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eds_scikit.event.icd10

conditions_from_icd10 

conditions_from_icd10(condition_occurrence: DataFrame, visit_occurrence: Optional[DataFrame] = None, codes: Optional[Dict[str, Union[str, List[str]]]] = None, date_from_visit: bool = True, additional_filtering: Dict[str, Any] = None, date_min: Optional[datetime] = None, date_max: Optional[datetime] = None) -> DataFrame

Phenotyping based on ICD-10 codes.

PARAMETER DESCRIPTION
condition_occurrence

condition_occurrence OMOP DataFrame.

TYPE: DataFrame

visit_occurrence

visit_occurrence OMOP DataFrame, only necessary if date_from_visit is set to True.

TYPE: Optional[DataFrame] DEFAULT: None

codes

Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are at least one of the following:

  • exact: To match the codes in codes["exact"] exactly
  • prefix: To match the codes in codes["prefix"] as prefixes
  • regex: To match the codes in codes["regex"] as regexes You can combine any of those keys.

TYPE: Dict[str, Union[str, List[str]]] DEFAULT: None

date_from_visit

If set to True, uses visit_start_datetime as the code datetime

TYPE: bool DEFAULT: True

additional_filtering

An optional dictionary to filter the resulting DataFrame.

Keys should be column names on which to filter, and values should be either

  • A single value
  • A list or set of values.

Default filetring is condition_status_source_value in {"DP", "DAS", "DR"}

TYPE: Dict[str, Any] DEFAULT: None

date_min

The minimum code datetime to keep. Depends on the date_from_visit flag

TYPE: Optional[datetime] DEFAULT: None

date_max

The minimum code datetime to keep. Depends on the date_from_visit flag

TYPE: Optional[datetime] DEFAULT: None

RETURNS DESCRIPTION
DataFrame

"event" DataFrame including the following columns:

  • t_start: If date_from_visit is set to False, contains condition_start_datetime, else contains visit_start_datetime
  • t_end: If date_from_visit is set to False, contains condition_start_datetime, else contains visit_end_datetime
  • concept : contaning values from codes.keys()
  • value : The extracted ICD-10 code.
  • visit_occurrence_id : the visit_occurrence_id from the visit which contains the ICD-10 code.
Source code in eds_scikit/event/icd10.py 
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def conditions_from_icd10(
    condition_occurrence: DataFrame,
    visit_occurrence: Optional[DataFrame] = None,
    codes: Optional[Dict[str, Union[str, List[str]]]] = None,
    date_from_visit: bool = True,
    additional_filtering: Dict[str, Any] = None,
    date_min: Optional[datetime] = None,
    date_max: Optional[datetime] = None,
) -> DataFrame:
    """

    Phenotyping based on ICD-10 codes.

    Parameters
    ----------
    condition_occurrence : DataFrame
        `condition_occurrence` OMOP DataFrame.
    visit_occurrence : Optional[DataFrame]
        `visit_occurrence` OMOP DataFrame, only necessary if `date_from_visit` is set to `True`.
    codes : Dict[str, Union[str, List[str]]]
        Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are
        at least one of the following:

        - `exact`: To match the codes in `codes["exact"]` **exactly**
        - `prefix`: To match the codes in `codes["prefix"]` **as prefixes**
        - `regex`: To match the codes in `codes["regex"]` **as regexes**
        You can combine any of those keys.
    date_from_visit : bool
        If set to `True`, uses `visit_start_datetime` as the code datetime
    additional_filtering : Dict[str, Any]
        An optional dictionary to filter the resulting DataFrame.

        Keys should be column names on which to filter, and values should be either

        - A single value
        - A list or set of values.

        Default filetring is condition_status_source_value in {"DP", "DAS", "DR"}
    date_min : Optional[datetime]
        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**
    date_max : Optional[datetime]
        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**

    Returns
    -------
    DataFrame
        "event" DataFrame including the following columns:

        - `t_start`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`,
        else contains `visit_start_datetime`
        - `t_end`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`,
        else contains `visit_end_datetime`
        - `concept` : contaning values from `codes.keys()`
        - `value` : The extracted ICD-10 code.
        - `visit_occurrence_id` : the `visit_occurrence_id` from the visit which contains the ICD-10 code.
    """  # noqa: E501
    if additional_filtering is None:
        additional_filtering = dict()

    DEFAULT_FILTERING = dict(condition_status_source_value={"DP", "DAS", "DR"})
    DEFAULT_FILTERING.update(additional_filtering)

    condition_columns = dict(
        code_source_value="condition_source_value",
        code_start_datetime="condition_start_datetime",
        code_end_datetime="condition_start_datetime",
    )
    events = []

    for concept, code_dict in codes.items():
        tmp_df = event_from_code(
            df=condition_occurrence,
            columns=condition_columns,
            visit_occurrence=visit_occurrence,
            concept=concept,
            codes=code_dict,
            date_from_visit=date_from_visit,
            additional_filtering=DEFAULT_FILTERING,
            date_min=date_min,
            date_max=date_max,
        )

        events.append(tmp_df)

    framework = get_framework(condition_occurrence)
    return framework.concat(events)
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