mode.
  • an HTTP response is returned to inform that the weather data can be acquired.
  • Step 203 Analyze and identify the data content provided by the weather information website to view the data content.
  • the weather information website provides data content in a specific format, and needs to analyze and identify the data content in a specific format provided by the weather information website to view the data content.
  • the data format provided by the API interface of the weather information website is in JSON format.
  • JSON is a data exchange format that uses a grammar convention similar to C.
  • the data content of the JSON format is analyzed and identified to view the data content.
  • Step 204 Determine whether the data content is a predetermined information content.
  • the data content is a predetermined information content. If the data content is not the predetermined information content, the data content is discarded, otherwise the next step is performed.
  • Step 205 If the data content is a predetermined information content, the data content is captured.
  • a depth-first search strategy may be used for the state space search when the data content is captured.
  • Step 206 Save the captured data content as the weather data to the local.
  • a database can be created on the computing device to save the weather data to the database.
  • the traditional web crawler first sets one or more portal URLs.
  • a new URL is extracted from the current webpage into the queue, so as to obtain the webpage content corresponding to the URL. , save the content of the webpage to the local, and then extract the effective address as the next entry URL until the crawl is completed.
  • traditional web crawlers download a large number of irrelevant web pages.
  • FIG. 3 is a structural diagram of a disease prediction apparatus according to Embodiment 3 of the present application.
  • the disease prediction apparatus 10 may include: a first acquisition unit 301, a second acquisition unit 302, a third acquisition unit 303, a pre-processing unit 304, a construction unit 305, an optimization unit 306, and a prediction unit 307.
  • the first obtaining unit 301 is configured to acquire disease monitoring data, where the disease monitoring data is time series data.
  • the disease monitoring data may include disease data for diseases such as influenza, hand, foot and mouth disease, measles, and mumps.
  • a disease monitoring network composed of a plurality of monitoring points may be established in a preset area (for example, a province, a city, a region), and disease monitoring data is acquired from the monitoring points, and the disease monitoring data constitutes time series data of disease monitoring.
  • Medical institutions, schools, child care institutions, pharmacies, etc. can be selected as monitoring points to conduct disease monitoring and data collection for the corresponding target population.
  • a place that meets the preset conditions can be selected as the monitoring point.
  • the preset condition may include a number of people, a scale, and the like. For example, select a school with a predetermined number of schools and child care institutions as monitoring points. Another example is to select a pharmacy that has reached the preset size (for example, by daily turnover) as a monitoring point. For another example, select a hospital (for example, the number of people who seek medical treatment in Japan) to reach a preset size as a monitoring point.
  • Disease monitoring data at different times constitute time series data for disease surveillance.
  • disease monitoring data collected on a daily basis can be used to form time series data for disease surveillance.
  • the disease monitoring data collected on a weekly basis may constitute time series data for disease monitoring.
  • Medical institutions (mainly including hospitals) are the best place to capture early warning signs of disease and are the first choice for disease surveillance.
  • Disease surveillance data can be obtained based on patient visits.
  • the disease monitoring data can be obtained according to the drug sales of the pharmacy.
  • the medical institution, the school, the child care institution, and the pharmacy are mainly selected for the collection of disease monitoring data.
  • the above selection of data sources does not limit the addition or replacement of other focused populations or sites in other embodiments as a source of data for monitoring.
  • hotels can be included in the disease surveillance area to obtain disease surveillance data for hotel residents.
  • the disease monitoring data collected by any type of monitoring point can constitute time series data of disease monitoring.
  • the disease monitoring data collected by the hospital can be taken to constitute time series data of disease monitoring.
  • the disease monitoring data collected by the plurality of types of monitoring points can be combined to form time series data of disease monitoring.
  • the disease monitoring data collected by the hospital can be mainly used, supplemented by the disease monitoring data participated by the pharmacy, and constitute time series data of disease monitoring.
