MeaurementPhenotype

Bases: CodelistPhenotype

What is MeasurementPhenotype for?

The MeasurementPhenotype is for manipulating numerical data found in RWD data sources e.g. laboratory or observation results. These tables often contain numerical values (height, weight, blood pressure, lab results). As an event-based table, each row records a single measurement value for a single patient with a date. All numerical values are in a 'value' column. A medical code indicates the type of numerical measurement and the units of measurement are in an additional column.

MeasurementPhenotype is a subclass of CodelistPhenotype, inheriting all of its functionality to identify patients by single or sets of medical codes (e.g. 'test type') within a specified time period. It can also :

• identify patients with a measurement value within a value range and
• return a measurement value, either all measurements values within filter criteria or perform simple aggregations (mean, median, max, min).

Example data:

PersonID	MedicalCode	EventDate	Value	Unit
1	HbA1c	2010-01-01	4.2	%
1	HT	2010-01-02	121	cm
2	WT	2010-01-01	130	kg

Note on data cleaning

In general, data cleaning operations should be performed upstream of Phenex. This includes:

• unit harmonization: ideally all values should be in the same unit for a given measurement type test. There are workarounds to deal with multiple units for a single measurement, but it is not recommended.

• removing nonsensical values: e.g. negative blood pressures, or values outside of a physiological range. While MeasurementPhenotype provides a clean_nonphysiologicals_value_filter parameter to remove such values, is recommended to perform this operation upstream of apex.

• dealing with duplicate entries: e.g. multiple entries for the same patient on the same day. The meaning of multiple entries on the same day may vary between data sources, so it is recommended to handle duplicate entries upstream of apex. In some EHR datasources, duplicate entries may suggest that this value is 'more accurate', as it is passed and recorded through multiple providers and systems, while in other datasources multiple entries may suggest faulty data entry.

Parameters:

Name	Type	Description	Default
value_aggregation	ValueAggregator	A ValueAggregator PhenEx class defining aggregation, whether over the whole defined time period (relative time range filter, date filter) using Mean, Median, Min, Max) or daily aggregation operation (DailyMin, DailyMax, DailyMedian, DailyMean) to be performed on the measurement values. This operation occurs after the cleaning value filter but prior to the primary value_filter. If Mean and Median are used, no EVENT_DATE will be returned. For daily aggregators, the daily EVENT_DATES will be returned. For Min and Max, all days that have the Min/Max value are returned, resulting in multiple rows per patient.	None
value_filter	ValueFilter	A ValueFilter to be applied to the measurement values. This filter is applied after the clean_nonphysiologicals_value_filter and value_aggregation. This filter is used to identify patients with a measurement value within a value range. If not specified, no value filter is applied. For example, to identify patients with a 1. systolic blood pressure above 120 mmHg, the value_filter would be set to ValueFilter(min_value=GreaterThan(120)), 2. systolic blood pressure above 120 mmHg but below 140 mmHg, the value_filter would be set to ValueFilter(min_value=GreaterThan(120), max_value=LessThan(140)).	None
further_value_filter_phenotype	str	If the input to the current MeasurementPhenotype is the output of a previous MeasurementPhenotype, set this parameter to the previous MeasurementPhenotype.	None
clean_nonphysiologicals_value_filter	str	A value filter to be applied prior to any filtering or aggregation. This should be used to remove nonsensical values e.g. negative blood pressures, or values outside of a physiological range that are certain to be due to measurement error. Ideally, such cleaing steps should performed upstream of apex, but have been provided due to realization of practical necessity.	None
clean_null_values	str	A boolean indicating whether to remove null values from the measurement table. If set to True, null values are removed prior to value filtering. If set to False, null values are not removed. If not specified, null values are removed (default is true)	True
return_date	str	Specifies whether to return the 'first', 'last', or 'nearest' event date. Default is 'first'.	required

Source code in phenex/phenotypes/measurement_phenotype.py

class MeasurementPhenotype(CodelistPhenotype):
    """
    # What is MeasurementPhenotype for?
    The MeasurementPhenotype is for manipulating numerical data found in RWD data sources e.g. laboratory or observation results. These tables often contain numerical values (height, weight, blood pressure, lab results). As an event-based table, each row records a single measurement value for a single patient with a date. All numerical values are in a 'value' column. A medical code indicates the type of numerical measurement and the units of measurement are in an additional column.

