CN111326261A - Upper respiratory disease prediction system based on meteorological data and prediction method thereof - Google Patents
Upper respiratory disease prediction system based on meteorological data and prediction method thereof Download PDFInfo
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Abstract
The invention provides a meteorological data-based upper respiratory disease prediction system and a prediction method thereof, wherein the meteorological data-based upper respiratory disease prediction system comprises a data preprocessing unit, a model construction unit and a derivation prediction unit; the data preprocessing unit collects regional historical meteorological information and upper respiratory disease knowledge, normalizes the meteorological information and constructs a historical data knowledge base; the model construction unit constructs a multiple linear regression model of the upper respiratory tract diseases; the deduction and prediction unit inputs the current meteorological information, the regional population age distribution information and the seasonal information of the region, and after normalization processing is carried out on the information, the constructed multiple linear regression model of the upper respiratory tract diseases is used for obtaining the morbidity trend of the upper respiratory tract diseases in the current meteorological condition and the season and providing prevention information for susceptible people.
Description
Technical Field
The invention relates to the technical field of meteorological service, in particular to an upper respiratory disease prediction system based on meteorological data and a prediction method thereof.
Background
With the attention of people on disease prevention and healthy lifestyle, the influence of climate change on human health is also concerned, and many researches show that meteorological factors and diseases have important influence. People have observed the prevalence of different diseases in the four seasons as early as 5 th century ago. Xixiao xiao ji (Zhou Li) recorded in Zuo Guo Shi, Xiao Jie Ji in summer, Dian Han Ji in autumn and Shu Shang Qi Ji in winter. The relation between infectious diseases and climate is also recognized, such as' the disease is more serious in autumn in Meng Chun, more diseases in summer in spring, more malaria in summer in Meng Qiu, etc. There is a certain inherent link between meteorological factors and the occurrence of disease. The external climate conditions change periodically, such as four seasons, day and night rotation, or non-periodic drastic change, such as strong wind, frost, rain and snow, etc., which act on the human body and cause various complex reactions in the body, when the climate conditions change greatly or exceed the adaptability of the human body, the immunity of the human body can be directly affected, and simultaneously, meteorological factors can also induce or make a certain disease worsen, such as cardiovascular and cerebrovascular diseases or respiratory diseases.
Cardiovascular and cerebrovascular diseases, respiratory diseases and the like are main diseases which are harmful to human health, and the disease incidence condition of climate sensitive diseases in a period is predicted by observing weather change, so that early warning and prevention are conveniently carried out, medical resources are reasonably distributed, and the pain of patients is reduced.
Disclosure of Invention
In view of the above, the present invention provides a meteorological data-based upper respiratory disease prediction system and a prediction method thereof, which are established according to historical meteorological data and outpatient number.
The technical scheme of the invention is realized as follows:
in one aspect, the present invention provides a system for predicting upper respiratory tract diseases based on meteorological data, which is characterized in that: the device comprises a data preprocessing unit, a model building unit and a derivation prediction unit;
the data preprocessing unit collects regional historical meteorological information and upper respiratory disease knowledge, normalizes the meteorological information and constructs a historical data knowledge base;
the model construction unit constructs a multiple linear regression model of the upper respiratory tract diseases;
the derivation and prediction unit inputs the current meteorological information of the region, the age distribution information of the population of the region and the seasonal information, and obtains the morbidity trend of the upper respiratory tract diseases under the current meteorological condition and in the season by utilizing the constructed multiple linear regression model of the upper respiratory tract diseases after normalization processing of the information.
On the basis of the technical scheme, preferably, the historical data knowledge base comprises a meteorological data sub-base, a disease knowledge sub-base, a season and disease relation sub-base and an age and disease relation sub-base; the weather data sub-database records data of historical weather information; the disease knowledge sub-base records the name, symptoms, preventive measures and regional historical medical records of the upper respiratory disease; recording information of upper respiratory tract diseases related to seasons by a season and disease relation sub-library; the age and disease relation sub-library records information of upper respiratory tract diseases related to ages; the weather data sub-base, the disease knowledge sub-base, the season and disease relation sub-base and the age and disease relation sub-base carry out normalization processing on the information in the respective bases and store the information according to the time sequence.
