A survey of automatic de-identification of longitudinal clinical narratives <s> 2014 i2b2/UTHealth Shared task <s> Display Omitted We present a method for automatic de-identification of clinical narratives.We propose and validate a two-pass tagging method to improve PHI entity recognition.We have shown that automated de-identification is comparable to human benchmark. A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Information (PHI). We describe the methods developed and evaluated as part of the i2b2/UTHealth 2014 challenge to identify PHI defined by 25 entity types in longitudinal clinical narratives. Our approach combines knowledge-driven (dictionaries and rules) and data-driven (machine learning) methods with a large range of features to address de-identification of specific named entities. In addition, we have devised a two-pass recognition approach that creates a patient-specific run-time dictionary from the PHI entities identified in the first step with high confidence, which is then used in the second pass to identify mentions that lack specific clues. The proposed method achieved the overall micro F1-measures of 91% on strict and 95% on token-level evaluation on the test dataset (514 narratives). Whilst most PHI entities can be reliably identified, particularly challenging were mentions of Organizations and Professions. Still, the overall results suggest that automated text mining methods can be used to reliably process clinical notes to identify personal information and thus providing a crucial step in large-scale de-identification of unstructured data for further clinical and epidemiological studies. <s> BIB001 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> 2014 i2b2/UTHealth Shared task <s> Display Omitted A model to automatically detect protected health information in clinical texts.Machine learning techniques combined with keywords and rule-based approaches.7 main PHI categories with 25 associated sub-categories are identified.Achieving an overall micro-averaged F-measure of 93.6%.The winner of 2014 i2b2 de-identification challenge task. This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge. <s> BIB002 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> 2014 i2b2/UTHealth Shared task <s> Display Omitted The method used in our work is completely machine-learning-based.Pattern-matching techniques are used in the preprocessing module.Three groups of features are extracted to train the de-identifier model.Missing errors are the main component of the errors in system output. De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional random fields (CRFs). A preprocessing module, which tokenizes the medical records using regular expressions and an off-the-shelf tokenizer, is introduced, and three groups of features are extracted to train the de-identifier model. The experiment shows that our system is effective in the de-identification of medical records, achieving a micro-F1 of 0.9232 at the i2b2 strict entity evaluation level. <s> BIB003 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> 2014 i2b2/UTHealth Shared task <s> Display Omitted We proposed a hybrid system to automatically de-identify electronic medical records.PHIs are identified by token-level and character-level conditional random fields.The character-level CRFs is used to avoid boundary errors caused by tokenization.Our system achieves an F-score of 91.24% on 2014 i2b2 corpus, which is top-ranked. De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informatics for Integrating Biology and Bedside) clinical natural language processing (NLP) challenge sets up a track for de-identification (track 1). In this study, we propose a hybrid system based on both machine learning and rule approaches for the de-identification track. In our system, PHI instances are first identified by two (token-level and character-level) conditional random fields (CRFs) and a rule-based classifier, and then are merged by some rules. Experiments conducted on the i2b2 corpus show that our system submitted for the challenge achieves the highest micro F-scores of 94.64%, 91.24% and 91.63% under the "token", "strict" and "relaxed" criteria respectively, which is among top-ranked systems of the 2014 i2b2 challenge. After integrating some refined localization dictionaries, our system is further improved with F-scores of 94.83%, 91.57% and 91.95% under the "token", "strict" and "relaxed" criteria respectively. <s> BIB004
The 2014 i2b2/UTHealth shared task used diabetic patient longitudinal medical records obtained from two large hospitals. The 2014 competition included four tracks. Track 1, the de-identification track, is the primary interest of this survey. Track 1 was attempted by ten teams who submitted 22 system outputs for evaluation. The best performing system of each team was evaluated, and three of the systems achieved F-measures over 0.90 ). A number of the top performing systems used a hybrid model combining regular expressions and rules with machine learning based systems BIB001 ; ; BIB002 ). The conditional random field was the most popular machine learning system. Categories such as ZIP, AGE and PHONE were considered easier, and PROFESSION and LOCATION categories proved to be more difficult ). It was no surprise that overall results for i2b2-PHI categories are worse than that of HIPAA-PHI categories, as i2b2-PHI had much more specific categories than HIPAA-PHI BIB001 ; BIB003 ; BIB004 ; BIB002 ).