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eds_scikit.event.diabetes

DEFAULT_DIABETE_FROM_ICD10_CONFIG module-attribute 

DEFAULT_DIABETE_FROM_ICD10_CONFIG = dict(codes=dict(DIABETES_TYPE_I=dict(prefix='E10'), DIABETES_TYPE_II=dict(prefix='E11'), DIABETES_MALNUTRITION=dict(prefix='E12'), DIABETES_IN_PREGNANCY=dict(prefix='O24'), OTHER_DIABETES_MELLITUS=dict(prefix=['E13', 'E14']), DIABETES_INSIPIDUS=dict(exact=['E232', 'N251'])), date_from_visit=True, additional_filtering=dict(condition_status_source_value={'DP', 'DAS'}))

Default parameters feeded to conditions_from_icd10()

diabetes_from_icd10 

diabetes_from_icd10(condition_occurrence: DataFrame, visit_occurrence: DataFrame, date_min: Optional[datetime] = None, date_max: Optional[datetime] = None, codes: Dict[str, Union[str, List[str]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG['codes'], date_from_visit: bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG['date_from_visit'], additional_filtering: Dict[str, Any] = DEFAULT_DIABETE_FROM_ICD10_CONFIG['additional_filtering']) -> DataFrame

Wrapper around the conditions_from_icd10() function. Check the default configuration to see the used parameters

PARAMETER DESCRIPTION
condition_occurrence

OMOP-like condition occurrence DataFrame

TYPE: DataFrame

visit_occurrence

OMOP-like visit_occurrence DataFrame

TYPE: Optional[DataFrame]

date_min

Lower temporal bound

TYPE: Optional[datetime] DEFAULT: None

date_max

Upper temporal bound

TYPE: Optional[datetime] DEFAULT: None

codes

Dictionary of ICD-10 used for phenotyping

TYPE: Optional[Dict[str, Union[str, List[str]]]] DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['codes']

date_from_visit

If true, use the visit_[start/end]_datetime for filtering. Else, use condition_start_datetime

TYPE: bool, by default True DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['date_from_visit']

additional_filtering

A dictionary to perform additional filtering.

  • Each key should be a valid column name from condition_occurrence
  • Each value should be a value / set of values / list of values For each pair (key, value), filtering is done as condition_occurrence[condition_occurrence[k].isin(v)]

TYPE: Dict[str, Any] DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['additional_filtering']

RETURNS DESCRIPTION
DataFrame

Event DataFrame in long format (with a concept and a value column). The concept column contains one of the following:

  • DIABETES_TYPE_I
  • DIABETES_TYPE_II
  • DIABETES_MALNUTRITION
  • DIABETES_IN_PREGNANCY
  • OTHER_DIABETES_MELLITUS
  • DIABETES_INSIPIDUS The value column contains the corresponding ICD-10 code that was extracted
Source code in eds_scikit/event/diabetes.py 
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def diabetes_from_icd10(
    condition_occurrence: DataFrame,
    visit_occurrence: DataFrame,
    date_min: Optional[datetime] = None,
    date_max: Optional[datetime] = None,
    codes: Dict[str, Union[str, List[str]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG[
        "codes"
    ],
    date_from_visit: bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG["date_from_visit"],
    additional_filtering: Dict[str, Any] = DEFAULT_DIABETE_FROM_ICD10_CONFIG[
        "additional_filtering"
    ],
) -> DataFrame:
    """
    Wrapper around the [conditions_from_icd10()][eds_scikit.event.icd10.conditions_from_icd10] function.
    Check the [default configuration][eds_scikit.event.diabetes.DEFAULT_DIABETE_FROM_ICD10_CONFIG] to see
    the used parameters

    Parameters
    ----------
    condition_occurrence
        OMOP-like condition occurrence DataFrame
    visit_occurrence : Optional[DataFrame]
        OMOP-like visit_occurrence DataFrame
    date_min : Optional[datetime]
        Lower temporal bound
    date_max : Optional[datetime]
        Upper temporal bound
    codes : Optional[Dict[str, Union[str, List[str]]]]
        Dictionary of ICD-10 used for phenotyping
    date_from_visit : bool, by default True
        If true, use the `visit_[start/end]_datetime` for filtering. Else, use `condition_start_datetime`
    additional_filtering : Dict[str, Any]
        A dictionary to perform additional filtering.

        - **Each key** should be a valid column name from `condition_occurrence`
        - **Each value** should be a value / set of values / list of values
        For each pair (key, value), filtering is done as `condition_occurrence[condition_occurrence[k].isin(v)]`

    Returns
    -------
    DataFrame
        Event DataFrame in **long** format (with a `concept` and a `value` column).
        The `concept` column contains one of the following:

        - DIABETES_TYPE_I
        - DIABETES_TYPE_II
        - DIABETES_MALNUTRITION
        - DIABETES_IN_PREGNANCY
        - OTHER_DIABETES_MELLITUS
        - DIABETES_INSIPIDUS
        The `value` column contains the corresponding ICD-10 code that was extracted
    """
    diabetes = conditions_from_icd10(
        condition_occurrence=condition_occurrence,
        visit_occurrence=visit_occurrence,
        date_min=date_min,
        date_max=date_max,
        codes=codes,
        date_from_visit=date_from_visit,
        additional_filtering=additional_filtering,
    )

    diabetes["value"] = diabetes["concept"]
    diabetes["concept"] = "DIABETES_FROM_ICD10"

    return diabetes
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