Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics.
PurposeProjects involving collaborations between different institutions require data security via selective de-identification of words or phrases. A semi-automated de-identification tool was developed and evaluated on different types of medical reports natively and after adapting the algorithm to the text structure.Materials and MethodsA semi-automated de-identification tool was developed and evaluated for its sensitivity and specificity in detecting sensitive content in written reports. Data from 4671 pathology reports (4105 + 566 in two different formats), 2804 medical reports, 1008 operation reports, and 6223 radiology reports of 1167 patients suffering from breast cancer were de-identified. The content was itemized into four categories: direct identifiers (name, address), indirect identifiers (date of birth/operation, medical ID, etc.), medical terms, and filler words. The software was tested natively (without training) in order to establish a baseline. The reports were manually edited and the model re-trained for the next test set. After manually editing 25, 50, 100, 250, 500 and if applicable 1000 reports of each type re-training was applied.ResultsIn the native test, 61.3 % of direct and 80.8 % of the indirect identifiers were detected. The performance (P) increased to 91.4 % (P25), 96.7 % (P50), 99.5 % (P100), 99.6 % (P250), 99.7 % (P500) and 100 % (P1000) for direct identifiers and to 93.2 % (P25), 97.9 % (P50), 97.2 % (P100), 98.9 % (P250), 99.0 % (P500) and 99.3 % (P1000) for indirect identifiers. Without training, 5.3 % of medical terms were falsely flagged as critical data. The performance increased, after training, to 4.0 % (P25), 3.6 % (P50), 4.0 % (P100), 3.7 % (P250), 4.3 % (P500), and 3.1 % (P1000). Roughly 0.1 % of filler words were falsely flagged.ConclusionTraining of the developed de-identification tool continuously improved its performance. Training with roughly 100 edited reports enables reliable detection and labeling of sensitive data in different types of medical reports.Key Points:· Collaborations between different institutions require de-identification of patients' data. · Software-based de-identification of content-sensitive reports grows in importance as a result of 'Big data'. · A de-identification software was developed and tested natively and after training. · The proposed de-identification software worked quite reliably, following training with roughly 100 edited reports. · A final check of the texts by an authorized person remains necessary.Citation Format· Seuss H, Dankerl P, Ihle M et al. Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics. Fortschr Röntgenstr 2017; 189: 661 - 671.
Department of Radiology, University Hospital Erlangen, Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
Hannes
H
Seuss
Department of Radiology, University Hospital Erlangen, Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
Peter
P
Dankerl
Text Analytics, Averbis GmbH, Freiburg, Germany.
Matthias
M
Ihle
Text Analytics, Averbis GmbH, Freiburg, Germany.
Andrea
A
Grandjean
Department of Neurology, Klinikum Nuremberg, Nuremberg, Germany.
Rebecca
R
Hammon
Department of Neuroradiology, University Hospital Erlangen, Erlangen, Germany.
Nicola
N
Kaestle
Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany.
Peter A
PA
Fasching
Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Christian
C
Maier
Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Jan
J
Christoph
Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Martin
M
Sedlmayr
Department of Radiology, University Hospital Erlangen, Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
Michael
M
Uder
Department of Radiology, University Hospital Erlangen, Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
Alexander
A
Cavallaro
Department of Radiology, University Hospital Erlangen, Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
Matthias
M
Hammon
D000465:Algorithms; D016494:Computer Security; D003219:Confidentiality; D057286:Electronic Health Records; D005858:Germany; D006801:Humans; D033183:Interdisciplinary Communication; D007373:Interinstitutional Relations; D000070516:Intersectoral Collaboration; D015203:Reproducibility of Results; D058028:Research Report; D012984:Software