    MeasurementPhenotype is a subclass of CodelistPhenotype, inheriting all of its functionality to identify patients by single or sets of medical codes (e.g. 'test type') within a specified time period. It can also :

    - identify patients with a measurement value within a value range and
    - return a measurement value, either all measurements values within filter
      criteria or perform simple aggregations (mean, median, max, min).

    # Example data:

    | PersonID    |   MedicalCode   |   EventDate   |   Value    | Unit|
    |-------------|-----------------|---------------|------------|-----|
    | 1           |   HbA1c         |   2010-01-01  |   4.2      | %   |
    | 1           |   HT            |   2010-01-02  |   121      | cm  |
    | 2           |   WT            |   2010-01-01  |   130      | kg  |

    # Note on data cleaning
    In general, data cleaning operations should be performed upstream of Phenex.
    This includes:

    - unit harmonization: ideally all values should be in the same unit for a given measurement type test. There are workarounds to deal with multiple units for a single measurement, but it is not recommended.

    - removing nonsensical values: e.g. negative blood pressures, or values outside of a physiological range. While MeasurementPhenotype provides a clean_nonphysiologicals_value_filter parameter to remove such values,  is recommended to perform this operation upstream of apex.

    - dealing with duplicate entries: e.g. multiple entries for the same patient on the same day. The meaning of multiple entries on the same day may vary between data sources, so it is recommended to handle duplicate entries upstream of apex. In some EHR datasources, duplicate entries may suggest that this value is 'more accurate', as it is passed and recorded through multiple providers and systems, while in other datasources multiple entries may suggest faulty data entry.


    Parameters:
        value_aggregation (ValueAggregator): A ValueAggregator PhenEx class defining aggregation, whether over the whole defined time period (relative time range filter, date filter) using Mean, Median, Min, Max) or daily aggregation operation (DailyMin, DailyMax, DailyMedian, DailyMean) to be performed on the measurement values. This operation occurs **after** the cleaning value filter but **prior** to the primary value_filter. If Mean and Median are used, no EVENT_DATE will be returned. For daily aggregators, the daily EVENT_DATES will be returned. For Min and Max, all days that have the Min/Max value are returned, resulting in multiple rows per patient.
        value_filter (ValueFilter): A ValueFilter to be applied to the measurement values. This filter is applied **after** the clean_nonphysiologicals_value_filter and value_aggregation. This filter is used to identify patients with a measurement value within a value range. If not specified, no value filter is applied. For example, to identify patients with a 1. systolic blood pressure above 120 mmHg, the value_filter would be set to ValueFilter(min_value=GreaterThan(120)), 2. systolic blood pressure above 120 mmHg but below 140 mmHg, the value_filter would be set to ValueFilter(min_value=GreaterThan(120), max_value=LessThan(140)).
        further_value_filter_phenotype (str): If the input to the current MeasurementPhenotype is the output of a previous MeasurementPhenotype, set this parameter to the previous MeasurementPhenotype.
        clean_nonphysiologicals_value_filter (str): A value filter to be applied **prior** to any filtering or aggregation. This should be used to remove nonsensical values e.g. negative blood pressures, or values outside of a physiological range that are certain to be due to measurement error. Ideally, such cleaing steps should performed upstream of apex, but have been provided due to realization of practical necessity.
        clean_null_values (str): A boolean indicating whether to remove null values from the measurement table. If set to True, null values are removed prior to value filtering. If set to False, null values are not removed. If not specified, null values are removed (default is true)
        return_date (str): Specifies whether to return the 'first', 'last', or 'nearest' event date. Default is 'first'.

