WO2023038363A1 - Appareil de diagnostic de la rhinite, procédé et support d'enregistrement - Google Patents

Appareil de diagnostic de la rhinite, procédé et support d'enregistrement Download PDF

Info

Publication number
WO2023038363A1
WO2023038363A1 PCT/KR2022/013025 KR2022013025W WO2023038363A1 WO 2023038363 A1 WO2023038363 A1 WO 2023038363A1 KR 2022013025 W KR2022013025 W KR 2022013025W WO 2023038363 A1 WO2023038363 A1 WO 2023038363A1
Authority
WO
WIPO (PCT)
Prior art keywords
rhinitis
variable
model
information
patient
Prior art date
Application number
PCT/KR2022/013025
Other languages
English (en)
Korean (ko)
Inventor
이화영
Original Assignee
가톨릭대학교 산학협력단
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 가톨릭대학교 산학협력단 filed Critical 가톨릭대학교 산학협력단
Publication of WO2023038363A1 publication Critical patent/WO2023038363A1/fr

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present embodiments provide rhinitis diagnosis apparatus, method and recording medium.
  • rhinitis can be diagnosed relatively easily because it has characteristic symptoms according to the patient's medical history. Ask well about symptoms related to the nose, and also check the relationship to the environment or job, and the weather. Even in the same allergic rhinitis, since the causative substances are all different, it is important to diagnose the patient's medical history and determine the treatment according to the severity of symptoms and environmental exposure. but. This diagnosis has a problem in that patients can receive it only by visiting a hospital every day.
  • the present embodiments can provide rhinitis diagnosis terminals, methods and recording media, methods and recording media that predict reliable rhinitis scores for rhinitis diagnosis and treatment of patients.
  • rhinitis diagnosis device significant variables are extracted from the patient's characteristic information including rhinitis severity information, environmental information and weather information through correlation analysis
  • a variable determining unit that determines a predictor variable, a model determining unit that creates a predictive model for rhinitis diagnosis based on the predictor variable, determines the predictive performance of the predictive model, and determines an optimized predictive model, and a patient corresponding to the predictor variable. It provides a rhinitis diagnostic device including a score prediction unit for predicting the patient's rhinitis score by inputting feature information into an optimized predictive model.
  • this embodiment is a method for managing rhinitis, a variable determination step of extracting a significant variable through correlation analysis from the patient's characteristic information including rhinitis severity information, environmental information and weather information and determining it as a predictor variable; A model decision step of generating a predictive model for rhinitis diagnosis based on the predictor variables and determining the optimized predictive model by determining the predictive performance of the predictive model and inputting the patient's characteristic information corresponding to the predictor variable into the optimized predictive model To provide a rhinitis diagnosis method comprising a score prediction step of predicting the patient's rhinitis score.
  • this embodiment extracts significant variables through correlation analysis from patient characteristic information including rhinitis severity information, environmental information, and weather information in a recording medium recording a program for executing a method for diagnosing rhinitis.
  • Patients corresponding to predictor variables and model determination function that generates a predictive model for rhinitis diagnosis based on the predictor variable, determines the predictive performance of the predictive model, and determines the optimized predictive model.
  • FIG. 1 is a diagram exemplarily illustrating a system configuration to which the present disclosure may be applied.
  • FIG. 2 is a diagram showing the configuration of a rhinitis diagnosis device according to an embodiment of the present disclosure.
  • Figure 3 is a flow chart for explaining the operation of rhinitis diagnosis apparatus according to an embodiment of the present disclosure to predict rhinitis score.
  • Figure 4 is a diagram for explaining the operation of rhinitis diagnosis apparatus according to an embodiment of the present disclosure determines the predictive variable.
  • Figure 5 is a diagram for explaining the operation of rhinitis diagnosis apparatus according to another embodiment of the present disclosure determines the predictive variable.
  • FIG. 6 is a diagram for explaining an operation of rhinitis diagnosis apparatus according to an embodiment of the present disclosure using a slope of weather information.
  • FIG. 7 is a diagram showing an example of inputting characteristic information of a patient in the rhinitis diagnosis apparatus according to an embodiment of the present disclosure.
  • FIG. 8 is a diagram showing an example of using weather information in the rhinitis diagnosis device according to an embodiment of the present disclosure.
  • FIG. 9 is a diagram showing an example of predicting a rhinitis score in the rhinitis diagnostic device according to an embodiment of the present disclosure.
  • FIG. 10 is a flow chart of a method for diagnosing rhinitis according to an embodiment of the present disclosure.
  • FIG. 11 is a diagram conceptually illustrating the configuration of a recording medium according to an embodiment of the present disclosure.
  • the present disclosure relates to a device for diagnosing rhinitis, a method, and a recording medium.
  • first, second, A, B, (a), and (b) may be used in describing the components of the present disclosure. These terms are only used to distinguish the component from other components, and the nature, order, or order of the corresponding component is not limited by the term.
  • an element is described as being “connected,” “coupled to,” or “connected” to another element, that element is directly connected or connectable to the other element, but there is another element between the elements. It will be understood that elements may be “connected”, “coupled” or “connected”.
  • VAS Visual Analogue Scale
  • symptom information in the present specification may mean a visual analog score in which the patient self-diagnoses the severity of nasal symptoms and checks the degree
  • SNOT-22 Tino-Nasal Outcome Test
  • Rhinitis score in the present specification is a result predicted through a predictive model based on a patient's symptom score, and may mean a score consistent with rhinitis diagnosis-related variables in a hospital.
  • the rhinitis score in the present specification may be used in the same sense as the VAS nose score (Easysum).
  • FIG. 1 is a diagram exemplarily illustrating a system configuration to which the present disclosure may be applied.
  • the present disclosure relates to a system for providing a rhinitis diagnosis device, may be implemented in the rhinitis diagnosis device 100 and the server (110).
