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edsteva.probes.note.completeness_predictors.per_visit

compute_completeness_predictor_per_visit 

compute_completeness_predictor_per_visit(
    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]],
    care_sites_sets: Union[str, Dict[str, str]],
    specialties_sets: Union[str, Dict[str, str]],
    extra_data: Data,
    length_of_stays: List[float],
    note_types: Union[str, Dict[str, str]],
    age_ranges: List[int],
    gender_source_values: Union[bool, str, Dict[str, str]],
    condition_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_visit algorithm computes c(t) the availability of clinical documents linked to patients' administrative stays:

c(t)=nwithdoc(t)nvisit(t)

Where nvisit(t) is the number of administrative stays, nwithdoc the number of visits having at least one document and t is the month.

Source code in edsteva/probes/note/completeness_predictors/per_visit.py 
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def compute_completeness_predictor_per_visit(
    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]],
    care_sites_sets: Union[str, Dict[str, str]],
    specialties_sets: Union[str, Dict[str, str]],
    extra_data: Data,
    length_of_stays: List[float],
    note_types: Union[str, Dict[str, str]],
    age_ranges: List[int],
    gender_source_values: Union[bool, str, Dict[str, str]],
    condition_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_visit`` algorithm computes $c_(t)$ the availability of clinical documents linked to patients' administrative stays:

    $$
    c(t) = \frac{n_{with\,doc}(t)}{n_{visit}(t)}
    $$

    Where $n_{visit}(t)$ is the number of administrative stays, $n_{with\,doc}$ the number of visits having at least one document and $t$ is the month.
    """

    self._note_columns = list(set(["note_type"]).intersection(set(self._index)))
    self._metrics = ["c", "n_visit", "n_visit_with_note"]
    check_tables(
        data=data,
        required_tables=[
            "note",
            "visit_occurrence",
            "care_site",
            "fact_relationship",
        ],
    )
    care_site_relationship = prepare_care_site_relationship(
        data=data,
    )
    self.care_site_relationship = care_site_relationship
    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

    visit_occurrence = prepare_visit_occurrence(
        data=data,
        start_date=start_date,
        end_date=end_date,
        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,
    )

    if condition_types:
        check_tables(
            data=data,
            required_tables=[
                "condition_occurrence",
            ],
        )
        conditions = prepare_condition_occurrence(
            data,
            extra_data=None,
            visit_occurrence=None,
            source_systems="ORBIS",
            diag_types=None,
            condition_types=condition_types,
            start_date=start_date,
            end_date=end_date,
        )[["visit_occurrence_id", "condition_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_relationship=care_site_relationship,
        care_site_specialties=care_site_specialties,
        care_sites_sets=care_sites_sets,
        specialties_sets=specialties_sets,
    )

    note = prepare_note(data, note_types)

    hospital_visit = get_hospital_visit(self, note, visit_occurrence, care_site)
    hospital_name = CARE_SITE_LEVEL_NAMES["Hospital"]
    note_predictor_by_level = {hospital_name: hospital_visit}

    # UF selection
    if not hospital_only(care_site_levels=care_site_levels):
        if extra_data:  # pragma: no cover
            visit_detail = prepare_visit_detail(data, start_date, end_date)

            uf_visit, uc_visit, uh_visit = get_visit_detail(
                self,
                extra_data=extra_data,
                note=note,
                visit_occurrence=visit_occurrence,
                visit_detail=visit_detail,
                care_site=care_site,
            )
            uf_name = CARE_SITE_LEVEL_NAMES["UF"]
            note_predictor_by_level[uf_name] = uf_visit
            uc_name = CARE_SITE_LEVEL_NAMES["UC"]
            note_predictor_by_level[uc_name] = uc_visit
            uh_name = CARE_SITE_LEVEL_NAMES["UH"]
            note_predictor_by_level[uh_name] = uh_visit

            pole_visit = get_pole_visit(uf_visit, care_site, care_site_relationship)
            pole_name = CARE_SITE_LEVEL_NAMES["Pole"]
            note_predictor_by_level[pole_name] = pole_visit
        else:
            logger.info("Note data are only available at hospital level")
            care_site_levels = ["Hospital"]

    # Concatenate all predictors
    note_predictor = concatenate_predictor_by_level(
        predictor_by_level=note_predictor_by_level,
        care_site_levels=care_site_levels,
    )

    if is_koalas(note_predictor):
        note_predictor.spark.cache()

    return compute_completeness(self, note_predictor)