Detect Phi

comprehendmedical_detect_phi

R Documentation

Inspects the clinical text for protected health information (PHI) entities and returns the entity category, location, and confidence score for each entity

Description

Inspects the clinical text for protected health information (PHI) entities and returns the entity category, location, and confidence score for each entity. Amazon Comprehend Medical only detects entities in English language texts.

Usage

comprehendmedical_detect_phi(Text)

Arguments

Text

[required] A UTF-8 text string containing the clinical content being examined for PHI entities.

Value

A list with the following syntax:

list(
  Entities = list(
    list(
      Id = 123,
      BeginOffset = 123,
      EndOffset = 123,
      Score = 123.0,
      Text = "string",
      Category = "MEDICATION"|"MEDICAL_CONDITION"|"PROTECTED_HEALTH_INFORMATION"|"TEST_TREATMENT_PROCEDURE"|"ANATOMY"|"TIME_EXPRESSION"|"BEHAVIORAL_ENVIRONMENTAL_SOCIAL",
      Type = "NAME"|"DX_NAME"|"DOSAGE"|"ROUTE_OR_MODE"|"FORM"|"FREQUENCY"|"DURATION"|"GENERIC_NAME"|"BRAND_NAME"|"STRENGTH"|"RATE"|"ACUITY"|"TEST_NAME"|"TEST_VALUE"|"TEST_UNITS"|"TEST_UNIT"|"PROCEDURE_NAME"|"TREATMENT_NAME"|"DATE"|"AGE"|"CONTACT_POINT"|"PHONE_OR_FAX"|"EMAIL"|"IDENTIFIER"|"ID"|"URL"|"ADDRESS"|"PROFESSION"|"SYSTEM_ORGAN_SITE"|"DIRECTION"|"QUALITY"|"QUANTITY"|"TIME_EXPRESSION"|"TIME_TO_MEDICATION_NAME"|"TIME_TO_DX_NAME"|"TIME_TO_TEST_NAME"|"TIME_TO_PROCEDURE_NAME"|"TIME_TO_TREATMENT_NAME"|"AMOUNT"|"GENDER"|"RACE_ETHNICITY"|"ALLERGIES"|"TOBACCO_USE"|"ALCOHOL_CONSUMPTION"|"REC_DRUG_USE",
      Traits = list(
        list(
          Name = "SIGN"|"SYMPTOM"|"DIAGNOSIS"|"NEGATION"|"PERTAINS_TO_FAMILY"|"HYPOTHETICAL"|"LOW_CONFIDENCE"|"PAST_HISTORY"|"FUTURE",
          Score = 123.0
        )
      ),
      Attributes = list(
        list(
          Type = "NAME"|"DX_NAME"|"DOSAGE"|"ROUTE_OR_MODE"|"FORM"|"FREQUENCY"|"DURATION"|"GENERIC_NAME"|"BRAND_NAME"|"STRENGTH"|"RATE"|"ACUITY"|"TEST_NAME"|"TEST_VALUE"|"TEST_UNITS"|"TEST_UNIT"|"PROCEDURE_NAME"|"TREATMENT_NAME"|"DATE"|"AGE"|"CONTACT_POINT"|"PHONE_OR_FAX"|"EMAIL"|"IDENTIFIER"|"ID"|"URL"|"ADDRESS"|"PROFESSION"|"SYSTEM_ORGAN_SITE"|"DIRECTION"|"QUALITY"|"QUANTITY"|"TIME_EXPRESSION"|"TIME_TO_MEDICATION_NAME"|"TIME_TO_DX_NAME"|"TIME_TO_TEST_NAME"|"TIME_TO_PROCEDURE_NAME"|"TIME_TO_TREATMENT_NAME"|"AMOUNT"|"GENDER"|"RACE_ETHNICITY"|"ALLERGIES"|"TOBACCO_USE"|"ALCOHOL_CONSUMPTION"|"REC_DRUG_USE",
          Score = 123.0,
          RelationshipScore = 123.0,
          RelationshipType = "EVERY"|"WITH_DOSAGE"|"ADMINISTERED_VIA"|"FOR"|"NEGATIVE"|"OVERLAP"|"DOSAGE"|"ROUTE_OR_MODE"|"FORM"|"FREQUENCY"|"DURATION"|"STRENGTH"|"RATE"|"ACUITY"|"TEST_VALUE"|"TEST_UNITS"|"TEST_UNIT"|"DIRECTION"|"SYSTEM_ORGAN_SITE"|"AMOUNT"|"USAGE"|"QUALITY",
          Id = 123,
          BeginOffset = 123,
          EndOffset = 123,
          Text = "string",
          Category = "MEDICATION"|"MEDICAL_CONDITION"|"PROTECTED_HEALTH_INFORMATION"|"TEST_TREATMENT_PROCEDURE"|"ANATOMY"|"TIME_EXPRESSION"|"BEHAVIORAL_ENVIRONMENTAL_SOCIAL",
          Traits = list(
            list(
              Name = "SIGN"|"SYMPTOM"|"DIAGNOSIS"|"NEGATION"|"PERTAINS_TO_FAMILY"|"HYPOTHETICAL"|"LOW_CONFIDENCE"|"PAST_HISTORY"|"FUTURE",
              Score = 123.0
            )
          )
        )
      )
    )
  ),
  PaginationToken = "string",
  ModelVersion = "string"
)

Request syntax

svc$detect_phi(
  Text = "string"
)