    """

    def __init__(
        self,
        value_filter: Optional["ValueFilter"] = None,
        clean_nonphysiologicals_value_filter: Optional["ValueFilter"] = None,
        clean_null_values: Optional[bool] = True,
        value_aggregation: Optional[
            Union[
                "ValueAggregator",
                "DailyMedian",
                "DailyMean",
                "DailyMin",
                "DailyMax",
                "DailyValueAggregator",
                "Min",
                "Max",
                "Mean",
            ]
        ] = None,
        further_value_filter_phenotype: Optional["MeasurementPhenotype"] = None,
        **kwargs,
    ):
        # Default value of return_date in codelist_phenotype is 'first'. This is not helpful behavior for measurementphenotype as we will perform further operations that require all values. For example, if we want the mean of all values in the post index period, setting return_date = 'first' will return only the values on the first day
        if "return_date" not in kwargs:
            kwargs["return_date"] = "all"
        kwargs["return_value"] = "all"
        super(MeasurementPhenotype, self).__init__(
            **kwargs,
        )

        if further_value_filter_phenotype is not None:
            self.add_children(further_value_filter_phenotype)

        self.clean_nonphysiologicals_value_filter = clean_nonphysiologicals_value_filter
        self.clean_null_values = clean_null_values
        self.value_filter = value_filter
        self.value_aggregation = value_aggregation
        self.further_value_filter_phenotype = further_value_filter_phenotype

        if self.return_date != "all":
            if self.value_aggregation.__class__.__name__ in [
                "Mean",
                "Median",
                "Max",
                "Min",
            ]:
                raise ValueError(
                    f"{self.name}: you have selected an aggregation of the entire time period ({self.value_aggregation.__class__.__name__}) while selecting a single date selection of {self.return_date}. Select a daily aggregator (DailyMean, DailyMedian, DailyMin, DailyMax) if selecting a specific return date."
                )

    def _execute(self, tables) -> PhenotypeTable:
        # perform codelist filtering
        # perform nonphysiological value filtering
        # perform value aggreation
        # perform value filter
        # perform value and dateaggregation
        code_table = tables[self.domain]
        code_table = self._perform_codelist_filtering(code_table)
        code_table = self._perform_categorical_filtering(code_table, tables)
        code_table = self._perform_null_value_filtering(code_table)
        code_table = self._perform_nonphysiological_value_filtering(code_table)
        code_table = self._perform_time_filtering(code_table)
        code_table = self._perform_date_selection(code_table)
        code_table = self._perform_value_aggregation(code_table)
        code_table = self._perform_value_filtering(code_table)
        return select_phenotype_columns(code_table)

    def _perform_null_value_filtering(self, code_table):
        if self.clean_null_values and self.value_filter:
            logger.debug(f"Applying null filtering for {self.name}")
            code_table = code_table[code_table[self.value_filter.column_name].notnull()]
        return code_table

    def _perform_nonphysiological_value_filtering(self, code_table):
        if self.clean_nonphysiologicals_value_filter is not None:
            code_table = self.clean_nonphysiologicals_value_filter.filter(code_table)
        return code_table

    def _perform_value_aggregation(self, code_table):
        if self.value_aggregation is not None:
            code_table = self.value_aggregation.aggregate(code_table)
        return code_table

    def _perform_value_filtering(self, code_table):
        if self.value_filter is not None:
            code_table = self.value_filter.filter(code_table)
        return code_table

dependencies property

Recursively collect all dependencies of a node (including dependencies of dependencies).

Returns:

Type	Description
Set[Node]	List[Node]: A list of Node objects on which this Node depends.

dependency_graph property

Build a dependency graph where each node maps to its direct dependencies (children).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, Set[Node]: A mapping of Node's to their children Node's.

execution_metadata property

Retrieve the full execution metadata row for this node from the local DuckDB database.

Returns:

Type	Description
	pandas.DataFrame: A table containing NODE_NAME, NODE_HASH, NODE_PARAMS, EXECUTION_PARAMS, EXECUTION_START_TIME, EXECUTION_END_TIME, and EXECUTION_DURATION for execution of this node, or None if the node has never been executed.

namespaced_table property

A PhenotypeTable has generic column names 'person_id', 'boolean', 'event_date', and 'value'. The namespaced_table prepends 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.

reverse_dependency_graph property

Build a reverse dependency graph where each node maps to nodes that depend on it (parents).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, List[Node]: A mapping of Node's to their parent Node's.

clear_cache(con=None, recursive=False)

Clear the cached state for this node, forcing re-execution on the next call to execute().