  • Rhinitis diagnosis device 100 may include a general PC such as a general desktop or laptop, and may include a mobile terminal such as a smart phone, a tablet PC, a PDA (Personal Digital Assistants) and a mobile communication terminal, and is limited thereto. It is not, and should be broadly interpreted as any electronic device capable of communicating with the server 110.
  • a general PC such as a general desktop or laptop
  • a mobile terminal such as a smart phone, a tablet PC, a PDA (Personal Digital Assistants) and a mobile communication terminal, and is limited thereto. It is not, and should be broadly interpreted as any electronic device capable of communicating with the server 110.
  • the server 110 has the same configuration as a conventional web server (Web Server), web application server (Web Application Server), or web server (WAP Server) in terms of hardware.
  • Web Server web server
  • Web Application Server web application server
  • WAP Server web server
  • program modules that are implemented through any language such as C, C++, Java, PHP, .Net, Python, Ruby, and perform various functions. can do.
  • the server 110 may be connected to an unspecified number of clients (including the rhinitis diagnosis device 100) and/or other servers through a network. Accordingly, the server 110 receives requests from clients or other servers to perform tasks. It may mean a computer system that accepts and derives and provides work results for it, or computer software (server program) installed for such a computer system.
  • server 110 is understood as a broad concept including, in addition to the above-described server program, a series of application programs that operate on the server 110 and, in some cases, various databases built inside or outside. It should be.
  • the database may refer to an aggregate of data in which data such as information or data is structured and managed for use by a server or other device, and may also refer to a storage medium for storing such an aggregate of data.
  • a database may include a plurality of databases classified according to a data structure method, management method, type, and the like.
  • the database may include a database management system (DBMS), which is software that allows information or data to be added, corrected, or deleted.
  • DBMS database management system
  • the server 110 may store and manage contents and various types of information and data in a database.
  • the database may be implemented inside or outside the server 110 .
  • the server 110 uses server programs that are provided in various ways according to operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. It can be implemented, and as a representative example, a website, IIS (Internet Information Server) used in a Windows environment, and Apache, Nginx, Light HTTP, etc. used in a Unix environment can be used.
  • operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. It can be implemented, and as a representative example, a website, IIS (Internet Information Server) used in a Windows environment, and Apache, Nginx, Light HTTP, etc. used in a Unix environment can be used.
  • IIS Internet Information Server
  • the network 120 is a network that connects the server 110 and the rhinitis diagnosis device 100, and may be a closed network such as a LAN (Local Area Network) and a WAN (Wide Area Network), but the Internet It may be an open network such as (Internet).
  • the Internet refers to the TCP/IP protocol and various services existing in its upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), It refers to a worldwide open computer network structure that provides Simple Network Management Protocol (SNMP), Network File Service (NFS), and Network Information Service (NIS).
  • SNMP Simple Network Management Protocol
  • NFS Network File Service
  • NIS Network Information Service
  • the rhinitis diagnosis device 100 includes a mobile terminal such as a smart phone, a tablet PC, a PDA (Personal Digital Assistants) and a mobile communication terminal
  • the network is a wireless access network such as a mobile communication network or a Wi-Fi (WiFi) network. may include more.
  • Rhinitis diagnosis apparatus 100 in the present specification may refer to a terminal of a user (User) that predicts a rhinitis score by receiving characteristic information of a patient in order to diagnose rhinitis of a patient.
  • the rhinitis diagnosis device 100 may refer to a device used by a user who has been granted permission to access the predictive model determined by the server 110.
  • the rhinitis diagnosis apparatus 100 may refer to a user device that obtains the patient's characteristic information described later and transmits it to the server.
  • the rhinitis diagnosis device 100 in the present specification may connect to the server 110 to upload / download information for diagnosing rhinitis to the content platform.
  • the content platform may mean an online platform capable of predicting rhinitis scores operated or operated by the server 110 .
  • FIG. 2 is a diagram showing the configuration of a rhinitis diagnosis device according to an embodiment of the present disclosure.
  • the rhinitis diagnosis apparatus 100 which provides a method for diagnosing rhinitis according to an embodiment of the present disclosure, is significant through correlation analysis from patient characteristic information including rhinitis severity information, environmental information, and weather information.
  • a variable determining unit 210 that extracts a variable and determines it as a predictor variable, a model determining unit that generates a predictive model for diagnosing rhinitis based on the predictor variable, and determines the predictive performance of the predictive model to determine an optimized predictive model.
  • the variable determination unit 210 may extract significant variables through correlation analysis from the patient's characteristic information including rhinitis severity information, environmental information, and weather information, and determine them as predictive variables.
  • the rhinitis severity information may include at least one of SNOT-22 (Sino-Nasal Outcome Test) information, information on the presence or absence of disturbances in daily life, and VAS symptom information.
  • environmental information may include information on mold, transportation, stress, or cold air.
  • the variable determining unit 210 may receive input of patient characteristic information including rhinitis severity information and environmental information from the patient through an input interface.
  • the input interface may refer to a module or device capable of inputting information to the rhinitis diagnosis device 100, such as a touch screen, a microphone, and a button.
  • the variable determiner 210 may obtain VAS symptom information, which is self-diagnosis information about nasal symptoms input from a patient, as rhinitis severity information.
  • the variable determiner 210 may obtain information on a symptom aggravating factor input from the patient as environmental information.
  • variable determining unit 210 may receive SNOT-22 information of a patient diagnosed in a hospital from a server and obtain it as rhinitis severity information.
