A survey of automatic de-identification of longitudinal clinical narratives <s> Recent development using Competitions Data <s> Display Omitted We used a data-driven methodology for rule-based information extraction.This was applied to the i2b2 cardiac risk factors challenge.It exploited an agile text mining system augmented with a simple classifier.Evaluation on an unseen test set gave an F-score of 91.7%. This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system. <s> BIB001 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> Recent development using Competitions Data <s> Electronic health record (EHR) is a digital data format that collects electronic health information about an individual patient or population. To enhance the meaningful use of EHRs, information extraction techniques have been developed to recognize clinical concepts mentioned in EHRs. Nevertheless, the clinical judgment of an EHR cannot be known solely based on the recognized concepts without considering its contextual information. In order to improve the readability and accessibility of EHRs, this work developed a section heading recognition system for clinical documents. In contrast to formulating the section heading recognition task as a sentence classification problem, this work proposed a token-based formulation with the conditional random field (CRF) model. A standard section heading recognition corpus was compiled by annotators with clinical experience to evaluate the performance and compare it with sentence classification and dictionary-based approaches. The results of the experiments showed that the proposed method achieved a satisfactory F-score of 0.942, which outperformed the sentence-based approach and the best dictionary-based system by 0.087 and 0.096, respectively. One important advantage of our formulation over the sentence-based approach is that it presented an integrated solution without the need to develop additional heuristics rules for isolating the headings from the surrounding section contents. <s> BIB002 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> Recent development using Competitions Data <s> De-identification in electronic health records is a prerequisite to distribute medical records for further clinical data processing or mining. In this paper, we introduce a framework based on recurrent neural network to solve the de-identification problem, and compare state-of-the-art methods with our framework. It is integrated, which includes records skeleton generation, chunk representation and protected information labeling. We evaluate our framework on three datasets involving two English datasets from i2b2 de-identification challenge and a Chinese dataset we created. To the best of our knowledge, we are the first to apply RNN model to the Chinese de-identification problem. The experimental results indicate that our framework not only achieves high performance but also has strong generalization ability. <s> BIB003
This section presents development and improvement in automatic de-identification of EHR using the 2014 competition datasets. At the stage of this study, there was only evidence of the availability and use of 2014 competition dataset. The 2016 competition data is not available as an open source. There are many examples -such as ), ), BIB001 BIB001 ), BIB002 BIB002 -where the 2014 dataset is used for experiments. This paper will focus only on the work relating to de-identification systems (similar to that of track 1). Table 9 presents examples of techniques used by systems developed using 2014 dataset to de-identify PHIs. In comparison to Table 4 there is a definite shift to using deep learning methods such as LSTM and recurrent neural networks (RNN). Although the expected 95% accuracy was not achieved in all PHIs by these systems, the overall F-measure was above 0.9. Also, all systems reported better F-measures to that of the 2014 competitions systems for poor performing PHIs such as PROFESSION and LOCATION. The change in trend, using deep learning only systems are welcoming and will be of interest to explore further. (S8,S9) ; Bi-LSTM (S6); LSTM (S10) Location-Others CRF (S7,S9); Bi-LSTM (S6); LSTM (S10) PROFESSION Profession CRF (S7); CRF + Dic (S8,S9); Bi-LSTM (S6); LSTM (S10) Table 9 . Techniques used to de-identify PHI categories in systems developed after the competitions using the 2014 dataset. These systems includes S11 (Phuong and Chau (2016)), S12 ), S13  ) and S14 BIB003 ). PHI categories Sub-categories Techniques DATE Date CRF + Rules (S11); LSTM (S12); RNN (S13, S14) NAME Doctor, Patient CRF + Rules (S11); LSTM (S12); RNN (S13, S14) Username CRF (S11); LSTM (S12); RNN (S13, S14) AGE Age CRF + Rules (S11); LSTM (S12); RNN (S13, S14) CONTACT Phone CRF + Rules (S11); LSTM (S12); RNN (S13, S14) Fax, Email, URL CRF (S11); LSTM (S12); RNN (S13, S14) ID all sub-categories CRF (S11); LSTM (S12); RNN (S13, S140 LOCATION all sub-categories CRF + Rules (S11); LSTM (S12); RNN (S13, S14) PROFESSION Profession CRF (S11); LSTM (S12); RNN (S13, S14) 