## 4 NLP Competitions and Datasets for Clinical Text
### Competitions  
Competitions and datasets are considered assets in NLP tasks. Although most of these challenges are for data from the general domains, clinical domain-related challenges have come up in the past. Clinical-NER based competitions were mostly focused on the de-identification of Personal Health Information (PHI). In 2014, there was a i2b2 UTHealth challenge that had longitudinal data [29], and the goal of the competition was to perform de-identification on clinical narratives, with a second track focused on determining risk factors for heart disease over time. [PERSON] et al. [30] provides a comprehensive review of a workshop that includes how data were released and how the submissions were evaluated. The 2016-CEGS N-GRID shared tasks that the workshop used in gathering psychiatric data [31] for addressing text de-identification, symptom severity detection, and the proposal of new research questions. [PERSON] et al. [31] explained how the data were generated; discussed the challenges with psychiatric data as it contains higher occurrence of PHI; and the outcomes, which showcase the best performing systems and how the submitted models were evaluated. There was also another competition on clinical NER for de-identification on Japanese text (2012 NTCIR-10) [32]. [PERSON] et al. [33] organized a competition, which was also a final deliverable for the Applied NLP course taught at UC Berkeley. The objective of the competition was to develop an algorithm that predicts/assigns an ICD-9 (International Classification of Diseases, 9 th revision) code to clinical free text [33]. MADE1.0 [34] is a competition for detecting Adverse Drug Events (ADEs) from EHR. The goal of the NLP task is to detect medication names and other attributes such as frequency and duration. Around 11 teams participated in at least one of the three tasks. There was a total of 41 submissions, among which [PERSON] et al. [35] ranked first for the NER task, with a micro-averaged F1-score of 0.892. SemEval-2014 [36] Task 7 was another competition on analyzing clinical text; it had two subtasks, namely, identification and normalization of disease and disorders in a clinical text from the ShARe [37] corpus. Around 21 teams participated in the identification task, and the best F1-score reported was 81.3, while for the normalization task, 18 teams participated, reporting a best accuracy of \(\sim\)74.1. National NLP Clinical Challenges, also called n2c2, is a very popular competition for different clinical NLP tasks. Between 2004 and 2014, the competition was called Informatics for Integrating Biology and the Bedside (i2b2) but was then changed to n2c2 in 2018. They introduced the following clinical RE tasks over the years, with datasets generated by the NIH-funded National Centers for Biomedical Computing (NCBC). * 2010 i2b2/VA Challenge on Concepts, Assertions, and Relations in Clinical Text [38]: In this competition, 16 teams participated in the relationship extraction task that showed that rule-based methods can be augmented with machine learning-based methods. SVM-based supervised learning algorithm performed the best with an F1-score of 0.737 [39]. * 2011 Evaluating the state-of-the-art in co-reference resolution for electronic medical records [40]: In this competition, 20 teams participated and rule-based and machine learning-based approaches performed best, with an augmentation of the external knowledge sources (coreference clues) from the document structure. The best results on the co-reference resolution on the ODIE corpus with the ground truth concept mentions and the ODIE clinical records were provided by [PERSON] et al. [41], with an F1-score of 0.827. The best results on both the i2b2 and the i2b2/UPMC data were provided by [PERSON] et al. [42], with F1-scores of 0.915 and 0.913, respectively.