  • the disease monitoring data may include disease data such as the number of visits to the disease, the rate of visits, the number of cases, and the incidence rate.
  • disease data such as the number of visits to the disease, the rate of visits, the number of cases, and the incidence rate.
  • the number of daily visits to a disease eg, flu
  • a medical institution eg, a hospital
  • the number of daily visits of the disease eg, flu
  • the daily incidence of a student's disease eg, influenza
  • influenza can be obtained from the school, and the daily incidence of the disease (eg, influenza) can be used as disease monitoring data.
  • the second obtaining unit 302 is configured to acquire weather data related to the disease monitoring data, where the weather data is time series data corresponding to the disease monitoring data.
  • Weather data related to disease surveillance data refers to weather data that affect disease surveillance data (ie disease disease data).
  • the influence of different weather data on the disease monitoring data may be analyzed in advance, and weather data having influence or influence on the disease monitoring data may be determined according to the analysis result.
  • the weather data may include humidity, temperature, air pressure, precipitation, water vapor pressure, wind speed, wind direction, and sunshine hours.
  • the weather data may include daily average temperature, average air pressure, maximum temperature, minimum temperature, average relative humidity, minimum relative humidity, precipitation, average wind speed, sunshine hours, and average water vapor pressure.
  • the weather data is the same as the time period corresponding to the disease monitoring data, and the weather data is the same as the statistical period (eg, daily, weekly) of the disease monitoring data.
  • the disease monitoring data is the number of daily visits from January to February 2018, and the weather data is daily weather data for January-February 2018.
  • the disease monitoring data is the number of weekly visits from January to December 2017, and the weather data is weekly weather data (eg, weekly average temperature) from January to December 2017.
  • the weather data can be captured from weather information websites (such as China Weather Network, Sina Weather, Sohu Weather, etc.) to improve the reliability of the weather data. It can be understood that the weather data can be captured from any webpage.
  • weather information websites such as China Weather Network, Sina Weather, Sohu Weather, etc.
  • Weather data for a predetermined area can be captured.
  • the predetermined area may include a province, a city, a region, and the like. For example, grab weather data from Shenzhen.
  • the predetermined time may include a year, a month, a day, and the like. For example, grab daily weather data for January-February 2018.
  • the weather data can be captured by a web crawler.
  • a web crawler is an application that automatically extracts the content of web page data. Web crawlers usually start with a URL (also called a seed URL) of one or several initial web pages, obtain the URL of the initial web page, and fetch the web page according to specific algorithms and strategies (such as depth-first search strategy). In the process, the new URL is continuously extracted from the current web page and placed in the corresponding queue until the stop condition is satisfied.
  • the URL is an abbreviation of Uniform Resource Locator, which is a uniform resource locator.
  • the weather data can be captured by using an open API interface of the weather information website (for example, an API interface opened by the China Weather Network).
  • the API is an abbreviation of application interface, which can realize mutual communication between computer software through an API interface.
  • the open API interface of the weather information website can return data in JSON format or XML format.
  • the weather data can be captured by a web crawler using an open API interface of the weather information website. See Figure 2 for the specific process of crawling the weather data through the web crawler using the open API interface of the weather information website.
  • the third obtaining unit 303 is configured to acquire public opinion data related to the disease monitoring data, where the public opinion data is time series data corresponding to the disease monitoring data.
  • the public opinion data related to the disease surveillance data refers to the public opinion data reflecting the disease monitoring data.
  • a disease such as the flu
  • many people go online to search for disease-related words (such as flu, Tamiflu, high fever, etc.), which have a large search volume. increase.
  • disease-related content such as illness information, treatment information, etc.
  • news websites such as news, forums, blogs, and post bars increases. Therefore, disease prediction data can be used to assist in disease prediction.
  • the lyric data may include the number of searches for a particular word.
  • the number of searches for a particular word by a predetermined search engine can be counted (eg, a specific region pre-sets the number of daily searches by a search engine for a particular word).
