WithinSameEncounterPhenotype

Bases: Phenotype

WithinSameEncounterPhenotype is a phenotype that filters a target phenotype based on the occurrence of an anchor phenotype within the same encounter. This phenotype can only be used with CodelistPhenotypes and MeasurementPhenotypes as anchor/phenotype.

Parameters:

Name	Type	Description	Default
name	str	The name of the phenotype. Optional. If not passed, name will be derived from the name of the codelist.	required
anchor_phenotype	Union[CodelistPhenotype, MeasurementPhenotype]	The phenotype which should define the encounter of interest	required
phenotype	Union[CodelistPhenotype, MeasurementPhenotype]	The phenotype which should occur within the same encounter of interest, defined by the anchor_phenotype	required
column_name	str	The name of the column which must be shared between anchor_phenotype and phenotype	required

Attributes:

Name	Type	Description
table	PhenotypeTable	The resulting phenotype table after filtering (None until execute is called)

Examples:

Imaging procedure within the same encounter as Pulmonary Embolism (OMOP)

from phenex.phenotypes import CodelistPhenotype, WithinSameEncounterPhenotype
from phenex.codelists import Codelist
from phenex.mappers import OMOPDomains
from phenex.filters import DateFilter, CategoricalFilter, Value
from phenex.ibis_connect import SnowflakeConnector

pe_diagnosis = CodelistPhenotype(
    name = "diagnosis_of_PE",
    domain = "CONDITION_OCCURRENCE",
    codelist = Codelist(codelist={'SNOMED': [608954, 4120091, 45768439, 45768888, 37165995, 436768, 44782732, 45768887, 45771016, 4017134, 4240832, 4129841, 43530934, 4219469, 442055, 442051, 442050, 433545, 437060, 435026, 433832, 440477, 439380, 439379, 4331168, 4108681, 37160752, 4091708, 1244882, 440417, 37109911, 3655209, 3655210, 45757145, 37016922, 43530605, 4119608, 4253796, 45766471, 4121618, 4236271, 36713113, 35615055, 40479606, 4119607, 4119609]}).copy(use_code_type=False),
    return_date = "first",
)

# where column id alone is idnetical left join
imaging = CodelistPhenotype(
    name = 'imaging',
    domain = 'PROCEDURE_OCCURRENCE',
    codelist = Codelist(codelist={'CPT4': [2211381, 2211379, 2211380, 2212002, 2212004, 2212003, 2212011, 42742555, 42742554, 2212006, 2212005, 2212008, 2212010, 2212009, 42742557, 42742556]}).copy(use_code_type=False),
)


imaging_in_entry_hospitalization = WithinSameEncounterPhenotype(
    name='imaging_in_entry_hospitalization',
    anchor_phenotype = pe_diagnosis,
    phenotype = imaging,
    column_name = 'VISIT_OCCURRENCE_ID'
)

Source code in phenex/phenotypes/within_same_encounter_phenotype.py

class WithinSameEncounterPhenotype(Phenotype):
    """
    WithinSameEncounterPhenotype is a phenotype that filters a target phenotype based on the occurrence of an anchor phenotype within the same encounter. This phenotype can only be used with CodelistPhenotypes and MeasurementPhenotypes as anchor/phenotype.

    Parameters:
        name: The name of the phenotype. Optional. If not passed, name will be derived from the name of the codelist.
        anchor_phenotype: The phenotype which should define the encounter of interest
        phenotype: The phenotype which should occur within the same encounter of interest, defined by the anchor_phenotype
        column_name: The name of the column which must be shared between anchor_phenotype and phenotype


    Attributes:
        table (PhenotypeTable): The resulting phenotype table after filtering (None until execute is called)

    Examples:

    Example: Imaging procedure within the same encounter as Pulmonary Embolism (OMOP)
        ```python
        from phenex.phenotypes import CodelistPhenotype, WithinSameEncounterPhenotype
        from phenex.codelists import Codelist
        from phenex.mappers import OMOPDomains
        from phenex.filters import DateFilter, CategoricalFilter, Value
        from phenex.ibis_connect import SnowflakeConnector

        pe_diagnosis = CodelistPhenotype(
            name = "diagnosis_of_PE",
            domain = "CONDITION_OCCURRENCE",
            codelist = Codelist(codelist={'SNOMED': [608954, 4120091, 45768439, 45768888, 37165995, 436768, 44782732, 45768887, 45771016, 4017134, 4240832, 4129841, 43530934, 4219469, 442055, 442051, 442050, 433545, 437060, 435026, 433832, 440477, 439380, 439379, 4331168, 4108681, 37160752, 4091708, 1244882, 440417, 37109911, 3655209, 3655210, 45757145, 37016922, 43530605, 4119608, 4253796, 45766471, 4121618, 4236271, 36713113, 35615055, 40479606, 4119607, 4119609]}).copy(use_code_type=False),
            return_date = "first",
        )

        # where column id alone is idnetical left join
        imaging = CodelistPhenotype(
            name = 'imaging',
            domain = 'PROCEDURE_OCCURRENCE',
            codelist = Codelist(codelist={'CPT4': [2211381, 2211379, 2211380, 2212002, 2212004, 2212003, 2212011, 42742555, 42742554, 2212006, 2212005, 2212008, 2212010, 2212009, 42742557, 42742556]}).copy(use_code_type=False),
        )


        imaging_in_entry_hospitalization = WithinSameEncounterPhenotype(
            name='imaging_in_entry_hospitalization',
            anchor_phenotype = pe_diagnosis,
            phenotype = imaging,
            column_name = 'VISIT_OCCURRENCE_ID'
        )

