## 5 Discussion on Clinical Named Entity Recognition
### Clinical NER Models  
* [PERSON] et al. [46] proposed a dictionary look up algorithm, where each named entity is mapped to a terminology. The dictionary was constructed using the terms from UMLS, SNOMED CT, and RxNORM. This implementation also involves a parser in which the output is used further to search for noun phrases. The limitation of this implementation is that it fails to resolve ambiguities while working with results from multiple terms in the same text. They datasets for NER are derived from Mayo clinic EMR. For exact and overlapping matches F1-score reported were 0.715 and 0.824 respectively. * [PERSON] et al. [56] used CRF model and a rule-based approach to detect NER on Swedish health records and identified four entities: Drug, Finding, Disorder, and Body structure. They also compared it on English clinical text. They reported precision and recall for all of their findings: 0.88 and 0.82 for body structure, 0.80 and 0.82 for disorders, 0.72 and 0.65 for findings, and 0.95 and 0.83 for pharmaceutical drugs. * [PERSON] et al. [57] developed a rule-based NER system that was designed to detect patients for clinical trial. They used the n2c2-1 challenge dataset for training and achieved an F1-score of 0.90. * [PERSON] et al. [48] developed a rule-based approach to detect extraction of food, nutrient, and dietary recommendations from text. They discussed four methods FoodIE, NCBO, NCBO (OntoFood), and NCBO(FoodON). Based on their comparison, they identified that FoodIE performs well. Their model was trained on the FoodBase Corpus and was able to identify entities from dietary recommendation. * [PERSON] et al. [58] developed a joint model based on which CRF performs word segmentation and NER. Generally, both systems are developed independently, but the joint model used to detect Chinese discharge summaries performed well. There was no score reported in this publication; they only reported that the joint model performance is better when they compared it with the two individual tasks. * [PERSON] et al. [59] developed an NLP pipeline, which processed clinical notes and performed NER using bi-directional LSTM coupled with CRF in the output layer. They used 1092 notes from 21 cancer patients, from which 800 notes were used for NER training. They reported NER precision, recall, F1-score for the entities individually and reported a macro-averaged F1-score of 0.81. * [PERSON] et al. [60] proposed a novel ensemble approach using the strength of one approach to overcome the weakness of other approaches. In their proposed two-stage approach, the first step is to identify base classifiers using SVM, while in the second phase, they combined the outputs of base classifiers based on voting. They used the i2b2 dataset and reported an F1-score of 0.77. * [PERSON] et al. [4] performed a comparison study between two well-known deep learning architectures, CNN and RNN, with three other implementations: CRFs and two state-of-the-art NER systems from the i2b2 2010 competition to extract components from clinical text. The comparison created a new state-of-the art performance for the RNN model and achieved an F1-score of 85.94%. * [PERSON] et al. [61] proposed a model to study symptoms from Chinese clinical text. They performed an extensive set of experiments and compared CRF with HMM and MEMM for detecting symptoms. They also used label sequencing and the CRF approach outperformed the other methods. * [PERSON] et al. [17] provided a comprehensive survey of deep neural architectures for NER and compared it with other approaches including supervised and semi-supervised learning algorithms. Their experiments showed good performance when they include neural networks, and they claim that integrating neural networks with earlier work on NER can help obtain better results. [PERSON] et al. [51] used Bidirectional Encoder Representations from Transformers (BERT) [62] and Spanish BERT (BETO) [63] for transfer learning. This model is used to extract tumor information from clinical reports written in Spanish. They reported an F1-score of 73.4%.