Further preferably, the weather data sub-library records air temperature, humidity, wind speed, precipitation, sunshine hours, air temperature day difference and air pollutant concentration data in historical weather information.
More preferably, the constructing of the multiple linear regression model of the upper respiratory disease is to construct the multiple linear regression model by respectively using the temperature, the humidity, the wind speed, the precipitation, the sunshine hours, the daily temperature difference and the air pollutant concentration which influence the pathogenesis as independent variables x1-x7 and using the pathogenesis index of the upper respiratory disease as a variable y:
y＝(b1x1+b2x2+b3x3+b4x4+b5x5+b6x6+b7x7)+e；
in the formula, b1-b7 is a regression coefficient of independent variables x1-x7, b 1-0.934578, b 2-0.025487, b 3-0.357542, b 4-0.062357, b 5-0.242151, b 6-0.606743, and b 7-0.056755; and e is a correction value.
Still more preferably, the correction value e includes an age correction value e1 and a season correction value e2, and e1+ e2, and the age correction value e1 and the season correction value e2 both have a value range of [0,1 ].
Further preferably, the method further comprises a model optimization unit; the model optimization unit adopts a K-nearest neighbor algorithm from a historical data knowledge base through input arguments x1-x7 to find K instances which are simultaneously closest to the input arguments x1-x7, wherein K is less than 20.
In another aspect, the present invention provides a method for predicting an upper respiratory disease prediction system based on meteorological data, comprising: the prediction method comprises the following steps:
s1: the method comprises the steps of configuring a data preprocessing unit, a model building unit, a derivation prediction unit and a model optimization unit;
s2: the data preprocessing unit acquires historical meteorological information and upper respiratory disease knowledge of regions of the past year from the open meteorological data and constructs a historical data knowledge base;
s3: the model construction unit is used for respectively constructing a multiple linear regression model of the upper respiratory disease incidence index y by using the air temperature, the humidity, the air speed, the precipitation, the sunshine hours, the air temperature day difference and the air pollutant concentration which influence the incidence as independent variables x1-x7, partial regression coefficients b1-b7 and a corrected value e;
s4: optimizing a multiple linear regression model of the upper respiratory disease incidence index y by a K nearest neighbor algorithm through a model optimization unit, and determining the optimal value range of independent variables x1-x7 of a corresponding region;
s5: and predicting the incidence index of the upper respiratory tract disease by the multiple linear regression model y of the incidence index y of the upper respiratory tract disease according to the current meteorological information of the region, and providing prediction information for susceptible people of corresponding age groups as a result.
Compared with the prior art, the upper respiratory disease prediction system based on meteorological data and the prediction method thereof provided by the invention have the following beneficial effects:
(1) according to the method, a large amount of regional historical meteorological information is collected, a historical data knowledge base and a multiple linear regression model of the upper respiratory tract diseases are constructed, and the disease incidence indexes of the upper respiratory tract diseases caused by climate factors in the region are predicted in advance;
(2) the historical data knowledge base is used for respectively classifying and storing meteorological data, disease knowledge, the relation between seasons and diseases and the relation between ages and diseases;
(3) the multivariate linear regression model of the upper respiratory disease is analyzed by taking the temperature, humidity, wind speed, precipitation, sunshine duration, temperature day difference and air pollutant concentration which influence the onset of the disease as variables, so that the reliability is higher;
(4) the model optimization unit can optimize the multiple linear regression model, determine a more accurate independent variable value range corresponding to the region, and improve the reliability of prediction through repeated training and learning.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a system diagram of an upper respiratory tract disease prediction system and a prediction method thereof based on meteorological data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in FIG. 1, the invention provides a system for predicting upper respiratory tract diseases based on meteorological data, which comprises a data preprocessing unit, a model construction unit, a derivation prediction unit and a model optimization unit;
the data preprocessing unit collects regional historical meteorological information and upper respiratory disease knowledge, normalizes the meteorological information and constructs a historical data knowledge base;
the model construction unit constructs a multiple linear regression model of the upper respiratory tract diseases;
the derivation and prediction unit inputs the current meteorological information of the region, the age distribution information of the population of the region and the seasonal information, and obtains the morbidity trend of the upper respiratory tract diseases under the current meteorological condition and in the season by utilizing the constructed multiple linear regression model of the upper respiratory tract diseases after normalization processing of the information.
