autoprognosis.plugins.prediction.risk_estimation.benchmarks.cvd.qrisk3.model module

class QRisk3Model

Bases: object

fit(*args: Any, **kwargs: Any) → QRisk3Model

predict(df: DataFrame, times: list = []) → Any

score(X: DataFrame, Y: DataFrame) → float

cvd_female_raw(age: float, b_AF: int, b_atypicalantipsy: int, b_corticosteroids: int, b_migraine: int, b_ra: int, b_renal: int, b_semi: int, b_sle: int, b_treatedhyp: int, b_type1: int, b_type2: int, bmi: float, ethrisk: int, fh_cvd: int, rati: float, sbp: float, sbps5: float, smoke_cat: int, surv: int, town: float) → float

cvd_male_raw(age: float, b_AF: bool, b_atypicalantipsy: bool, b_corticosteroids: bool, b_impotence2: bool, b_migraine: bool, b_ra: bool, b_renal: bool, b_semi: bool, b_sle: bool, b_treatedhyp: bool, b_type1: bool, b_type2: bool, bmi: float, ethrisk: int, fh_cvd: int, rati: float, sbp: float, sbps5: float, smoke_cat: int, surv: int, town: float) → float

inference(gender: str, age: float, b_AF: bool, b_atypicalantipsy: bool, b_corticosteroids: bool, b_impotence2: bool, b_migraine: bool, b_ra: bool, b_renal: bool, b_semi: bool, b_sle: bool, b_treatedhyp: bool, b_type1: bool, b_type2: bool, bmi: float, ethrisk: int, fh_cvd: int, rati: float, sbp: float, sbps5: float, smoke_cat: int, town: float, surv: int = 10) → float

mmolL_to_mgdl(val: float) → float