  • the variable determiner 210 may receive and obtain weather information according to the location information of the patient from the server.
  • the variable determiner 210 may receive temperature information, humidity information, and fine dust information of a current location using a global positioning system (GPS) to obtain weather information.
  • GPS global positioning system
  • variable determining unit 210 may determine a predictor variable suitable for the predictive model by dividing the significant variable extracted through correlation analysis with the rhinitis score of the patient into a common variable or an individual variable.
  • the variable determiner 210 may calculate a correlation coefficient for each variable of the patient's characteristic information, and classify significant variables extracted based on the correlation coefficient as common variables.
  • the common variable may mean a variable having a fixed effect.
  • the variable determination unit 210 may calculate a correlation coefficient (p) through correlation analysis (Pearson Correlation analysis) with the rhinitis score of the patient.
  • variable determining unit 210 may extract significant variables from variables having a distinct quantitative linear relationship such that the calculated correlation coefficient (p) is 0.3 or more and 0.7 or less, and classify them as common variables.
  • the variable determiner 210 may classify each of the temperature information, humidity information, and fine dust information included in the obtained weather information based on the location information of the patient as individual variables.
  • the individual variable may mean a variable having an arbitrary effect.
  • the slope of each of the temperature information, humidity information, and fine dust information for the rhinitis score may be different for each patient.
  • the variable determiner 210 may classify a random intercept as an individual variable.
  • variable determiner 210 may classify arbitrary segments as individual variables in order to consider the basic characteristics of each patient by reflecting the correlation as the distribution of rhinitis scores shows a difference between patients. This is because the predicted rhinitis score has different subjective characteristics for each individual.
  • the model determination unit 220 may generate a predictive model for diagnosing rhinitis based on predictor variables, and determine an optimized predictive model by determining the predictive performance of each predictive model.
  • the model determiner 220 may generate a plurality of predictive models including a regression model, a linear mixed model, and an ensemble machine learning model based on predictor variables. there is.
  • the model determination unit 220 may determine the prediction performance of each prediction model and determine an optimized prediction model from among a plurality of prediction models.
  • the model determiner 220 may generate a regression model that does not reflect any effect (individual variable), a linear mixed model that reflects only an arbitrary intercept, or a linear mixed model that reflects both the random intercept and the slope as a prediction model. .
  • the model determiner 220 may generate an ensemble model based on a random forest algorithm or an ensemble model based on a LightGBM algorithm as a predictive model.
  • each ensemble model may be composed of a model that does not reflect any effect (individual variable), a model that reflects only an arbitrary intercept, or a model that reflects both the random intercept and the slope.
  • the generated model is an example, and is not limited thereto.
  • the model determiner 220 may determine the predictive performance of the predictive model based on the root mean square error calculated by applying K-fold cross-validation to the patient's feature information corresponding to the predictor variable.
  • the model determination unit 220 may determine the degree of fitness of the model using Bayesian information criterion (BIC).
  • BIC Bayesian information criterion
  • the Bayes information criterion may be a numerical value used in Bayesian statistics as a criterion for selecting a model from among a plurality of models.
  • the model determiner 220 may determine an optimized prediction model using a root mean square error calculated by applying K-fold cross-validation to each of a plurality of prediction models and a Bayes information criterion.
  • the model determiner 220 may determine an ensemble model generated by applying a common variable and an individual variable to a tree-based machine learning algorithm as an optimized prediction model.
  • the score prediction unit 230 may predict the patient's rhinitis score by inputting the patient's characteristic information corresponding to the predictor variable into an optimized prediction model. For example, the score prediction unit 230 may obtain predictive validity by confirming the internal consistency of rhinitis diagnosis-related variables and the predicted rhinitis score through reliability analysis (Cronbach's alpha). At this time, variables related to rhinitis diagnosis may be variables related to SNOT-22 information performed for rhinitis diagnosis in a hospital.
  • Figure 3 is a flow chart for explaining the operation of rhinitis diagnosis apparatus according to an embodiment of the present disclosure to predict rhinitis score.
  • the variable determining unit 210 of the rhinitis diagnostic device may determine a predictor variable suitable for the predictive model by extracting a significant variable through a correlation analysis (S310). For example, the variable determination unit 210 may determine a predictor variable suitable for the predictive model by classifying the significant variable extracted through correlation analysis with the rhinitis score of the patient into a common variable or an individual variable. For example, the variable determination unit 210 may calculate a correlation coefficient (p) in the patient's rhinitis score and correlation analysis. Also, the variable determining unit 210 may extract a significant variable from variables having a distinct quantitative linear relationship such that the calculated correlation coefficient (p) is greater than or equal to 0.3 and less than or equal to 0.7.
  • a correlation coefficient (p) in the patient's rhinitis score and correlation analysis.
  • the variable determining unit 210 may extract a significant variable from variables having a distinct quantitative linear relationship such that the calculated correlation coefficient (p) is greater than or equal to 0.3 and less than or equal to 0.7.
  • the variable determination unit 210 may determine only significant variables as predictor variables by applying the linear mixed model among the extracted variables.
  • the correlation coefficient (p) calculated in the correlation analysis can be calculated as SNOT-22 information 0.66, VAS symptom score 0.45, information on the presence or absence of disturbance in daily life 0.36 included in rhinitis severity.