  • the sensation data may also include the number of lyric information containing a particular word for a particular sensation website (e.g., news, forums, blogs, post bars, etc.).
  • the specific word is a word related to the predicted disease, for example, the specific word is a word related to the disease symptom, and when the predicted disease is influenza, the specific word may include: sudden onset, high fever, chills, headache , weakness, inflammation of the throat, muscle soreness, dry cough, etc.
  • the specific words when the predicted disease is hand, foot and mouth, the specific words may include: mouth pain, anorexia, hypothermia, hand herpes, small mouth ulcers, and the like.
  • the time period corresponding to the disease monitoring data is the same, and the public opinion data is the same as the statistical period of the disease monitoring (eg, daily, weekly).
  • the disease monitoring data is the number of daily visits from January to February 2018, and the public opinion data is daily sensation data of January-February 2018 (for example, the number of search times for a specific word day).
  • the disease monitoring data is the number of weekly visits from January to December 2017, and the public opinion data is weekly sensation data of January-December 2017 (for example, a specific number of word searches).
  • the pre-processing unit 304 is configured to pre-process the disease monitoring data, the weather data, and the public opinion data.
  • Pre-processing of disease monitoring data, weather data, and public opinion data may include anomalous data processing.
  • Abnormal data processing of disease surveillance data, weather data and public opinion data is to correct abnormal data in the disease monitoring data, weather data and public opinion data, and improve the reliability and accuracy of disease prediction.
  • the abnormal data processing can include filling missing values in the disease monitoring data, weather data, and public opinion data.
  • the missing values can be filled by the mean or median of the data before and after the missing values, or the missing values can be filled by regression fitting.
  • the abnormal data processing may further include correcting abnormal values in the disease monitoring data, weather data, and public opinion data.
  • the outlier is a value that deviates significantly from other data. The outlier can be corrected by interpolation.
  • Pre-processing of disease monitoring data, weather data, and public opinion data may also include data format conversion of the disease monitoring data, weather data, and public opinion data.
  • disease surveillance data, weather data, and public opinion data are standardized so that disease surveillance data, weather data, and public opinion data have a consistent standard format to fit the input data as a GRU model.
  • the building unit 305 is configured to construct a Gated Recurrent Unit Recurrent Neural Network model, that is, a multi-layer GRU model.
  • the multi-layer GRU model includes two layers of GRU unit layers and one layer of fully connected layers, and the first layer of GRU unit layers is used to construct features for input data (eg, input data composed of disease monitoring data, weather data, and public opinion data)
  • Obtaining a first hidden layer unit where the second layer GRU unit layer is configured to combine the first hidden layer unit to obtain a second hidden layer unit, where the fully connected layer is used according to the second hidden layer
  • the unit obtains prediction results (eg, disease prediction results), and each GRU unit layer includes a reset gate and an update gate that controls the memory state of the GRU unit layer.
  • the GRU model is a time recurrent neural network model. Compared with the traditional Recurrent Neural Network (RNN) model, the GRU model stores information by constructing some gates at the GRU unit layer, so the gradient does not disappear quickly during the model training.
  • RNN Recurrent Neural Network
  • the multi-layer GRU model used in the method includes two layers of GRU unit layers and one layer of fully connected layers, and the first layer of GRU unit layers is used to construct features for input data (such as disease monitoring data, weather data, and input data composed of public opinion data).
  • Obtaining a first hidden layer unit wherein the second layer GRU unit layer is configured to combine the first hidden layer units to obtain a second hidden layer unit.
  • the fully connected layer obtains a predicted value according to the second hidden layer unit.
  • the first hidden layer unit is a local feature
  • the second hidden layer unit is a global feature. That is, the first layer GRU unit layer is used to extract local information, and the second layer GRU unit layer is used to combine global features to obtain global features, and the fully connected layer is used to obtain prediction results according to global features (eg, disease prediction results). .