        ```
    """

    def __init__(
        self,
        name: str,
        anchor_phenotype: Union["CodelistPhenotype", "MeasurementPhenotype"],
        phenotype: Union["CodelistPhenotype", "MeasurementPhenotype"],
        column_name: str,
        **kwargs,
    ):
        super(WithinSameEncounterPhenotype, self).__init__(**kwargs)
        self.name = name
        if (
            anchor_phenotype.__class__.__name__
            not in ["CodelistPhenotype", "MeasurementPhenotype"]
        ) or (
            phenotype.__class__.__name__
            not in ["CodelistPhenotype", "MeasurementPhenotype"]
        ):
            raise ValueError(
                "Both anchor_phenotype and phenotype must be of type CodelistPhenotype or MeasurementPhenotype"
            )

        self.anchor_phenotype = anchor_phenotype
        self.phenotype = phenotype
        self.column_name = column_name
        self.children.append(self.anchor_phenotype)

    def _execute(self, tables) -> "PhenotypeTable":
        # Subset the raw anchor data that occurs on the same day as the anchor date in order to get the column of interest
        _anchor_table = tables[self.anchor_phenotype.domain]
        _anchor_table = _anchor_table.join(
            self.anchor_phenotype.table,
            (self.anchor_phenotype.table.PERSON_ID == _anchor_table.PERSON_ID)
            & (self.anchor_phenotype.table.EVENT_DATE == _anchor_table.EVENT_DATE),
            how="inner",
        ).select(["PERSON_ID", self.column_name])

        # Subset the target phenotype raw data for patient id and column name of interest
        _table = tables[self.phenotype.domain]
        _table = _table.join(
            _anchor_table,
            (_anchor_table.PERSON_ID == _table.PERSON_ID)
            & (_anchor_table[self.column_name] == _table[self.column_name]),
            how="inner",
        )

        # run the target phenotype on the subsetted data
        return self.phenotype._execute({self.phenotype.domain: _table})

namespaced_table property

A PhenotypeTable has generic column names 'person_id', 'boolean', 'event_date', and 'value'. The namespaced_table appends the phenotype name to all of these columns. This is useful when joining multiple phenotype tables together.

Returns:

Name	Type	Description
table	Table	The namespaced table for the current phenotype.

execute(tables)

Executes the phenotype computation for the current object and its children. This method recursively iterates over the children of the current object and calls their execute method if their table attribute is None.

Parameters:

Name	Type	Description	Default
tables	Dict[str, PhenexTable]	A dictionary mapping table names to PhenexTable objects. See phenex.mappers.DomainsDictionary.get_mapped_tables().	required

Returns:

Name	Type	Description
table	PhenotypeTable	The resulting phenotype table containing the required columns. The PhenotypeTable will contain the columns: PERSON_ID, EVENT_DATE, VALUE. DATE is determined by the return_date parameter. VALUE is different for each phenotype. For example, AgePhenotype will return the age in the VALUE column. A MeasurementPhenotype will return the observed value for the measurement. See the specific phenotype of interest to understand more.

Source code in phenex/phenotypes/phenotype.py

def execute(self, tables: Dict[str, Table]) -> PhenotypeTable:
    """
    Executes the phenotype computation for the current object and its children. This method recursively iterates over the children of the current object and calls their execute method if their table attribute is None.

    Args:
        tables (Dict[str, PhenexTable]): A dictionary mapping table names to PhenexTable objects. See phenex.mappers.DomainsDictionary.get_mapped_tables().

    Returns:
        table (PhenotypeTable): The resulting phenotype table containing the required columns. The PhenotypeTable will contain the columns: PERSON_ID, EVENT_DATE, VALUE. DATE is determined by the return_date parameter. VALUE is different for each phenotype. For example, AgePhenotype will return the age in the VALUE column. A MeasurementPhenotype will return the observed value for the measurement. See the specific phenotype of interest to understand more.
    """
    logger.info(f"Phenotype '{self.name}': executing...")
    for child in self.children:
        if child.table is None:
            logger.debug(
                f"Phenotype {self.name}: executing child phenotype '{child.name}'..."
            )
            child.execute(tables)
        else:
            logger.debug(
                f"Phenotype {self.name}: skipping already computed child phenotype '{child.name}'."
            )

    table = self._execute(tables).mutate(BOOLEAN=True)

    if not set(PHENOTYPE_TABLE_COLUMNS) <= set(table.columns):
        raise ValueError(
            f"Phenotype {self.name} must return columns {PHENOTYPE_TABLE_COLUMNS}. Found {table.columns}."
        )

    self.table = table.select(PHENOTYPE_TABLE_COLUMNS)
    # for some reason, having NULL datatype screws up writing the table to disk; here we make explicit cast
    if type(self.table.schema()["VALUE"]) == ibis.expr.datatypes.core.Null:
        self.table = self.table.cast({"VALUE": "float64"})

    assert is_phenex_phenotype_table(self.table)
    logger.info(f"Phenotype '{self.name}': execution completed.")
    return self.table