  • the correlation coefficient (p) can be calculated as mold 0.31, transportation 0.34, stress 0.36, and cold air 0.42 included in the environmental information.
  • the variable determining unit 210 may finally classify the SNOT-22 information, cold air, stress, and transportation information as common variables based on the correlation coefficient and determine them as predictive variables.
  • the correlation coefficient is also changed when patient data is changed.
  • variable determiner 210 may classify each of the temperature information, humidity information, and fine dust information included in the weather information as individual variables.
  • the variable determining unit 210 may have different slopes of temperature information, humidity information, and fine dust information for rhinitis scores for each patient. Accordingly, the weather information has a low correlation when considering the entire patient, but can be divided into individual variables having random effects as they are offset by different slopes. Also, the weather information may be applied to a prediction model by assuming a random slope.
  • variable determiner 210 may classify a random intercept as an individual variable.
  • the variable determiner 210 may classify the arbitrary intercept as an individual variable in order to consider the basic characteristics differently for each patient by reflecting the correlation. This is because each individual may have subjective characteristics as the patient's subjective rhinitis severity information is reflected in the predicted rhinitis score. In addition, since the distribution of rhinitis scores differs between patients, it is necessary to consider the characteristic information differently for each patient.
  • Model determination unit 220 of the rhinitis diagnosis device may generate a plurality of predictive models for rhinitis diagnosis based on the predictor variables (S320).
  • the model determiner 220 may generate a plurality of predictive models including a regression model, a linear mixed model, and an ensemble machine learning model based on predictor variables. there is.
  • the model determiner 220 may generate a regression model that does not reflect any effect (individual variable).
  • the VAS symptom information has a high correlation with the SNOT-22 information, a multicollinearity problem occurs and is insignificant, so it can be excluded from the predictor variables.
  • the model determination unit 220 is SNOT-22 information included in rhinitis severity information, information on the presence or absence of disturbances in daily life, information on mold, transportation, stress, cold air included in environmental information, and intercepts ( rintercept) as a predictor variable.
  • the model determiner 220 may generate a linear mixed model reflecting only the random intercept or a linear mixed model reflecting both the random intercept and the slope.
  • the model determining unit 220 may create a linear mixed model in consideration of the characteristic that the predictive rhinitis score reflects the subjectivity of the patient.
  • the linear mixed model is less significant than the regression model, but may be a reasonable model as an approach that assumes that each patient is different by reflecting the correlation within the patient.
  • the model determination unit 220 may generate a linear mixed model reflecting only an arbitrary intercept according to the rhinitis scores of different patient groups.
  • the variable correlated with the predicted rhinitis score is mainly environmental information, which can also be seen as a subjective factor that varies for each patient.
  • the information on fungi included in the environmental information is less significant in the corresponding linear mixed model, and considering that only a small number of patients entered as fungi as an aggravating factor, it can be excluded from the predictor variables. Therefore, the model determination unit 220 generates a regression model using the SNOT-22 information included in the rhinitis severity information, the information on transportation, stress, and cold air included in the environmental information, and the intercept as a predictor variable.
  • the model determination unit 220 may generate a linear mixed model reflecting both a random intercept and a slope as the slope of the weather information for the rhinitis score is different for each patient.
  • the model determiner 220 may generate an ensemble model based on a random forest algorithm or an ensemble model based on a LightGBM algorithm.
  • each ensemble model may be composed of a model that does not reflect any effect (individual variable), a model that reflects only an arbitrary intercept, or a model that reflects both the random intercept and the slope.
  • an ensemble model based on a random forest algorithm may be a model that outputs a final result by collecting classification or prediction results from a plurality of decision trees constructed in a training process.
  • the ensemble model based on the LightGBM algorithm may be a model using a 'leaf-wise' method of continuously splitting around a node having a maximum loss value in order to correct an error of a decision tree.
  • Model determining unit 220 of the rhinitis diagnosis apparatus may determine the predictive performance of each predictive model to determine the optimized predictive model (S330). For example, the model determiner 220 may determine the predictive performance of the predictive model based on the root mean square error calculated by applying K-fold cross-validation. For example, the model determiner 220 may calculate prediction performance k times by dividing the data into k pieces and dividing them into a training set and a validation set.
  • Overall RMSE may be a value obtained by taking the square root of K mean square errors calculated for each iteration. Table 1 shows the prediction performance of each prediction model judged using the overall RMSE.
  • the predictive performance of the predictive model differs depending on the type of random effect (individual variable) in the linear mixed model and the ensemble model.
  • the predictive performance of the model excluding random effects (individual variables) may be judged to be good.
  • the ensemble model it can be determined that the predictive performance of the model in which only the random intercept is reflected is good.
  • the model determiner 220 may determine the degree of fitness of the model using Bayesian information criterion (BIC). For example, the model determiner 220 may determine model fit using the adjusted coefficient of determination of the regression model.
  • the adjusted coefficient of determination is R 2 included in the output of the regression model, and may be an indicator for measuring whether the model explains past data well.
  • the resulting regression model calculated an adjusted coefficient of determination of 0.525, which could mean explaining 52.5% of the rhinitis score variance.
  • the linear mixed model reflecting only the random intercept can be calculated as 1826.423 based on the Bayes information criterion.