  • the GRU unit layer includes an update gate z t and a reset gate r t .
  • the update gate z t is a logic gate that updates the hidden layer unit h t .
  • Reset gate r t decides to choose candidate hidden layer unit When to discard the previous hidden layer unit h t .
  • the update gate z t of the GRU unit layer, the reset gate r t , and the candidate hidden layer unit And the hidden layer unit h t is calculated as follows:
  • r t ⁇ (W r x t +U r h t-1 +b r ).
  • ⁇ is the Sigmoid activation function
  • tanh is the Tanh activation function
  • W z , U z , b z are the parameters of the update gate z t
  • W r , U r , b r are the parameters of the reset gate r t
  • W, U , b is a candidate hidden layer unit Parameters.
  • the optimization unit 306 is configured to obtain training data and verification data from the pre-processed disease monitoring data, weather data, and public opinion data, and use the training data and the verification data to train the multi-layer GRU model and Performance verification, optimized multi-layer GRU model.
  • the time series data may be intercepted from the disease monitoring data, the weather data, and the public opinion data after the pre-processing to constitute the training data and the verification data.
  • the input data of the multi-layer GRU model is a vector of a preset dimension (for example, 1000 dimensions).
  • the pre-processed disease monitoring data, weather data and public opinion data corresponding to each time point may be constructed into a preset dimension vector from the intercepted time series data, and the vectors corresponding to the respective time points are sequentially input into the time sequence.
  • a multi-layer GRU model is used to train or verify the multi-layer GRU model.
  • first time series data for training the multi-layer GRU model For example, intercepting first time series data for training the multi-layer GRU model from the pre-processed disease monitoring data, weather data, and public opinion data; each time point from the intercepted first time series data Corresponding pre-processed disease monitoring data, weather data, and public opinion data construct a first vector of a preset dimension, and sequentially input the first vector corresponding to each time point into the multi-layer GRU model in time sequence, for The multi-layer GRU model is trained.
  • the pre-processed disease monitoring data, the weather data, and the public opinion data construct a second vector of a preset dimension, and sequentially input the second vector corresponding to each time point into the multi-layer GRU model in time sequence, for Multi-layer GRU model for verification.
  • the loss function of the multi-layer GRU model may be defined as a mean square error, and the parameters of the multi-layer GRU model are adjusted such that the mean square error takes a minimum value.
  • the training process can use the RMSprop algorithm.
  • RMSprop is an improved random gradient descent algorithm.
  • the mean square error and RMSprop algorithm are prior art and will not be described here.
  • the predicting unit 307 is configured to obtain disease monitoring data, weather data, and public opinion data before the predicted time point from the pre-processed disease monitoring data, weather data, and public opinion data, and the disease monitoring data before the predicted time point.
  • the weather data and the public opinion data are input into the optimized multi-layer GRU model to obtain a disease prediction result at the predicted time point.
  • the disease monitoring data, weather data, and public opinion data before the predicted time point are obtained as time series data.
  • the disease monitoring data, the weather data and the public opinion data before the predicted time point are obtained, and the pre-processed disease monitoring data, the weather data and the public opinion data corresponding to each time point are constructed into a third vector of a preset dimension. In a chronological order, the third vector corresponding to each time point is sequentially input to the multi-layer GRU model to perform disease prediction on the predicted time point.
  • the optimized multi-layer GRU model obtains the hidden layer units of the current time point through the input data of the current time point and the hidden layer unit of the previous time point, according to the current
  • the hidden layer unit at the time point obtains the predicted value of the current time point, and continuously recursively acquires the hidden layer unit of the next time point and the predicted value according to the chronological order until the predicted value of the given time point is obtained.
  • Example 3 predicts disease data by a multi-layer GRU model.
  • the GRU model can extract knowledge directly from the data, construct a feature vector that is favorable for prediction, and improve the prediction accuracy.