This method removes the node's hash from the node states table and optionally drops the materialized table from the database. After calling this method, the node will be treated as if it has never been executed before.

Parameters:

Name	Type	Description	Default
con	Optional[object]	Database connector. If provided, clears only runs with matching execution context and drops the materialized table. If None, clears all runs for the node.	None
recursive	bool	If True, also clear the cache for all child nodes recursively. Defaults to False.	False

Example

# Clear all cached runs for a single node
my_node.clear_cache()

# Clear runs with specific execution context and drop materialized table
my_node.clear_cache(con=my_connector)

# Clear cache for node and all its dependencies
my_node.clear_cache(recursive=True)

Source code in phenex/node.py

def clear_cache(self, con: Optional[object] = None, recursive: bool = False):
    """
    Clear the cached state for this node, forcing re-execution on the next call to execute().

    This method removes the node's hash from the node states table and optionally drops the materialized table from the database. After calling this method, the node will be treated as if it has never been executed before.

    Parameters:
        con: Database connector. If provided, clears only runs with matching execution context and drops the materialized table. If None, clears all runs for the node.
        recursive: If True, also clear the cache for all child nodes recursively. Defaults to False.

    Example:
        ```python
        # Clear all cached runs for a single node
        my_node.clear_cache()

        # Clear runs with specific execution context and drop materialized table
        my_node.clear_cache(con=my_connector)

        # Clear cache for node and all its dependencies
        my_node.clear_cache(recursive=True)
        ```
    """
    # Delegate all logic to NodeManager
    return Node._node_manager.clear_cache(self, con=con, recursive=recursive)

execute(tables=None, con=None, overwrite=False, lazy_execution=False, n_threads=1)

Executes the Node computation for the current node and its dependencies.

Lazy Execution

When lazy_execution=True, nodes are only recomputed if changes are detected. The system tracks: 1. Node definition changes: Detected by hashing the node's parameters (from to_dict()) and class name 2. Execution environment changes: Detected by tracking source/destination database configurations

A node will be rerun if either: - The node's defining parameters have changed (different hash than last execution) - The database connector's source or destination databases have changed - The node has never been executed before

If no changes are detected, the node uses its cached result from the database instead of recomputing.

Requirements for lazy execution: - A database connector (con) must be provided to store and retrieve cached results - overwrite=True must be set to allow updating existing cached tables

State tracking is maintained in a local DuckDB database (__PHENEX_META__NODE_STATES table) that stores: - Node hashes, parameters, and execution metadata - Database connector configuration used during execution - Execution timing information

Parameters:

Name	Type	Description	Default
tables	Dict[str, Table]	A dictionary mapping domains to Table objects.	None
con	Optional[object]	Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector. Required for lazy_execution.	None
overwrite	bool	If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed. Must be True when using lazy_execution.	False
lazy_execution	bool	If True, only re-executes nodes when changes are detected in either the node definition or execution environment. Defaults to False. Requires con to be provided.	False
n_threads	int	Max number of Node's to execute simultaneously when this node has multiple children.	1

Returns:

Name	Type	Description
Table	Table	The resulting table for this node. Also accessible through self.table after calling self.execute().

Raises:

Type	Description
ValueError	If lazy_execution=True but overwrite=False or con=None.

Source code in phenex/node.py

def execute(
    self,
    tables: Dict[str, Table] = None,
    con: Optional[object] = None,
    overwrite: bool = False,
    lazy_execution: bool = False,
    n_threads: int = 1,
) -> Table:
    """
    Executes the Node computation for the current node and its dependencies.

    Lazy Execution:
        When lazy_execution=True, nodes are only recomputed if changes are detected. The system tracks:
        1. Node definition changes: Detected by hashing the node's parameters (from to_dict()) and class name
        2. Execution environment changes: Detected by tracking source/destination database configurations

        A node will be rerun if either:
        - The node's defining parameters have changed (different hash than last execution)
        - The database connector's source or destination databases have changed
        - The node has never been executed before

        If no changes are detected, the node uses its cached result from the database instead of recomputing.

