De-identification of health records using Anonym: Effectiveness and robustness across datasets

Artificial Intelligence in Medicine

Volumn 61, Issue 3, 2014, Pages 145-151

  Zuccon, Guido ^a,b   Kotzur, Daniel ^a   Nguyen, Anthony ^a   Bergheim, Anton ^c  

^a ROYAL BRISBANE AND WOMEN'S HOSPITAL   (Australia)

^b QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^c CANCER INSTITUTE NSW   (Australia)

Author keywords

Conditional random fields; De identification; Health records; Pattern matching

Indexed keywords

OPTICAL CHARACTER RECOGNITION; PATTERN MATCHING;

AMOUNT OF INFORMATION; CONDITIONAL RANDOM FIELD; DE-IDENTIFICATION; ELECTRONIC HEALTH RECORD; HEALTH RECORDS; METHODS AND MATERIALS; PATTERN-MATCHING TECHNIQUE; PERSONAL HEALTH INFORMATIONS;

RANDOM PROCESSES;

ALGORITHM; ANALYZER; ANONYM; ARTICLE; CANCER REGISTRY; DATA ANALYSIS; DECISION TREE; ELECTRONIC MEDICAL RECORD; HEALTH PRACTITIONER; HIDDEN MARKOV MODEL; MACHINE LEARNING; MEDICAL INFORMATION; MEDICAL RECORD; MOBILE PHONE; PRIORITY JOURNAL; RANDOM FOREST; SUPPORT VECTOR MACHINE; UNITED STATES; ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAM; CONFIDENTIALITY; FACTUAL DATABASE; HEALTH INSURANCE; HUMAN; PATIENT IDENTIFICATION; STATISTICS AND NUMERICAL DATA;

ARTIFICIAL INTELLIGENCE; CONFIDENTIALITY; DATABASES, FACTUAL; ELECTRONIC HEALTH RECORDS; HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT; HEALTH RECORDS, PERSONAL; HUMANS; PATIENT IDENTIFICATION SYSTEMS; SOFTWARE; UNITED STATES;

EID: 84904305048     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2014.03.006     Document Type: Article

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