Related Experiment Video
Updated: Jan 13, 2026

12:18
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
7.9K
Effects of Treatment and Weather Variables on Nocturnal Enuresis: Long-Term Analysis Using the Classification and
Yuta Onuki1, Teruo Miyano2, Yuma Iwanaka1
1Pediatrics, Showa Medical University Northern Yokohama Hospital, Yokohama, JPN.
Cureus
|January 12, 2026
Summary
This study found that weather and clinical factors significantly impact nocturnal enuresis (NE) in children. Individualized treatment plans considering these variables, like temperature and season, are crucial for effective long-term management of bedwetting.
Area of Science:
- Pediatric Urology
- Environmental Medicine
- Data Science
Background:
- Nocturnal enuresis (NE), or bedwetting, is a common pediatric condition requiring prolonged treatment.
- Existing research on NE risk factors often overlooks the long-term impact of combined clinical and environmental variables.
- This study addresses the unexplored territory of composite risk factors for NE, including meteorological influences.
Purpose of the Study:
- To investigate composite risk factors for nocturnal enuresis (NE) in pediatric patients.
- To evaluate the long-term effects of combined clinical and meteorological factors on NE.
- To identify potential improvements in NE management strategies.
Main Methods:
- Prospective observational study utilizing unbalanced panel data and the Classification and Regression Trees (CART) model.
- Analysis of 14 months of clinical and meteorological data from 19 pediatric patients (aged 6-15) with NE in Japan.
- Inclusion of demographic variables, five treatment modalities, and six meteorological factors in the risk factor analysis.
Main Results:
- Meteorological variables, specifically daily average ambient temperature and calendar month, were identified as composite factors for NE risk.
- Factors contributing to NE risk included specific treatment histories (e.g., desmopressin without vibegron), treatment duration, absence of alarm therapy, and certain months.
- Vibegron demonstrated potential for greater effectiveness in reducing NE risk compared to other treatments.
Conclusions:
- Effective long-term management of NE necessitates individualized treatment plans.
- Clinical and weather-related variables, including seasonal and environmental factors, should be integrated into NE management.
- Personalized approaches considering both patient-specific clinical data and external environmental conditions are key for successful NE treatment.
Related Concept Videos
Survival Tree
379
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
379
Multiple Regression
3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Regression Toward the Mean
6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
Regression Analysis
7.9K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.9K
Classification of Systems-I
543
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
543