  • the linear mixed model that reflects both the random intercept and the slope is calculated as 1530.467, so it can be judged that the model fit is higher than the model that reflects only the random intercept.
  • Score prediction unit 230 of the rhinitis diagnosis device may predict the patient's rhinitis score using an optimized prediction model (S340).
  • the optimized prediction model may be an ensemble model generated by applying a tree-based machine learning algorithm to common variables and individual variables.
  • the predicted patient's rhinitis score may be a score for which consistency with variables determined from the patient's characteristic information is confirmed. The consistency reliability of the rhinitis score will be described later with reference to FIG. 9 .
  • Figure 4 is a diagram for explaining the operation of rhinitis diagnosis apparatus according to an embodiment of the present disclosure determines the predictive variable.
  • variable determination unit 210 of the rhinitis diagnosis device may explain the correlation analysis with the continuous variable.
  • the variable determining unit 210 may extract significant variables through correlation analysis of continuous variables included in the patient's rhinitis score and rhinitis severity information.
  • the variable determining unit 210 may calculate a correlation coefficient of 0.66 through correlation analysis between the rhinitis score and the SNOT-22 information.
  • the variable determining unit 210 may calculate a correlation coefficient of 0.45 through correlation analysis between the rhinitis score and the VAS symptom information.
  • the variable determiner 210 may calculate a correlation coefficient of 0.69 through correlation analysis between the SNOT-22 information and the VAS symptom information. Therefore, it can be confirmed that rhinitis score has a clear quantitative linear relationship with SNOT-22 information and VAS symptom information.
  • Figure 5 is a diagram for explaining the operation of rhinitis diagnosis apparatus according to another embodiment of the present disclosure determines the predictive variable.
  • variable determination unit 210 of the rhinitis diagnosis device may explain the correlation analysis with the discrete variable.
  • the variable determination unit 210 may perform rhinitis score and correlation analysis for each discrete variable and display it as a box plot.
  • the box plot may be a graph expressing numerical data to quickly check the range and median of the data set.
  • the variable determining unit 210 may calculate a correlation coefficient of 0.36 through correlation analysis with rhinitis scores and information about the presence or absence of disturbances in daily life input from the patient.
  • the variable determining unit 210 may calculate a correlation coefficient through a correlation analysis between the rhinitis score and each environmental information input from the patient.
  • variable determining unit 210 may calculate a correlation coefficient between the rhinitis score and mold as 0.31.
  • the variable determining unit 210 may calculate a correlation coefficient between the rhinitis score and transportation as 0.34.
  • Variable determining unit 210 may calculate the correlation coefficient between rhinitis score and stress as 0.36.
  • the variable determiner 210 may calculate a correlation coefficient between the rhinitis score and the cold air as 0.42. Therefore, it can be confirmed that the rhinitis score has a clear quantitative linear relationship with environmental information on symptom aggravating factors.
  • FIG. 6 is a diagram for explaining an operation of rhinitis diagnosis apparatus according to an embodiment of the present disclosure using a slope of weather information.
  • the variable determination unit 210 of the rhinitis diagnosis apparatus may explain the distribution and slope of rhinitis scores according to weather information for each patient.
  • the variable determining unit 210 may obtain weather information of a current location for each patient using a global navigation system (GPS) and calculate the distribution and slope of rhinitis scores according to the weather information.
  • the variable determining unit 210 may calculate the slope of temperature information, humidity information, and fine dust information for rhinitis scores for each patient. The calculated slope may have different values for each patient.
  • FIG. 6 shows the distribution and slope of rhinitis scores according to temperature information among weather information.
  • FIG. 7 is a diagram showing an example of inputting characteristic information of a patient in the rhinitis diagnosis apparatus according to an embodiment of the present disclosure.
  • the rhinitis severity information and environmental information from the rhinitis diagnosis apparatus 100 according to an embodiment of the present disclosure from the patient.
  • VAS symptom information 710 of the patient in the rhinitis diagnosis apparatus 100 is obtained by inputting the degree of symptom self-diagnosis for nasal symptoms by a patient, and may be information included in rhinitis severity information.
  • the VAS symptom information 710 may be acquired by inputting additional self-diagnosis information about asthma symptoms in addition to nasal symptoms.
  • the VAS symptom information 710 may be input by the patient at regular intervals, but collected and monitored for a specific period of time. The predetermined period may be a one-day interval, but is not limited thereto.
  • the information 720 on aggravating factors is obtained by selecting and acquiring a factor that is thought to be a cause of exacerbation of nasal symptoms by a patient, and may be information included in environmental information.
  • the information 720 on aggravating factors may include cold, fine dust, house dust, pets, mold, pollen, cold air, humidity, transportation, smell, smoking, stress, acid reflux, exercise, medication, and food. can However, it is not limited thereto.
  • FIG. 8 is a diagram showing an example of using weather information in the rhinitis diagnosis device according to an embodiment of the present disclosure.
  • weather information 810 of a patient may be obtained from the rhinitis diagnosis apparatus 100 according to an embodiment of the present disclosure.
  • the weather information 810 may be temperature information, humidity information, and fine dust (PM10) information for each patient obtained from the Korea Meteorological Administration based on a global navigation system (GPS) once a day.
  • GPS global navigation system
  • the weather information 810 can be obtained based on the location information detected using the global navigation system (GPS) of the rhinitis diagnosis apparatus 100 when a signal such as a screen touch is input from the patient.
  • FIG. 9 is a diagram showing an example of predicting a rhinitis score in the rhinitis diagnostic device according to an embodiment of the present disclosure.
  • the rhinitis score 910 may be a score output by inputting patient characteristic information into an optimized predictive model.