  • the weather data and the public opinion data are included as influence factors in the disease prediction, and the accuracy of the disease prediction is improved.
  • the GRU model compared with the disease prediction method based on LSTM (Long Short-term Memory) model, the GRU model has a simple structure and can be quickly optimized to speed up the entire disease prediction process. Therefore, the third embodiment achieves a fast and high accuracy disease prediction.
  • FIG. 4 is a detailed structural diagram of a second acquisition unit (ie, 302 in FIG. 3) in the disease prediction apparatus provided in Embodiment 4 of the present application.
  • the second obtaining unit 302 can capture the weather data through a web crawler by using an API interface opened by the weather information website.
  • the second obtaining unit 302 may include: a generating subunit 3021, a requesting subunit 3022, an analyzing subunit 3023, a determining subunit 3024, a grabbing subunit 3025, and a storing subunit 3026.
  • a generating subunit 3021 is configured to generate a seed URL for the API interface of the weather information website and a subsequent URL.
  • the seed URL is the basis and premise for the web crawler to do everything.
  • the seed URL can be one or more.
  • the structural characteristics of the URL of the weather information website can be analyzed, and the subsequent URLs are obtained according to the structural characteristics of the URL.
  • the requesting subunit 3022 is configured to send an HTTP request to the API interface of the weather information website to request access to the API interface.
  • the HTTP request can be sent to the API interface of the weather information website in GET mode.
  • an HTTP response is returned to inform that the weather data can be acquired.
  • the analyzing subunit 3023 is configured to analyze and identify the data content provided by the weather information website to view the data content.
  • the weather information website provides data content in a specific format, and needs to analyze and identify the data content in a specific format provided by the weather information website to view the data content.
  • the data format provided by the API interface of the weather information website is in JSON format.
  • JSON is a data exchange format that uses a grammar convention similar to C.
  • the data content of the JSON format is analyzed and identified to view the data content.
  • the determining subunit 3024 is configured to determine whether the data content is a predetermined information content.
  • the data content is a predetermined information content. If the data content is not the predetermined information content, the data content is discarded, otherwise the next step is performed.
  • the capture subunit 3025 is configured to capture the data content if the data content is a predetermined information content.
  • a depth-first search strategy may be used for the state space search when the data content is captured.
  • the storage subunit 3026 is configured to save the captured data content as the weather data to the local.
  • a database can be created on the computing device to save the weather data to the database.
  • the traditional web crawler first sets one or more portal URLs.
  • a new URL is extracted from the current webpage into the queue, so as to obtain the webpage content corresponding to the URL. , save the content of the webpage to the local, and then extract the effective address as the next entry URL until the crawl is completed.
  • the second obtaining unit 302 uses the API interface opened by the weather information website to capture the weather data through the web crawler, thereby avoiding downloading irrelevant web pages and efficiently acquiring weather data, thereby improving the efficiency of disease prediction.
  • FIG. 5 is a schematic diagram of a computer apparatus according to Embodiment 5 of the present application.
  • the computer device 1 includes a memory 20, a processor 30, and a computer program 40, such as a disease prediction program, stored in the memory 20 and executable on the processor 30.
  • the processor 30 executes the computer program 40 to implement the steps in the above-described disease prediction method embodiment, such as steps 101-107 shown in FIG.
  • the processor 30, when executing the computer program 40, implements the functions of the various modules/units in the above-described apparatus embodiments, such as units 301-307 in FIG.
  • the computer program 40 can be partitioned into one or more modules/units that are stored in the memory 20 and executed by the processor 30 to complete This application.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing a particular function for describing the execution of the computer program 40 in the computer device 1.
  • the computer program 40 may be divided into a first obtaining unit 301, a second obtaining unit 302, a third obtaining unit 303, a pre-processing unit 304, a building unit 305, an optimizing unit 306, and a predicting unit 307 in FIG.