It has long been observed that meteorological conditions and seasonal changes can affect the onset of disease. When the periodic change or mutation of the outside climate acts on a human body, various complex reactions can be caused in the human body; when the weather is changed greatly or exceeds the adaptability of the human body, the physical, behavioral, psychological, work efficiency and mental states of the human body can be directly influenced. The normalization process adjusts the range value to a value between 0 and 1, which is convenient for subsequent calculation; the smaller the normalized value is, the closer to the lower limit of the area history record is, and the larger the normalized value is, the closer to the upper limit of the area history record is.
In the scheme, the historical data knowledge base comprises a meteorological data sub-base, a disease knowledge sub-base, a season and disease relation sub-base and an age and disease relation sub-base; the weather data sub-database records data of historical weather information; the disease knowledge sub-base records the name, symptoms, preventive measures and regional historical medical records of the upper respiratory disease; recording information of upper respiratory tract diseases related to seasons by a season and disease relation sub-library; the age and disease relation sub-library records information of upper respiratory tract diseases related to ages; the weather data sub-base, the disease knowledge sub-base, the season and disease relation sub-base and the age and disease relation sub-base carry out normalization processing on the information in the respective bases and store the information according to the time sequence.
And the weather data sub-library records the air temperature, humidity, wind speed, precipitation, sunshine hours, air temperature day difference and air pollutant concentration data in the historical weather information. The weather data sub-base is based on the data table of Chinese ground climate data month value of the past year published by the Chinese weather data network, records the temperature, humidity, wind speed, precipitation, sunshine duration and weather day difference of each region from 1951 to the present, namely the difference between the highest value and the lowest value of the weather temperature and the concentration data of air pollutants at the present day, and obtains the number between 0 and 1 by normalizing the data. The data of temperature, humidity, wind power, precipitation, sunshine duration and air pressure can be selected from partial time periods, such as historical meteorological data between 1980 and 2010 are contained in a meteorological database.
The disease knowledge sub-base is used for storing names, symptoms and preventive measures of the upper respiratory diseases and regional historical medical records in a period, and the time of the regional historical medical records can be the same as the time period of the historical meteorological data.
The season and disease relation sub-library is used for recording the upper respiratory tract disease information with multiple seasons and seasonal changes; such as cold and cough in spring and winter.
The age-disease relation sub-library is used for recording and storing upper respiratory disease information related to age, such as diseases susceptible to the old and children with poor resistance, diseases susceptible to the young and the middle-aged, corresponding age group information and the like.
