DeathPhenotype

DeathPhenotype

Bases: Phenotype

DeathPhenotype is a class that represents a death-based phenotype. It filters individuals who have died and returns their date of death.

Parameters:

Name	Type	Description	Default
name	str	Name of the phenotype, default is 'death'.	'death'
domain	str	Domain of the phenotype, default is 'PERSON'.	'PERSON'
relative_time_range	Union[RelativeTimeRangeFilter, List[RelativeTimeRangeFilter]]	Filter patients relative to some date (e.g. death after discharge from hospital)	None

Attributes:

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

Source code in phenex/phenotypes/death_phenotype.py

class DeathPhenotype(Phenotype):
    """
    DeathPhenotype is a class that represents a death-based phenotype. It filters individuals
    who have died and returns their date of death.

    Parameters:
        name: Name of the phenotype, default is 'death'.
        domain: Domain of the phenotype, default is 'PERSON'.
        relative_time_range: Filter patients relative to some date (e.g. death after discharge from hospital)

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

    def __init__(
        self,
        name: str = "death",
        domain: str = "PERSON",
        relative_time_range: Union[
            RelativeTimeRangeFilter, List[RelativeTimeRangeFilter]
        ] = None,
    ):
        self.name = name
        self.domain = domain
        self.children = []
        self.relative_time_range = relative_time_range
        if self.relative_time_range is not None:
            if isinstance(self.relative_time_range, RelativeTimeRangeFilter):
                self.relative_time_range = [self.relative_time_range]
            for rtr in self.relative_time_range:
                if rtr.anchor_phenotype is not None:
                    self.children.append(rtr.anchor_phenotype)
        super(DeathPhenotype, self).__init__()

    def _execute(self, tables: Dict[str, Table]) -> PhenotypeTable:
        person_table = tables[self.domain]
        person_table = person_table.mutate(EVENT_DATE=person_table.DATE_OF_DEATH)
        assert is_phenex_person_table(person_table)

        death_table = person_table.filter(person_table.DATE_OF_DEATH.notnull())
        if self.relative_time_range is not None:
            for rtr in self.relative_time_range:
                death_table = rtr.filter(death_table)
        death_table = death_table.mutate(VALUE=ibis.null())
        return death_table.mutate(EVENT_DATE=death_table.DATE_OF_DEATH)

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