  • the computer program 40 may be divided into a first obtaining unit 301, a second obtaining unit 302, a third obtaining unit 303, a pre-processing unit 304, a building unit 305, an optimizing unit 306, and a predicting unit 307 in FIG.
  • the third embodiment For the specific functions of each unit, refer to the third embodiment.
  • the processor 30 may be a central processing unit (CPU), or may be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor 30 may be any conventional processor or the like, and the processor 30 is a control center of the computer device 1, and connects the entire computer device 1 by using various interfaces and lines. Various parts.
  • the memory 20 can be used to store the computer program 40 and/or modules/units by running or executing computer programs and/or modules/units stored in the memory 20, and by calling in memory.
  • the data within 20 implements various functions of the computer device 1.
  • the memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be Data (such as audio data, phone book, etc.) created according to the use of the computer device 1 is stored.
  • the memory 20 may include a high-speed random access memory, and may also include a non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a secure digital (Secure Digital, SD).
  • a non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a secure digital (Secure Digital, SD).
  • SMC smart memory card
  • SD Secure Digital
  • Card flash card, at least one disk storage device, flash device, or other volatile solid state storage device.
  • the modules/units integrated by the computer device 1 can be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the present application implements all or part of the processes in the foregoing embodiments, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a non-volatile readable storage medium.
  • the computer program when executed by the processor, implements the steps of the various method embodiments described above.
  • the computer program comprises computer program code, which may be in the form of source code, object code form, executable file or some intermediate form.
  • the non-transitory readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read- Only Memory), Random Access Memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media.
  • ROM Read Only Memory
  • RAM Random Access Memory
  • the contents of the non-volatile readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, Volatile readable media does not include electrical carrier signals and telecommunication signals.

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Abstract

Un procédé de prédiction de maladie consiste à : acquérir des données de surveillance de maladie (101), des données météorologiques (102) et des données d'opinion publique (103); prétraiter les données de surveillance de maladie, les données météorologiques et les données d'opinion publique (104); construire un modèle GRU multicouche (105); mettre en œuvre un apprentissage et une vérification de performance sur le modèle GRU multicouche de sorte à obtenir un modèle GRU multicouche optimisé (106); utiliser le modèle GRU multicouche optimisé pour effectuer une prédiction à un point temporel de prédiction, de sorte à obtenir un résultat de prédiction de maladie au point temporel de prédiction (107). La présente invention concerne en outre un dispositif de prédiction de maladie, un dispositif informatique et un support d'informations lisible, et permet de réaliser une prédiction rapide de maladie avec une précision élevée.