The construction of the multiple linear regression model of the upper respiratory disease is characterized in that the temperature, the humidity, the wind speed, the precipitation, the sunshine hours, the daily temperature difference and the air pollutant concentration in a meteorological database influencing the pathogenesis are respectively used as independent variables x1-x7, the pathogenesis index of the upper respiratory disease is used as a variable y, and the multiple linear regression model is constructed:
y＝(b1x1+b2x2+b3x3+b4x4+b5x5+b6x6+b7x7)+e；
in the formula, b1-b7 is a regression coefficient of independent variables x1-x7, b 1-0.934578, b 2-0.025487, b 3-0.357542, b 4-0.062357, b 5-0.242151, b 6-0.606743, and b 7-0.056755; and e is a correction value. b1 is the effect of y for each increment of x1 by one unit when x2-x7 are fixed, i.e. b1 is the regression coefficient of x1 to y; the other regression coefficients are the same. The multiple linear regression model is constructed by selecting 7 independent variables which are greatly related to the upper respiratory disease. Negative values of the regression coefficient indicate that the negative correlation with the index of the upper respiratory disease, such as lower temperature, higher wind speed, lower sunshine and higher concentration of the control pollutants, is more likely to cause the corresponding upper respiratory disease. The upper respiratory tract disease incidence index as variable y is generally as small as possible.
The correction value e includes an age correction value e1 and a season correction value e2, and e is e1+ e2, and the age correction value e1 and the season correction value e2 both have a value range of [0,1 ].
The invention also comprises a model optimization unit; the model optimization unit uses a K-nearest neighbor algorithm from the historical data knowledge base by cycling through the input arguments x1-x7 to find K instances that are simultaneously closest to the input arguments x1-x7, where K < 20. The method can be used for further optimizing and training the multiple linear regression model, accurately limits the value range of the input independent variable x1-x7 in a specific region, and improves the reliability of the multiple linear regression model.
In addition, the invention also provides a forecasting method of the upper respiratory tract disease forecasting system based on meteorological data, and the forecasting method comprises the following steps:
s1: the method comprises the steps of configuring a data preprocessing unit, a model building unit, a derivation prediction unit and a model optimization unit;
s2: the data preprocessing unit acquires historical meteorological information and upper respiratory disease knowledge of regions of the past year from the open meteorological data and constructs a historical data knowledge base;
s3: the model construction unit is used for respectively constructing a multiple linear regression model of the upper respiratory disease incidence index y by using the air temperature, the humidity, the air speed, the precipitation, the sunshine hours, the air temperature day difference and the air pollutant concentration which influence the incidence as independent variables x1-x7, partial regression coefficients b1-b7 and a corrected value e;
s4: optimizing a multiple linear regression model of the upper respiratory disease incidence index y by a K nearest neighbor algorithm through a model optimization unit, and determining the optimal value range of independent variables x1-x7 of a corresponding region;
s5: and predicting the incidence index of the upper respiratory tract disease by the multiple linear regression model y of the incidence index y of the upper respiratory tract disease according to the current meteorological information of the region, and providing prediction information for susceptible people of corresponding age groups as a result.
Based on the outcome of the incidence index of upper respiratory tract diseases, preventive measures can be classified:
when the upper respiratory disease incidence index y is less than 0.9, the lower respiratory disease incidence rate is indicated, and the temperature is suitable for outgoing activities;
when the disease index y of the upper respiratory tract disease is less than 0.9 and less than 1.29, the patient has strong wind or moderate cooling, and needs to keep warm;
when the upper respiratory disease incidence index y is more than 1.30, the incidence rate of the upper respiratory disease is high, and people who are susceptible need to reduce going out and pay attention to heat preservation;
when the upper respiratory disease incidence index y is more than 2, the disease incidence rate of the upper respiratory disease is very high, the temperature reduction or temperature difference is large, the patients are susceptible in all ages, and outdoor activities are reduced.
Related people can deal with the upper respiratory tract disease in advance according to the upper respiratory tract disease incidence index y, so that the incidence probability is reduced, and the life quality is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A system for upper respiratory disease prediction based on meteorological data, comprising: the device comprises a data preprocessing unit, a model building unit and a derivation prediction unit;
the data preprocessing unit collects regional historical meteorological information and upper respiratory disease knowledge, normalizes the meteorological information and constructs a historical data knowledge base;
the model construction unit constructs a multiple linear regression model of the upper respiratory tract diseases;
the derivation and prediction unit inputs the current meteorological information of the region, the age distribution information of the population of the region and the seasonal information, and obtains the morbidity trend of the upper respiratory tract diseases under the current meteorological condition and in the season by utilizing the constructed multiple linear regression model of the upper respiratory tract diseases after normalization processing of the information.
