cardea.problem_definition.ProlongedLengthOfStay

class cardea.problem_definition.ProlongedLengthOfStay(t=7)

Defines the problem of length of stay

Predicting whether a patient stayed in the hospital more or less than a week (by default).

Parameters

• target_label_column_name (str) – The target label of the prediction problem.

• target_entity (str) – Name of the entity containing the target label.

• cutoff_time_label (str) – The cutoff time label of the prediction problem.

• cutoff_entity (str) – Name of the entity containing the cutoff time label.

• prediction_type (str) – The type of the machine learning prediction.

__init__(t=7)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([t])	Initialize self.
check_for_missing_values_in_target_label(…)	Checks if there is a missing value in the target label.
check_target_label(entity_set, …)	Checks if target label exists in the entity set.
generate_cutoff_times(es)	Generates cutoff times for the prediction problem.
generate_target_label(es)	Generates target labels in the case of having missing label in the entityset.
unify_cutoff_time_admission_time(es, …)	Process records in the entity that contains cutoff times based on shared days and time.
unify_cutoff_time_discharge_time(es, …)	Process records in the entity that contains cutoff times based on shared days and time.
unify_cutoff_times_days_admission_time(df, …)	Unify records cutoff times based on shared days.
unify_cutoff_times_days_discharge_time(df, …)	Unify records cutoff times based on shared days.
unify_cutoff_times_hours_admission_time(df, …)	Unify records cutoff times based on shared time.
unify_cutoff_times_hours_discharge_time(df, …)	Unify records cutoff times based on shared time.

Attributes

conn
cutoff_entity
cutoff_time_label
prediction_type
target_entity
target_label_column_name
updated_es

cardea.problem_definition cardea.problem_definition.ProlongedLengthOfStay.generate_cutoff_times