PCT/CN2018/099612 2018-04-11 2018-08-09 Procédé et dispositif de prédiction de maladie, dispositif informatique et support d'informations lisible WO2019196280A1 (fr)

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Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108648829A (zh) * 2018-04-11 2018-10-12 平安科技（深圳）有限公司 疾病预测方法及装置、计算机装置及可读存储介质
CN108288502A (zh) * 2018-04-11 2018-07-17 平安科技（深圳）有限公司 疾病预测方法及装置、计算机装置及可读存储介质
CN109119159B (zh) * 2018-08-20 2022-04-15 北京理工大学 一种基于快速权重机制的深度学习医疗诊断系统
CN110895926A (zh) * 2018-09-12 2020-03-20 普天信息技术有限公司 一种语音识别方法及装置
CN109558128A (zh) * 2018-10-25 2019-04-02 平安科技（深圳）有限公司 json数据解析方法、装置及计算机可读存储介质
CN109545386B (zh) * 2018-11-02 2021-07-20 深圳先进技术研究院 一种基于深度学习的流感时空预测方法及装置
CN109473180A (zh) * 2018-11-20 2019-03-15 河南省疾病预防控制中心 一种基于b/s架构的疾控机构信息系统
CN110162398B (zh) * 2019-04-11 2024-05-03 平安科技（深圳）有限公司 一种疾病分析模型的调度方法、装置及终端设备
CN110211690A (zh) * 2019-04-19 2019-09-06 平安科技（深圳）有限公司 疾病风险预测方法、装置、计算机设备及计算机存储介质
CN110379522B (zh) * 2019-07-23 2022-08-12 四川骏逸富顿科技有限公司 一种疾病流行趋势预测系统及方法
CN110610767B (zh) * 2019-08-01 2023-06-02 平安科技（深圳）有限公司 发病率监测方法、装置、设备及存储介质
CN110675959B (zh) * 2019-08-19 2023-07-07 平安科技（深圳）有限公司 数据智能分析方法、装置、计算机设备及存储介质
CN110767279A (zh) * 2019-10-21 2020-02-07 山东师范大学 基于lstm的电子健康记录缺失数据补全方法及系统
CN110993114B (zh) * 2019-11-26 2023-06-27 泰康保险集团股份有限公司 医疗数据分析方法及装置、存储装置、电子设备
CN111430040A (zh) * 2020-03-03 2020-07-17 广东省公共卫生研究院 基于病例、气象和病原监测数据的手足口病疫情预测方法
CN111696674B (zh) * 2020-06-12 2023-09-08 电子科技大学 一种电子病历的深度学习方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090157422A1 (en) * 2007-12-17 2009-06-18 Boris Gorbis Internet-based balascopy-information exchange system
CN106327240A (zh) * 2016-08-11 2017-01-11 中国船舶重工集团公司第七0九研究所 一种基于gru神经网络的推荐方法和系统
CN106897404A (zh) * 2017-02-14 2017-06-27 中国船舶重工集团公司第七0九研究所 一种基于多gru层神经网络的推荐方法与系统
CN107563122A (zh) * 2017-09-20 2018-01-09 长沙学院 基于交织时间序列局部连接循环神经网络的犯罪预测方法
CN107590168A (zh) * 2016-07-08 2018-01-16 百度（美国）有限责任公司 用于关系推断的系统和方法
CN108288502A (zh) * 2018-04-11 2018-07-17 平安科技（深圳）有限公司 疾病预测方法及装置、计算机装置及可读存储介质

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005063456A (ja) * 2004-10-07 2005-03-10 Mitsui Sumitomo Insurance Co Ltd 疾患症状予測サーバ、疾患症状予測システム、疾患症状予測方法、及びプログラム
JP2013092929A (ja) * 2011-10-26 2013-05-16 Seiko Epson Corp 病気予測システム及び病気予測方法
CN105678080A (zh) * 2016-01-11 2016-06-15 浪潮集团有限公司 通过大数据搜寻分析预测流感爆发可能性的方法
CN105812463A (zh) * 2016-03-10 2016-07-27 深圳市前海安测信息技术有限公司 基于医疗大数据的疾病预警系统及方法
KR101846951B1 (ko) * 2017-02-22 2018-04-09 주식회사 씨씨앤아이리서치 Copd 급성 악화 예측용 애플리케이션

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090157422A1 (en) * 2007-12-17 2009-06-18 Boris Gorbis Internet-based balascopy-information exchange system
CN107590168A (zh) * 2016-07-08 2018-01-16 百度（美国）有限责任公司 用于关系推断的系统和方法
CN106327240A (zh) * 2016-08-11 2017-01-11 中国船舶重工集团公司第七0九研究所 一种基于gru神经网络的推荐方法和系统
CN106897404A (zh) * 2017-02-14 2017-06-27 中国船舶重工集团公司第七0九研究所 一种基于多gru层神经网络的推荐方法与系统
CN107563122A (zh) * 2017-09-20 2018-01-09 长沙学院 基于交织时间序列局部连接循环神经网络的犯罪预测方法
CN108288502A (zh) * 2018-04-11 2018-07-17 平安科技（深圳）有限公司 疾病预测方法及装置、计算机装置及可读存储介质

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