        Requirements for lazy execution:
        - A database connector (con) must be provided to store and retrieve cached results
        - overwrite=True must be set to allow updating existing cached tables

        State tracking is maintained in a local DuckDB database (__PHENEX_META__NODE_STATES table) that stores:
        - Node hashes, parameters, and execution metadata
        - Database connector configuration used during execution
        - Execution timing information

    Parameters:
        tables: A dictionary mapping domains to Table objects.
        con: Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector. Required for lazy_execution.
        overwrite: If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed. Must be True when using lazy_execution.
        lazy_execution: If True, only re-executes nodes when changes are detected in either the node definition or execution environment. Defaults to False. Requires con to be provided.
        n_threads: Max number of Node's to execute simultaneously when this node has multiple children.

    Returns:
        Table: The resulting table for this node. Also accessible through self.table after calling self.execute().

    Raises:
        ValueError: If lazy_execution=True but overwrite=False or con=None.
    """
    # Handle None tables
    if tables is None:
        tables = {}

    # Build dependency graph for all dependencies
    all_deps = self.dependencies
    nodes = {node.name: node for node in all_deps}
    nodes[self.name] = self  # Add self to the nodes

    # Build dependency and reverse graphs
    dependency_graph = self._build_dependency_graph(nodes)
    reverse_graph = self._build_reverse_graph(dependency_graph)

    # Track completion status and results
    completed = set()
    completion_lock = threading.Lock()
    worker_exceptions = []  # Track exceptions from worker threads
    stop_all_workers = (
        threading.Event()
    )  # Signal to stop all workers on first error

    # Track in-degree for scheduling
    in_degree = {}
    for node_name, dependencies in dependency_graph.items():
        in_degree[node_name] = len(dependencies)
    for node_name in nodes:
        if node_name not in in_degree:
            in_degree[node_name] = 0

    # Queue for nodes ready to execute
    ready_queue = queue.Queue()

    # Add nodes with no dependencies to ready queue
    for node_name, degree in in_degree.items():
        if degree == 0:
            ready_queue.put(node_name)

    def worker():
        """Worker function for thread pool"""
        while not stop_all_workers.is_set():
            try:
                node_name = ready_queue.get(timeout=1)
                # timeout forces to wait 1 second to avoid busy waiting
                if node_name is None:  # Sentinel value to stop worker
                    break
            except queue.Empty:
                continue

            try:
                logger.info(
                    f"Thread {threading.current_thread().name}: executing node '{node_name}'"
                )
                node = nodes[node_name]

                # Execute the node (without recursive child execution since we handle dependencies here)
                if lazy_execution:
                    if not overwrite:
                        raise ValueError(
                            "lazy_execution only works with overwrite=True."
                        )
                    if con is None:
                        raise ValueError(
                            "A DatabaseConnector is required for lazy execution."
                        )

                    if Node._node_manager.should_rerun(node, con):
                        # Time the execution
                        node.lastexecution_start_time = datetime.now()
                        table = node._execute(tables)

                        if (
                            table is not None
                        ):  # Only create table if _execute returns something
                            con.create_table(table, node_name, overwrite=overwrite)
                            table = con.get_dest_table(node_name)

                        node.lastexecution_end_time = datetime.now()
                        node.lastexecution_duration = (
                            node.lastexecution_end_time
                            - node.lastexecution_start_time
                        ).total_seconds()

                        Node._node_manager.update_run_params(node, con)
                    else:
                        table = con.get_dest_table(node_name)
                else:
                    # Time the execution
                    node.lastexecution_start_time = datetime.now()
                    table = node._execute(tables)

                    if (
                        con and table is not None
                    ):  # Only create table if _execute returns something
                        con.create_table(table, node_name, overwrite=overwrite)
                        table = con.get_dest_table(node_name)

                    node.lastexecution_end_time = datetime.now()
                    node.lastexecution_duration = (
                        node.lastexecution_end_time - node.lastexecution_start_time
                    ).total_seconds()

                node.table = table

                with completion_lock:
                    completed.add(node_name)

