요인별 기관지천식에 대한 범주예측모형 개발
The development of patient-tailored asthma prediction model for the alarm system
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Purpose: The increased incidence of asthma due to rising allergic diseases requires the prevention of worsening asthma. It is necessary to develop a patient-tailored asthma prediction model. Methods: We developed causative factors for the asthma forecast system: infant and young children (0–2 years), preschool children (3–6 years), school children and adolescents (7–18 years), adults (19–64 years), old aged adult (>64 years). We used the Emergency Department code data which charged the short-acting bronchodilator (Salbutamol sulfate) from Health Insurance Review and Assessment Service for the development of asthma prediction models. Three kinds of statistical models (multiple regression models, logistic regression models, and decision tree models) were applied to 40 study groups (4 seasons, 2 sex, and 5 age groups) separately. Results: The 3 kinds of models were compared based on model assessment measures. Estimated logistic regression models or decision tree models were recommended as binary forecast models. To improve the predictability, a threshold was used to generate binary forecasts. Conclusion: We suggest the binary forecast models as a patient-tailored asthma prediction system for this category. It may be needed the extended study duration and long-term data analysis for asthmatic patients for the further improvement of asthma prediction models.
Abstract
Purpose: The increased incidence of asthma due to rising allergic diseases requires the prevention of worsening asthma. It is necessary to develop a patient-tailored asthma prediction model. Methods: We developed causative factors for the asthma forecast system: infant and young children (0–2 years), preschool children (3–6 years), school children and adolescents (7–18 years), adults (19–64 years), old aged adult (>64 years). We used the Emergency Department code data which charged the short-acting bronchodilator (Salbutamol sulfate) from Health Insurance Review and Assessment Service for the development of asthma prediction models. Three kinds of statistical models (multiple regression models, logistic regression models, and decision tree models) were applied to 40 study groups (4 seasons, 2 sex, and 5 age groups) separately. Results: The 3 kinds of models were compared based on model assessment measures. Estimated logistic regression models or decision tree models were recommended as binary forecast models. To improve the predictability, a threshold was used to generate binary forecasts. Conclusion: We suggest the binary forecast models as a patient-tailored asthma prediction system for this category. It may be needed the extended study duration and long-term data analysis for asthmatic patients for the further improvement of asthma prediction models.
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