A survey of automatic de-identification of longitudinal clinical narratives <s> Overview of the recent Competitions <s> Display Omitted New corpus of 1304 de-identified medical records.Longitudinal records represent 296 patients.Three patient cohorts: with Coronary Artery Disease (CAD), no CAD, develop CAD.Overview of novel uses for this corpus. The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured a new longitudinal corpus of 1304 records representing 296 diabetic patients. The corpus contains three cohorts: patients who have a diagnosis of coronary artery disease (CAD) in their first record, and continue to have it in subsequent records; patients who do not have a diagnosis of CAD in the first record, but develop it by the last record; patients who do not have a diagnosis of CAD in any record. This paper details the process used to select records for this corpus and provides an overview of novel research uses for this corpus. This corpus is the only annotated corpus of longitudinal clinical narratives currently available for research to the general research community. <s> BIB001 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> Overview of the recent Competitions <s> Display Omitted NLP task focused on identifying risk factors over time in clinical narratives.Corpus of 1304 longitudinal medical records for 296 patients."Light" annotation task for domain expert annotators.Gold standard created through voting.Corpus used for track 2 of 2014 i2b2/UTHealth NLP Shared Task. The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm to annotate a set of 1304 longitudinal medical records describing 296 patients for risk factors and the times they were present. We designed the annotation task for this track with the goal of balancing annotation load and time with quality, so as to generate a gold standard corpus that can benefit a clinically-relevant task. We applied light annotation procedures and determined the gold standard using majority voting. On average, the agreement of annotators with the gold standard was above 0.95, indicating high reliability. The resulting document-level annotations generated for each record in each longitudinal EMR in this corpus provide information that can support studies of progression of heart disease risk factors in the included patients over time. These annotations were used in the Risk Factor track of the 2014 i2b2/UTHealth shared task. Participating systems achieved a mean micro-averaged F1 measure of 0.815 and a maximum F1 measure of 0.928 for identifying these risk factors in patient records. <s> BIB002 </s> A survey of automatic de-identification of longitudinal clinical narratives <s> Overview of the recent Competitions <s> Abstract Clinical narratives (the text notes found in patients’ medical records) are important information sources for secondary use in research. However, in order to protect patient privacy, they must be de-identified prior to use. Manual de-identification is considered to be the gold standard approach but is tedious, expensive, slow, and impractical for use with large-scale clinical data. Automated or semi-automated de-identification using computer algorithms is a potentially promising alternative. The Informatics Institute of the University of Alabama at Birmingham is applying de-identification to clinical data drawn from the UAB hospital’s electronic medical records system before releasing them for research. We participated in a shared task challenge by the Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDoC Individualized Domains (N-GRID) at the de-identification regular track to gain experience developing our own automatic de-identification tool. We focused on the popular and successful methods from previous challenges: rule-based, dictionary-matching, and machine-learning approaches. We also explored new techniques such as disambiguation rules, term ambiguity measurement, and used multi-pass sieve framework at a micro level. For the challenge’s primary measure (strict entity), our submissions achieved competitive results (f-measures: 87.3%, 87.1%, and 86.7%). For our preferred measure (binary token HIPAA), our submissions achieved superior results (f-measures: 93.7%, 93.6%, and 93%). With those encouraging results, we gain the confidence to improve and use the tool for the real de-identification task at the UAB Informatics Institute. <s> BIB003
This section provides a brief overview of the 2014 and 2016 competitions. Both 2014 and 2016 competitions used longitudinal data as it enables the understanding of each patient's medical records and their progress over time (months or years). It provides a more comprehensive clinical summary of patient-based clinical experience than encounter-based or provider-based data. However, the main issue with using longitudinal data is that it contains an abundance of personal information on each patient. Collectively, longitudinal data is more likely to allow the piecing together of patient identity ;  ). Also, both competitions used "as is" raw data and reflects the realities of what the de-identification system would have to handle in reality ). Data was split 60/40 and distributed into two parts. Initially, the training set for the development of the system was distributed followed by testing set on a later date. For more details on the 2014 competition see BIB001 BIB001 ),  ), ), BIB002 and ). Details of the 2016 competition is presented in: ), BIB003 BIB003 ) and  ).