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163 | def compute_completeness_predictor_per_measurement(
self,
data: Data,
start_date: datetime,
end_date: datetime,
care_site_levels: Union[bool, str, List[str]],
stay_types: Union[bool, str, Dict[str, str]],
care_site_ids: List[int],
care_site_short_names: List[str],
care_site_specialties: Union[bool, List[str]],
measurement_concept_codes: Union[bool, List[str]],
care_sites_sets: Union[str, Dict[str, str]],
specialties_sets: Union[str, Dict[str, str]],
concepts_sets: Union[str, Dict[str, str]],
length_of_stays: List[float],
source_terminologies: Dict[str, str],
mapping: List[Tuple[str, str, str]],
age_ranges: List[int],
gender_source_values: Union[bool, str, Dict[str, str]],
condition_types: Union[bool, str, Dict[str, str]],
diag_types: Union[bool, str, Dict[str, str]],
provenance_sources: Union[bool, str, Dict[str, str]],
stay_sources: Union[bool, str, Dict[str, str]],
drg_sources: Union[bool, str, Dict[str, str]],
**kwargs
):
r"""Script to be used by [``compute()``][edsteva.probes.base.BaseProbe.compute]
The ``per_measurement`` algorithm computes $c_(t)$ the availability of biological measurements:
$$
c(t) = \frac{n_{biology}(t)}{n_{max}}
$$
Where $n_{biology}(t)$ is the number of biological measurements, $t$ is the month and $n_{max} = \max_{t}(n_{biology}(t))$.
"""
self._metrics = ["c", "n_measurement"]
check_tables(
data=data,
required_tables=[
"visit_occurrence",
"care_site",
"fact_relationship",
"measurement",
"concept",
"concept_relationship",
],
)
care_site_relationship = prepare_care_site_relationship(
data=data,
)
self.care_site_relationship = care_site_relationship
standard_terminologies = self._standard_terminologies
biology_relationship = prepare_biology_relationship(
data=data,
standard_terminologies=standard_terminologies,
source_terminologies=source_terminologies,
mapping=mapping,
)
self.biology_relationship = biology_relationship
root_terminology = mapping[0][0]
person = (
prepare_person(data, gender_source_values)
if (age_ranges or gender_source_values)
else None
)
cost = prepare_cost(data, drg_sources) if drg_sources else None
measurement = prepare_measurement(
data=data,
biology_relationship=biology_relationship,
measurement_concept_codes=measurement_concept_codes,
concepts_sets=concepts_sets,
start_date=start_date,
end_date=end_date,
root_terminology=root_terminology,
standard_terminologies=standard_terminologies,
per_visit=False,
)
visit_occurrence = prepare_visit_occurrence(
data=data,
start_date=None,
end_date=None,
stay_types=stay_types,
length_of_stays=length_of_stays,
provenance_sources=provenance_sources,
stay_sources=stay_sources,
cost=cost,
person=person,
age_ranges=age_ranges,
).drop(columns=["visit_occurrence_source_value", "date"])
if condition_types:
conditions = prepare_condition_occurrence(
data,
extra_data=None,
visit_occurrence=None,
source_systems="ORBIS",
diag_types=diag_types,
condition_types=condition_types,
start_date=start_date,
end_date=end_date,
)[["visit_occurrence_id", "condition_type", "diag_type"]].drop_duplicates()
visit_occurrence = visit_occurrence.merge(conditions, on="visit_occurrence_id")
care_site = prepare_care_site(
data=data,
care_site_ids=care_site_ids,
care_site_short_names=care_site_short_names,
care_site_specialties=care_site_specialties,
care_sites_sets=care_sites_sets,
specialties_sets=specialties_sets,
care_site_relationship=care_site_relationship,
)
hospital_measurement = get_hospital_measurements(
measurement=measurement,
visit_occurrence=visit_occurrence,
care_site=care_site,
)
hospital_name = CARE_SITE_LEVEL_NAMES["Hospital"]
biology_predictor_by_level = {hospital_name: hospital_measurement}
if care_site_levels and not hospital_only(care_site_levels=care_site_levels):
logger.info(
"Biological measurements are only available at hospital level for now"
)
care_site_levels = "Hospital"
biology_predictor = concatenate_predictor_by_level(
predictor_by_level=biology_predictor_by_level,
care_site_levels=care_site_levels,
)
return compute_completeness(self, biology_predictor)
|