## 4 NLP Competitions and Datasets for Clinical Text
### Datasets  
The datasets are important in understanding the different entities and relations extracted in the clinical domain. This subsection gives an overview of the different datasets used for clinical NER and RE tasks for a better understanding of the challenges in the domain. We came across a few publicly available datasets for clinical NER; however, these datasets are restricted to specific NLP tasks in clinical domain. Below is a list of datasets that were used in NER challenges or used as training for NER models, which are discussed in Section 5.3 for training, testing, and validation:  
* Mayo Clinic EMR: It has around 273 clinical notes, which includes 61 consult, 4 educational visits and general medical examinations, and a couple of exam notes. A few models, such as [PERSON] et al. [46], generated a clinical corpus from Mayo Clinic EMR [47]. * MADE1.0 Data set: This dataset consists of 1092 medical notes from 21 randomly selected cancer patients' EHR notes at the University of Massachusetts Memorial Hospital. * FoodBase Corpus: It consists of 1000 recipes annotated with food concepts. The recipes were collected from a popular recipe sharing social network. This is the first annotated corpora with food entities and was used by [PERSON] et al. [48] to compare food-based NER methods and to extract food entities from dietary records for individuals that were written in an unstructured text format. * Swedish and Spanish Clinical Corpora [49]: This dataset consists of annotated corpora clinical texts extracted from EHRs; the Spanish dataset consists of annotated entities for disease and drugs, while the Swedish dataset has entities annotated for body parts, disorder, and findings. This dataset is mostly used for training and validation for NER on Swedish and Spanish clinical text. * i2b2 2010 dataset [38]: This dataset includes discharge data summaries from Partners Healthcare, Beth Israel Deaconess Medical center, and University of Pittsburgh (also contributed progress reports). It consists of 394 training, 477 test, and 877 unannotated reports. All of the information are de-identified and released for challenge. These datasets are used for training and validation in many of the NER models used for clinical text. * MIMIC-III Clinical Database [50]: This is a large and freely available dataset consisting of de-identified clinical data of more than 40,000 patients who stayed at the Beth Israel Deaconess Medical Center between 2001 and 2012. This dataset also consists of free-text notes, besides also providing a demo dataset with information for 100 patients. * Shared Annotated Resources (shARe) Corpus [37]: This dataset consists of a corpus annotated with disease/disorder in clinical text. * CanTeMiST [51]: It comprises 6933 clinical documents that does not contain any PHI. The dataset is annotated for the synonyms of tumor morphology and was used for clinical NER on a Spanish text by [PERSON] et al. [51]. Specific relations annotated in the datasets from the various clinical RE challenges mentioned in Section 4.1 are as follows:  
1. 2010 i2b2/VA Challenge on Concepts, Assertions, and Relations in Clinical Text [38]: A wide variety of relations were identified as follows: * Medical problem-treatment relations: * _TrIP_ indicates that treatment improves medical problems, such as _hypertension_ being controlled by _hydrochlorothiazzide_. _TrWP_ indicates that treatment worsens medical conditions, such as the _tumor_ growing despite the available _chemotherapeutic regimen_. * _TrCP_ indicates that treatment causes medical problems, such as _Bactrium_ possibly being a cause of _abnormalities_. * _TrAP_ indicates that treatment is administered for medical problems, e.g., periodic _Lasix_ treatment preventing _congestive heart failure_. * _TrNAP_ indicates that treatment is not administered because of medical problems e.g., _Relfen_ being contraindicated because of _ulcers_. * _Others_ that do not fit into medical problem-treatment relations.