2. The meteorological-data-based upper respiratory disease prediction system of claim 1, wherein: the historical data knowledge base comprises a meteorological data sub-base, a disease knowledge sub-base, a season and disease relation sub-base and an age and disease relation sub-base; the weather data sub-database records data of historical weather information; the disease knowledge sub-base records the name, symptoms, preventive measures and regional historical medical records of the upper respiratory disease; recording information of upper respiratory tract diseases related to seasons by a season and disease relation sub-library; the age and disease relation sub-library records information of upper respiratory tract diseases related to ages; the weather data sub-base, the disease knowledge sub-base, the season and disease relation sub-base and the age and disease relation sub-base carry out normalization processing on the information in the respective bases and store the information according to the time sequence.
3. The meteorological-data-based upper respiratory disease prediction system of claim 2, wherein: and the weather data sub-database records the air temperature, humidity, wind speed, precipitation, sunshine duration, air temperature day difference and air pollutant concentration data in historical weather information.
4. The meteorological-data-based upper respiratory disease prediction system of claim 3, wherein: the construction of the multiple linear regression model of the upper respiratory disease is characterized in that the multiple linear regression model is constructed by respectively taking the temperature, the humidity, the wind speed, the precipitation, the sunshine duration, the temperature day difference and the air pollutant concentration which influence the onset of the disease as independent variables x1-x7 and taking the onset index of the upper respiratory disease as a variable y:
y＝(b1x1+b2x2+b3x3+b4x4+b5x5+b6x6+b7x7)+e；
in the formula, b1-b7 is a partial regression coefficient of an independent variable x1-x7, b 1-0.934578, b 2-0.025487, b 3-0.357542, b 4-0.062357, b 5-0.242151, b 6-0.606743, b 7-0.056755; and e is a correction value.
5. The meteorological-data-based upper respiratory disease prediction system of claim 4, wherein: the correction value e includes an age correction value e1 and a season correction value e2, and e is e1+ e2, and the age correction value e1 and the season correction value e2 both have a value range of [0,1 ].
6. The meteorological-data-based upper airway disease prediction system of claim 5, wherein: the system also comprises a model optimization unit; the model optimization unit finds K instances which are closest to the input independent variables x1-x7 at the same time by inputting the independent variables x1-x7 and adopting a K nearest neighbor algorithm from a historical data knowledge base, and determines the value range of the independent variables x1-x7 of the corresponding region, wherein K is less than 20.
7. A prediction method of an upper respiratory tract disease prediction system based on meteorological data is characterized in that: the prediction method comprises the following steps:
s1: the method comprises the steps of configuring a data preprocessing unit, a model building unit, a derivation prediction unit and a model optimization unit;
s2: the data preprocessing unit acquires historical meteorological information and upper respiratory disease knowledge of regions of the past year from the open meteorological data and constructs a historical data knowledge base;
s3: the model construction unit is used for respectively constructing a multiple linear regression model of the upper respiratory disease incidence index y by using the air temperature, the humidity, the air speed, the precipitation, the sunshine hours, the air temperature day difference and the air pollutant concentration which influence the incidence as independent variables x1-x7, partial regression coefficients b1-b7 and a corrected value e;
s4: optimizing a multiple linear regression model of the upper respiratory disease incidence index y by a K nearest neighbor algorithm through a model optimization unit, and determining the optimal value range of independent variables x1-x7 of a corresponding region;
s5: and predicting the incidence index of the upper respiratory tract disease by the multiple linear regression model y of the incidence index y of the upper respiratory tract disease according to the current meteorological information of the region, and providing prediction information for susceptible people of corresponding age groups as a result.
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