def get_stroke_probability(self, person):
model_spec = load_model_spec("StrokeMIPartitionModel")
strokePartitionModel = StatsModelLinearRiskFactorModel(
RegressionModel(**model_spec))
strokeProbability = scipySpecial.expit(
strokePartitionModel.estimate_next_risk(person))
return strokeProbability
def setUp(self):
self.imputed_dataset_first_person = Person(71, NHANESGender.MALE,
NHANESRaceEthnicity.NON_HISPANIC_WHITE,
144.667, 52.6667, 9.5, 34, 191, 30.05,
110.0, 128, 45, 0, Education.COLLEGEGRADUATE,
SmokingStatus.FORMER, AlcoholCategory.NONE, 0, 0, 0,
initializeAFib)
model_spec = load_model_spec("nhanesMortalityModel")
self.model = StatsModelCoxModel(CoxRegressionModel(**model_spec))
def __init__(
self,
mi_case_fatality=default_mi_case_fatality,
stroke_case_fatality=default_stroke_case_fatality,
mi_secondary_case_fatality=default_secondary_mi_case_fatality,
stroke_secondary_case_fatality=default_secondary_stroke_case_fatality,
secondary_prevention_multiplier=default_secondary_prevention_multiplier,
):
self.mi_case_fatality = mi_case_fatality
self.mi_secondary_case_fatality = (mi_secondary_case_fatality,)
self.stroke_case_fatality = stroke_case_fatality
self.stroke_secondary_case_fatality = stroke_secondary_case_fatality
self.secondary_prevention_multiplier = secondary_prevention_multiplier
model_spec = load_model_spec("StrokeMIPartitionModel")
self.strokePartitionModel = StatsModelLinearRiskFactorModel(RegressionModel(**model_spec))
def __init__(self):
self.mi_case_fatality = CVOutcomeDetermination.default_mi_case_fatality
self.secondary_mi_case_fatality = CVOutcomeDetermination.default_secondary_mi_case_fatality
self.stroke_case_fatality = CVOutcomeDetermination.default_stroke_case_fatality
self.secondary_stroke_case_fatality = (
CVOutcomeDetermination.default_secondary_stroke_case_fatality)
self.secondary_prevention_multiplier = (
CVOutcomeDetermination.default_secondary_prevention_multiplier)
self.outcomeDet = CVOutcomeDetermination(
self.mi_case_fatality,
self.stroke_case_fatality,
self.secondary_mi_case_fatality,
self.secondary_stroke_case_fatality,
self.secondary_prevention_multiplier,
)
# variable used in testing to control whether a patient will have a stroke or mi
self.manualStrokeMIProbability = None
self._models = {}
femaleCVCoefficients = {
"lagAge": 0.106501,
"black": 0.432440,
"lagSbp#lagSbp": 0.000056,
"lagSbp": 0.017666,
"current_bp_treatment": 0.731678,
"current_diabetes": 0.943970,
"current_smoker": 1.009790,
"lagAge#black": -0.008580,
"lagSbp#current_bp_treatment": -0.003647,
"lagSbp#black": 0.006208,
"black#current_bp_treatment": 0.152968,
"lagAge#lagSbp": -0.000153,
"black#current_diabetes": 0.115232,
"black#current_smoker": -0.092231,
"lagSbp#black#current_bp_treatment": -0.000173,
"lagAge#lagSbp#black": -0.000094,
"Intercept": -12.823110,
}
maleCVCoefficients = {
"lagAge": 0.064200,
"black": 0.482835,
"lagSbp#lagSbp": -0.000061,
"lagSbp": 0.038950,
"current_bp_treatment": 2.055533,
"current_diabetes": 0.842209,
"current_smoker": 0.895589,
"lagAge#black": 0,
"lagSbp#current_bp_treatment": -0.014207,
"lagSbp#black": 0.011609,
"black#current_bp_treatment": -0.119460,
"lagAge#lagSbp": 0.000025,
"black#current_diabetes": -0.077214,
"black#current_smoker": -0.226771,
"lagSbp#black#current_bp_treatment": 0.004190,
"lagAge#lagSbp#black": -0.000199,
"Intercept": -11.679980,
}
self._models[OutcomeModelType.CARDIOVASCULAR] = {
"female":
ASCVDOutcomeModel(
RegressionModel(
coefficients=femaleCVCoefficients,
coefficient_standard_errors={
key: 0
for key in femaleCVCoefficients
},
residual_mean=0,
residual_standard_deviation=0,
),
tot_chol_hdl_ratio=0.151318,
black_race_x_tot_chol_hdl_ratio=0.070498,
),
"male":
ASCVDOutcomeModel(
RegressionModel(
coefficients=maleCVCoefficients,
coefficient_standard_errors={
key: 0
for key in maleCVCoefficients
},
residual_mean=0,
residual_standard_deviation=0,
),
tot_chol_hdl_ratio=0.193307,
black_race_x_tot_chol_hdl_ratio=-0.117749,
),
}
self._models[
OutcomeModelType.GLOBAL_COGNITIVE_PERFORMANCE] = GCPModel()
self._models[OutcomeModelType.DEMENTIA] = DementiaModel()
# This represents non-cardiovascular mortality..
nonCVModelSpec = load_model_spec("nhanesMortalityModelLogit")
mortModel = StatsModelLogisticRiskFactorModel(
RegressionModel(**nonCVModelSpec), False)
# Recalibrate mortalitly model to align with life table data, as explored in notebook
# buildNHANESMortalityModel
mortModel.non_intercept_params[
"age"] = mortModel.non_intercept_params["age"] * -1
mortModel.non_intercept_params["squareAge"] = (
mortModel.non_intercept_params["squareAge"] * 4)
self._models[OutcomeModelType.NON_CV_MORTALITY] = mortModel
def initialize_cox_model(self, modelName):
model_spec = load_model_spec(modelName)
return StatsModelCoxModel(CoxRegressionModel(**model_spec))