  • the rhinitis score 910 can confirm internal consistency with variables included in the patient's rhinitis severity information through reliability analysis (Cronbach's alpha). Accordingly, the predicted rhinitis score can obtain predictive validity as a value that can be replaced with rhinitis diagnosis.
  • the reliability coefficient is a value representing the reliability of the internal consistency of the test, and it can be analyzed whether items are composed of homogeneous elements.
  • the reliability coefficient may be calculated to be 0.7 or more. Accordingly, it can be confirmed that the consistency of four variables corresponding to rhinitis score 910, SNOT-22 information, information on the presence or absence of disturbances in daily life, and VAS symptom information is reliable.
  • the rhinitis diagnosis apparatus 100 may provide a self-management method according to the degree of control by utilizing the predicted patient's rhinitis score.
  • FIG. 10 is a flow chart of a method for diagnosing rhinitis according to an embodiment of the present disclosure.
  • the method for diagnosing rhinitis of the present disclosure may include a variable determination step of determining predictive variables (S1010).
  • the rhinitis diagnostic device may extract significant variables through correlation analysis from the patient's characteristic information including rhinitis severity information, environmental information, and weather information and determine them as predictive variables.
  • the rhinitis diagnosis apparatus may receive characteristic information of a patient including rhinitis severity information and environmental information from a patient through an input interface.
  • the rhinitis diagnosis apparatus may acquire VAS symptom information, which is self-diagnosis information about nasal symptoms input from a patient, as rhinitis severity information.
  • the variable determiner 210 may obtain information on a symptom aggravating factor input from the patient as environmental information.
  • the rhinitis diagnosis device may obtain rhinitis severity information by receiving SNOT-22 information of a patient diagnosed in a hospital from a server.
  • the rhinitis diagnosis apparatus may receive weather information according to the patient's location information from the server and obtain it as the patient's characteristic information.
  • the rhinitis diagnosis device may obtain weather information by receiving temperature information, humidity information, and fine dust information of the current location using a global navigation system (GPS).
  • GPS global navigation system
  • the rhinitis diagnosis apparatus may determine a predictor variable suitable for a predictive model by dividing a significant variable extracted through correlation analysis with a patient's rhinitis score into a common variable or an individual variable. For example, the rhinitis diagnosis device may calculate a correlation coefficient for each variable of the patient's characteristic information, and distinguish a significant variable extracted based on the calculated correlation coefficient as a common variable. Specifically, the rhinitis diagnosis device may calculate a correlation coefficient (p) from the patient's rhinitis score and correlation analysis (Pearson Correlation analysis).
  • the rhinitis diagnostic device can be divided into common variables by extracting significant variables from among variables having a clear quantitative linear relationship with a calculated correlation coefficient (p) of 0.3 or more and 0.7 or less.
  • the rhinitis diagnosis device may classify each of the temperature information, humidity information, and fine dust information included in the obtained weather information based on the patient's location information as individual variables.
  • Each of the temperature information, humidity information, and fine dust information included in the obtained weather information based on the patient's location information may be classified as individual variables.
  • a rhinitis diagnostic device may classify a random intercept as an individual variable.
  • Rhinitis diagnosis method of the present disclosure may include a model determination step of determining a predictive model (S1020).
  • the rhinitis diagnosis device may generate a predictive model for diagnosing rhinitis based on a predictor variable, and determine an optimized predictive model by determining the predictive performance of each predictive model.
  • the rhinitis diagnosis device may generate a plurality of predictive models including a regression model, a linear mixed model, and an ensemble model (Ensemble Machine Learning Model) based on predictor variables. Then, the rhinitis diagnostic device may determine the prediction performance of each predictive model to determine an optimized predictive model from among a plurality of predictive models.
  • the rhinitis diagnosis device may determine the predictive performance of the predictive model based on the root mean square error calculated by applying K-fold cross validation to the patient's characteristic information corresponding to the predictor variable.
  • rhinitis diagnosis device may determine the model fit using the Bayesian information criterion (BIC).
  • BIC Bayesian information criterion
  • the rhinitis diagnosis device may determine a predictive model optimized for a predictive model having a low value of the root mean square error and Bayesian information criterion calculated by applying K-fold cross-validation to each of a plurality of predictive models.
  • the rhinitis diagnosis device may determine an ensemble model generated by applying a common variable and an individual variable to a tree-based machine learning algorithm as an optimized predictive model.
  • Rhinitis diagnosis method of the present disclosure may include a score prediction step of predicting the rhinitis score (S1030).
  • the rhinitis diagnostic device may predict the patient's rhinitis score by inputting the patient's characteristic information corresponding to the predictor variable into an optimized predictive model.
  • the rhinitis diagnosis device can obtain predictive validity by confirming the internal consistency between the rhinitis diagnosis-related variables and the predicted rhinitis score through reliability analysis (Cronbach's alpha).
  • FIG. 11 is a diagram conceptually illustrating the configuration of a recording medium according to an embodiment of the present disclosure.
  • the recording medium 1100 recording the program for executing the method for diagnosing rhinitis extracts significant variables through correlation analysis from patient characteristic information including rhinitis severity information, environmental information, and weather information, and predicts variables.
  • the variable determining function 1110 may extract a significant variable through correlation analysis from the patient's characteristic information including rhinitis severity information, environmental information, and weather information and determine it as a predictor variable.
  • the variable determining function 1110 may determine a predictor variable suitable for a predictive model by classifying a significant variable extracted through correlation analysis with a patient's rhinitis score into a common variable or an individual variable.
