Reference

Algorithm Configuration

The Algorithm configurations are stored in the database and can be managed via the API. Many of the attributes defined on a configuration are limited to a specific set of values, which are defined here.

Features

The Feature enum defines the types of attributes that can be used for matching during the linkage evaluation phase. The following features are supported:

BIRTHDATE: The patient's birthdate in the format YYYY-MM-DD.
MRN: The patient's medical record number.
SSN: The patient's social security number.
SEX: The patient's sex in the format of M, F, or U for unknown.
GENDER: The gender the patient identifies with in the format of "FEMALE", "MALE", "NON_BINARY", "ASKED_DECLINED" or "UNKNOWN".
RACE: The patient's race in the format of "AMERICAN_INDIAN", "ASIAN", "BLACK", "HAWAIIAN", "WHITE", "OTHER", "ASKED_UNKNOWN" or "UNKNOWN".
GIVEN_NAME: The patient's given name, this includes first and middle names.
FIRST_NAME: The patient's first name.
LAST_NAME: The patient's last name.
SUFFIX: The patient's name suffix.
ADDRESS: The patient's address.
CITY: The patient's city.
STATE: The patient's state.
ZIP: The patient's 5 digit zip code.
COUNTY: The patient's county.
TELECOM: The patient's phone, email, fax, or other contact information.
DRIVERS_LICENSE: The patient's driver's license number.

Blocking Key Types

The BlockingKey enum defines the types of blocking values that are generated from the patient data and used during query retrieval. The following blocking key types are supported:

BIRTHDATE (ID: 1): The patients birthdate in the format YYYY-MM-DD.
MRN (ID: 2): The last 4 characters of a patient's medical record number.
SEX (ID: 3): The patient's sex in the format of M, F, or U for unknown.
ZIP (ID: 4): The patient's 5 digit zip code.
FIRST_NAME (ID: 5): The first 4 characters of the patient's first name.
LAST_NAME (ID: 6): The first 4 characters of the patient's last name.
ADDRESS (ID: 7): The first 4 characters of the patient's address.

Evaluation Functions

These are the functions that can be used to evaluate the matching results as a collection, thus determining it the incoming payload is a match or not to an existing Patient record.

func:recordlinker.linking.matchers.eval_perfect_match: Determines whether a given set of feature comparisons represent a 'perfect' match (i.e. all features that were compared match in whatever criteria was specified).
func:recordlinker.linking.matchers.eval_log_odds_cutoff: Determines whether a given set of feature comparisons matches enough to be the result of a true patient link instead of just random chance. This is represented using previously computed log-odds ratios. A true_match_threshold needs to be set in the kwargs parameter to determine the minimum log-odds ratio that is considered a match. Example: {"kwargs": {"true_match_threshold": 12.5}}

Feature Matching Functions

These are the functions that can be used to compare the values of two features to determine if they are a match or not.

Note: When most features are compared, we are doing a 1 to 1 comparison (e.g. "M" == "M"). However, some features have the ability to have multiple values (e.g. GIVEN_NAME), thus feature matching is designed to compare one list of values to another list of values. For example, an incoming record could have a GIVEN_NAME of ["John", "Dean"] and we could be comparing them to an existing Patient with the GIVEN_NAME of ["John", "D"].

func:recordlinker.linking.matchers.feature_match_any: Determines if any of the features are a direct match.
func:recordlinker.linking.matchers.feature_match_all: Determines if all of the features are a direct match.
func:recordlinker.linking.matchers.feature_match_fuzzy_string: Determines if the features are a fuzzy match based on a string comparison. JaroWinkler, Levenshtein and Damerau-Levenshtein are supported, with JaroWinkler as the default. Use the kwargs parameter to specify the desired algorithm and thresholds. Example: {"kwargs": {"similarity_measure": "levenshtein", "thresholds": {"FIRST_NAME": 0.8}}}
func:recordlinker.linking.matchers.feature_match_log_odds_fuzzy_compare: Similar to the above function, but uses a log-odds ratio to determine if the features are a match probabilistically. This is useful when wanting to more robustly compare features by incorporating their predictive power (i.e., the log-odds ratio for a feature represents how powerful of a predictor that feature is in determining whether two patient records are a true match, as opposed to a match by random chance). Use the kwargs parameter to specify the fuzzy match threshold and log-odds ratio based on training. Example: {"kwargs": {"thresholds": {"FIRST_NAME": 0.8}, "log_odds": {"FIRST_NAME": 6.8}}}