CLivD Points Simulation Model
Submodules
models.clivd_points.clivd module
Custom model to predict the percentage of developing liver disease in the next 15 years based on input parameters using linear scaling.
- Args:
age (int): The age of the individual (between 20 and 100). sex (int): The gender of the individual (1 for male, 2 for female). alcohol (float): The alcohol intake per week (in drinks, between 0 and 50). diabetes (bool): Indicates if the individual has diabetes (True for yes, False for no). whr (float): The waist-hip ratio of the individual (between 0.6 and 1.4). ggt (float): The gamma-glutamyl transferase level (in U/L, between 0 and 200). smoking (bool): Indicates if the individual is a smoker (True for yes, False for no).
- Returns:
float: The predicted risk score based on the input parameters.
- class biomodel.models.clivd_points.clivd.CLivDPoints
Bases:
CustomModelCustom model to predict the percentage of developing liver disease in the next 15 years based on input parameters using linear scaling.
- Args:
age (int): The age of the individual (between 20 and 100). sex (int): The gender of the individual (1 for male, 2 for female). alcohol (float): The alcohol intake per week (in drinks, between 0 and 50). diabetes (bool): Indicates if the individual has diabetes (True for yes, False for no). whr (float): The waist-hip ratio of the individual (between 0.6 and 1.4). ggt (float): The gamma-glutamyl transferase level (in U/L, between 0 and 200). smoking (bool): Indicates if the individual is a smoker (True for yes, False for no).
- Returns:
float: The predicted risk score based on the input parameters.
- interpretation = 'percentage'
- intervals = [RiskInterval(value=0.0, name='Minimal risk'), RiskInterval(value=0.5, name='Low risk'), RiskInterval(value=4, name='Intermediate risk'), RiskInterval(value=10, name='High risk')]
- name = 'CLivD'
- predict(age: AgeInt(id='age', minimum=20, maximum=100, name='Age'), sex: Enum(id='sex', values={1: 'Male', 2: 'Female'}, name='Sex'), alcohol: Float(id=35810226, minimum=0, maximum=50, name='Alcohol intake (drinks/week)'), diabetes: BoolInt(id=35817874, true=1, false=0, name='Has diabetes?'), whr: Float(id=4087501, minimum=0.6, maximum=1.4, name='Waist-hip ratio'), ggt: Float(id=4210715, minimum=0, maximum=200, name='Gamma-glutamyl transferase (U/L)'), smoking: BoolInt(id=1585856, true=1, false=0, name='Is smoker?')) float
Predict the risk score based on the input parameters using linear scaling.
- Args:
age (int): The age of the individual (between 20 and 100). sex (int): The gender of the individual (1 for male, 2 for female). alcohol (float): The alcohol intake per week (in drinks, between 0 and 50). diabetes (bool): Indicates if the individual has diabetes (True for yes, False for no). whr (float): The waist-hip ratio of the individual (between 0.6 and 1.4). ggt (float): The gamma-glutamyl transferase level (in U/L, between 0 and 200). smoking (bool): Indicates if the individual is a smoker (True for yes, False for no).
- Returns:
float: The predicted risk score based on the input parameters.
- title = 'Percentage of developing liver disease in the next 15 years'
- biomodel.models.clivd_points.clivd.linear_points(value, min_bucket, max_bucket, min_value, max_value)
Calculate the linearly scaled value based on the input value and the specified range.
- Args:
value (float): The input value to be scaled. min_bucket (float): The minimum value of the input range. max_bucket (float): The maximum value of the input range. min_value (float): The minimum value of the output range. max_value (float): The maximum value of the output range.
- Returns:
float: The linearly scaled value within the specified output range.