A survey of automatic de-identification of longitudinal clinical narratives <s> Top performing De-identification system in the 2014 competitions <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> BIB001 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> Top performing De-identification system in the 2014 competitions <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> BIB002 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> Top performing De-identification system in the 2014 competitions <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> Top performing De-identification system in the 2014 competitions <s> Display Omitted We introduce a novel use of non-parametric Bayesian HMM for de-identification.The paper gives a thorough discussion of the motivation of designing the model.Our model understands local context cues without significant feature engineering.The model offers competitive performance comparing to the state-of-the-art CRF model. For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance. <s> BIB004
This section provides an overview of some of the best-performing systems developed during the 2014 competition, track 1 (de-identification track). Table 2 presents overall performance micro F-measure for these systems. The micro F-measures are the primary evaluation method for the de-identification task (task 1) ). Overall, the HIPAA-PHI micro F-measure is always better than that of i2b2-PHI micro F-measure. This is mainly because i2b2-PHI categories include more PHI categories to de-identify than HIPAA-PHI categories. Three out of the top ten teams had an F-measure of above 0.9 for the i2b2:PHI categories and four out of the top ten teams had an F-measure of above 0.9 for the HIPAA:PHI categories ). Table 3 presents the best and worst performing PHI categories for these systems. Table 2 . The overall performance of some of the top teams in 2014 competitions. 2014 Top teams includes S1 BIB001 ), S2 ), S3 BIB002 ), S4 BIB003 ) and S5 Table 4 presents examples of techniques used by the top teams in 2014 competitions to de-identify PHIs. One of the main observation with 2014 competitions is that there ). BIB004 ) presented a non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP) and was the only top team to present a machine learning only system to de-identify all PHI's. Handcrafted rules and regular expressions used in the 2014 competitions are particular to the dataset. Table 5 presents examples of regular expressions used in the 2014 competitions for both tokenization (as part of pre-processing) and PHI identification. Evidently, in the 2014 competition, many top systems preferred hybrid approaches with CRF being the most popular machine learning approach. These de-identification systems included data specific rules and regular expressions to tackle PHIs such as ID and CONTACT. Also, most teams performed well in PHI categories, such as NAME, AGE and DATE, and poorly in PROFESSION and LOCATION.