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edsteva.probes.biology.completeness_predictors.per_measurement

compute_completeness_predictor_per_measurement 

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
)

Script to be used by compute()

The per_measurement algorithm computes c(t) the availability of biological measurements:

c(t)=nbiology(t)nmax

Where nbiology(t) is the number of biological measurements, t is the month and nmax=maxt(nbiology(t)).

Source code in edsteva/probes/biology/completeness_predictors/per_measurement.py 
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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)