                    # Update in-degree for dependent nodes and add ready ones to queue
                    for dependent in reverse_graph.get(node_name, set()):
                        in_degree[dependent] -= 1
                        if in_degree[dependent] == 0:
                            # Check if all dependencies are completed
                            deps_completed = all(
                                dep in completed
                                for dep in dependency_graph.get(dependent, set())
                            )
                            if deps_completed:
                                ready_queue.put(dependent)

                # Log completion with timing info
                if node.lastexecution_duration is not None:
                    logger.info(
                        f"Thread {threading.current_thread().name}: completed node '{node_name}' "
                        f"in {node.lastexecution_duration:.3f} seconds"
                    )
                else:
                    logger.info(
                        f"Thread {threading.current_thread().name}: completed node '{node_name}' (cached)"
                    )

            except Exception as e:
                logger.error(f"Error executing node '{node_name}': {str(e)}")
                with completion_lock:
                    # Store exception for main thread
                    worker_exceptions.append(e)
                    # Signal all workers to stop immediately and exit worker loop
                    stop_all_workers.set()
                    break
            finally:
                ready_queue.task_done()

    # Start worker threads
    threads = []
    for i in range(min(n_threads, len(nodes))):
        thread = threading.Thread(target=worker, name=f"PhenexWorker-{i}")
        thread.daemon = True
        thread.start()
        threads.append(thread)

    # Wait for all nodes to complete or for an error to occur
    while (
        len(completed) < len(nodes)
        and not worker_exceptions
        and not stop_all_workers.is_set()
    ):
        threading.Event().wait(0.1)  # Small delay to prevent busy waiting

    if not stop_all_workers.is_set():
        # Time to stop workers and cleanup
        stop_all_workers.set()

    # Check if any worker thread had an exception
    if worker_exceptions:
        # Signal workers to stop
        for _ in threads:
            ready_queue.put(None)
        # Wait for threads to finish
        for thread in threads:
            thread.join(timeout=1)
        # Re-raise the first exception
        raise worker_exceptions[0]

    # Signal workers to stop and wait for them
    for _ in threads:
        ready_queue.put(None)  # Sentinel value to stop workers

    for thread in threads:
        thread.join(timeout=1)

    logger.info(
        f"Node '{self.name}': completed multithreaded execution of {len(nodes)} nodes"
    )
    return self.table

get_codelists()

Get all codelists used in the phenotype definition, including all children / dependent phenotypes.

Returns:

Name	Type	Description
codeslist	List[Codelist]	A list of codelists used in the cohort definition.

Source code in phenex/phenotypes/codelist_phenotype.py

def get_codelists(self) -> List[Codelist]:
    """
    Get all codelists used in the phenotype definition, including all children / dependent phenotypes.

    Returns:
        codeslist: A list of codelists used in the cohort definition.
    """
    codelists = [self.codelist]
    for p in self.children:
        codelists.extend(p.get_codelists())
    return codelists

visualize_dependencies()

Create a text visualization of the dependency graph for this node and its dependencies.

Returns:

Name	Type	Description
str	str	A text representation of the dependency graph

Source code in phenex/node.py

def visualize_dependencies(self) -> str:
    """
    Create a text visualization of the dependency graph for this node and its dependencies.

    Returns:
        str: A text representation of the dependency graph
    """
    lines = [f"Dependencies for Node '{self.name}':"]

    # Get all dependencies
    all_deps = self.dependencies
    nodes = {node.name: node for node in all_deps}
    nodes[self.name] = self  # Add self to the nodes

    # Build dependency graph
    dependency_graph = self._build_dependency_graph(nodes)

    for node_name in sorted(nodes.keys()):
        dependencies = dependency_graph.get(node_name, set())
        if dependencies:
            deps_str = ", ".join(sorted(dependencies))
            lines.append(f"  {node_name} depends on: {deps_str}")
        else:
            lines.append(f"  {node_name} (no dependencies)")

    return "\n".join(lines)