  • the variable determination function 1110 may calculate a correlation coefficient for each variable of the patient's characteristic information, and classify significant variables extracted based on the correlation coefficient as common variables.
  • variable determination function 1110 classifies each of the temperature information, humidity information, and fine dust information included in the obtained weather information based on the patient's location information as individual variables, each temperature information for the rhinitis score, The slope of the humidity information and the fine dust information may be different for each patient.
  • the model determining function 1120 may generate a predictive model for diagnosing rhinitis based on predictor variables and determine an optimized predictive model by determining predictive performance of the predictive model.
  • the model determination function 1120 may generate a plurality of predictive models including a regression model, a linear mixed model, and an ensemble model based on predictor variables.
  • the model determination function 1120 may determine the prediction performance of each prediction model and determine an optimized prediction model from among a plurality of prediction models.
  • the model determination function 1120 may determine the predictive performance of the predictive model based on the root mean square error calculated by applying K-fold cross-validation to the patient's feature information corresponding to the predictor variable.
  • the model determination function 1120 may determine an ensemble model generated by applying a common variable and an individual variable to a tree-based machine learning algorithm as an optimized predictive model.
  • the rhinitis diagnosis method according to an embodiment of the present disclosure described above is implemented as an application (ie, a program) installed by default in the rhinitis diagnosis device 100 or directly installed by a user, and is readable by a computer such as the rhinitis diagnosis device 100. can be recorded on a recordable medium.
  • a program implementing the method for diagnosing rhinitis according to an embodiment of the present disclosure executes a variable determination function, a model determination function, a score prediction function, and the like. These programs can be recorded on a computer-readable recording medium and executed by a computer to execute the aforementioned functions.
  • the above-described program is a computer such as C, C ++, JAVA, machine language, etc. that the processor (CPU) of the computer can read. It may include code coded in a language.
  • These codes may include functional codes related to functions defining the above-described functions, and may include control codes related to execution procedures necessary for a processor of a computer to execute the above-described functions according to a predetermined procedure.
  • these codes may further include memory reference related codes for which location (address address) of the computer's internal or external memory should be referenced for additional information or media necessary for the computer's processor to execute the above-mentioned functions. .
  • the code is used by the computer processor to communicate with the computer's communication module (e.g., wired and/or wireless communication module). ) may further include communication-related codes for how to communicate with any other remote computer or server, and what information or media should be transmitted/received during communication.
  • the computer's communication module e.g., wired and/or wireless communication module.
  • a functional program for implementing the present disclosure codes and code segments related thereto, in consideration of the system environment of a computer that reads a recording medium and executes a program, etc. It may be easily inferred or changed by
  • the computer-readable recording medium on which the above-described program is recorded is distributed to computer systems connected through a network, so that computer-readable codes can be stored and executed in a distributed manner.
  • any one or more of the plurality of distributed computers may execute some of the functions presented above, transmit the execution results to one or more of the other distributed computers, and receive the transmitted results.
  • a computer may also execute some of the functions presented above and provide the results to other distributed computers as well.
  • a computer-readable recording medium recording a program for executing the rhinitis diagnosis method is, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical media storage devices.
  • the computer-readable recording medium recording the application which is a program for executing the rhinitis diagnosis method according to an embodiment of the present disclosure, is an application store server (Application Store Server), an application or a web server related to the service (Web Server ), etc., may be a storage medium (eg, hard disk, etc.) included in the application providing server (Application Provider Server), the application providing server itself, or another computer on which a program is recorded or its storage medium.
  • Application Store Server Application Store Server
  • Web Server web server
  • Storage medium eg, hard disk, etc.
  • a computer capable of reading a recording medium on which an application, which is a program for executing the rhinitis diagnosis method according to an embodiment of the present disclosure, is recorded, as well as a general PC such as a general desktop or laptop computer, a smart phone, a tablet PC, a PDA (Personal It may include mobile terminals such as Digital Assistants and mobile communication terminals, and should be interpreted as all devices capable of computing.
  • a general PC such as a general desktop or laptop computer
  • a smart phone such as a tablet PC
  • PDA Personal It may include mobile terminals such as Digital Assistants and mobile communication terminals, and should be interpreted as all devices capable of computing.
  • a mobile terminal such as a smart phone, tablet PC, PDA (Personal Digital Assistants) and mobile communication terminal
  • the mobile terminal may download and install the corresponding application from an application providing server including an application store server, a web server, etc.
  • the mobile device is downloaded through a synchronization program. It can also be installed in a terminal.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Data Mining & Analysis (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Theoretical Computer Science (AREA)
  • Environmental & Geological Engineering (AREA)
  • Software Systems (AREA)
  • Physiology (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Engineering & Computer Science (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Atmospheric Sciences (AREA)
  • Pulmonology (AREA)
  • Ecology (AREA)
  • Environmental Sciences (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Immunology (AREA)
  • Vascular Medicine (AREA)
  • Psychiatry (AREA)
  • Signal Processing (AREA)

Abstract

La présente divulgation concerne un appareil de diagnostic de la rhinite, un procédé, et un support d'enregistrement, et peut concerner un appareil de diagnostic de la rhinite, un procédé et un support d'enregistrement, dans lesquels un score de rhinite est prédit par utilisation individuelle d'informations caractéristiques d'un patient sans que le patient ne doive personnellement se rendre dans un hôpital. En particulier, l'invention concerne un appareil de diagnostic de la rhinite, un procédé et un support d'enregistrement pour prédire un score de rhinite fiable par génération d'un modèle de prévision pour diagnostiquer la rhinite sur la base d'une variable de prévision extraite d'informations caractéristiques d'un patient par l'intermédiaire d'une analyse de corrélation.
PCT/KR2022/013025 2021-09-07 2022-08-31 Appareil de diagnostic de la rhinite, procédé et support d'enregistrement WO2023038363A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR10-2021-0118903 2021-09-07
KR1020210118903A KR20230036261A (ko) 2021-09-07 2021-09-07 비염 진단 장치, 방법 및 기록매체

Publications (1)

Publication Number Publication Date
WO2023038363A1 true WO2023038363A1 (fr) 2023-03-16

Family

ID=85502559

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2022/013025 WO2023038363A1 (fr) 2021-09-07 2022-08-31 Appareil de diagnostic de la rhinite, procédé et support d'enregistrement

Country Status (2)

Country Link
KR (1) KR20230036261A (fr)
WO (1) WO2023038363A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101729694B1 (ko) * 2017-01-02 2017-04-25 한국과학기술정보연구원 시뮬레이션 결과 예측 방법 및 장치
KR102013831B1 (ko) * 2017-03-16 2019-08-23 사회복지법인 삼성생명공익재단 알레르기 질환 환자 맞춤형 환경 통합관리 시스템, 방법 및 기록매체
KR20190115330A (ko) * 2018-04-02 2019-10-11 주식회사 씨씨앤아이리서치 만성 호흡기 질환의 급성 악화 예측용 애플리케이션
US20200297955A1 (en) * 2015-08-26 2020-09-24 Resmed Sensor Technologies Limited Systems and methods for monitoring and management of chronic disease
WO2021063935A1 (fr) * 2019-09-30 2021-04-08 F. Hoffmann-La Roche Ag Prédiction d'état pathologique

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200297955A1 (en) * 2015-08-26 2020-09-24 Resmed Sensor Technologies Limited Systems and methods for monitoring and management of chronic disease
KR101729694B1 (ko) * 2017-01-02 2017-04-25 한국과학기술정보연구원 시뮬레이션 결과 예측 방법 및 장치
KR102013831B1 (ko) * 2017-03-16 2019-08-23 사회복지법인 삼성생명공익재단 알레르기 질환 환자 맞춤형 환경 통합관리 시스템, 방법 및 기록매체
KR20190115330A (ko) * 2018-04-02 2019-10-11 주식회사 씨씨앤아이리서치 만성 호흡기 질환의 급성 악화 예측용 애플리케이션
WO2021063935A1 (fr) * 2019-09-30 2021-04-08 F. Hoffmann-La Roche Ag Prédiction d'état pathologique

Also Published As

Publication number Publication date
KR20230036261A (ko) 2023-03-14

Similar Documents

Publication Publication Date Title
KR101846951B1 (ko) Copd 급성 악화 예측용 애플리케이션
Lombardo et al. ESSENCE II and the framework for evaluating syndromic surveillance systems.
WO2015008936A1 (fr) Appareil de diagnostic à l'aide d'habitudes, appareil de gestion du diagnostic et procédé de diagnostic l'utilisant.
WO2022169037A1 (fr) Procédé pour prédire la santé mentale et fournir des solutions de santé mentale par apprentissage de données d'indicateur psychologique et de données d'indicateur physique sur la base d'un apprentissage automatique, et dispositif d'évaluation de santé mentale l'utilisant
WO2019070200A1 (fr) Système de gestion d'informations de soins de santé
US20230409966A1 (en) Training machine learning algorithms with temporaly variant personal data, and applications thereof
WO2020119403A1 (fr) Procédé, appareil et dispositif de détection d'anomalie de données d'hospitalisation, et support d'informations lisible
WO2021215809A1 (fr) Système et procédé pour fournir un diagnostic précoce d'un trouble cognitif et d'un service de mise en correspondance de soins de communauté pour personnes âgées
CN112117002A (zh) 结合虚拟现实技术的新型智能化心理评估干预系统、方法
Schultze et al. Identifying care home residents in electronic health records-an OpenSAFELY short data report
WO2022211385A1 (fr) Système de consultation de soins de santé utilisant la distribution de valeurs de prédiction de maladie
WO2023038363A1 (fr) Appareil de diagnostic de la rhinite, procédé et support d'enregistrement
Wauben et al. Learning from aviation to improve safety in the operating room-A systematic literature review
McCarron et al. Personal exposure to fine particulate matter (PM2. 5) and self-reported asthma-related health
Beierle et al. Self-Assessment of Having COVID-19 with the Corona Check mHealth App
Cossin et al. Public Health and Epidemiology Informatics: Recent Research Trends Moving toward Public Health Data Science
Manuel et al. A commentary on the value of hospital data for covid-19 pandemic surveillance and planning
CN112711579A (zh) 医疗数据的质量检测方法及装置、存储介质及电子设备
Martinez Rebollar et al. Automatic detection of social isolation based on human behavior analysis
WO2022107902A1 (fr) Serveur et système pour collecter des informations de maladie et établir une base de données de celles-ci, et procédé de commande associé
CN112189206A (zh) 使用机器学习算法处理个人数据及其应用
WO2023033434A1 (fr) Dispositif, procédé et support d'enregistrement d'aide à l'utilisation de gouttes oculaires
CN114613506B (zh) 基于大数据的路径预测管控方法、装置及存储介质
WO2024090643A1 (fr) Dispositif et procédé permettant de mesurer la performance de commercialisation en utilisant une recherche de portail et une analyse de réputation
EP3940712A1 (fr) Inférence de l'état d'un analyseur médical

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22867632

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE