## 5 Discussion on Clinical Named Entity Recognition
### Clinical NER Methods  
One of the important challenges in designing a clinical NER is how to extract meaningful information without much human effort. Prior to NER, the NLP techniques used required a lot of human effort to process the text. There are various NER models proposed over the last decade to extract information from the clinical text that can be broadly classified into four types of approaches:  
* Dictionary-Based Approach: In this approach, a predefined set of named entities are defined that are later used as a lookup while parsing the clinical text for entities. For example, [PERSON] et al. [46] used a dictionary-based approach to detect NERs from clinical text using their NLP toolkit.
* Rule-Based Approach: Here, the rules/entities are predefined by domain experts. Most of the rules are handcrafted and are used to detect entities in a specific text. The limitation of this approach is generalizability or extensibility, as most of them are applicable to the domain they were defined in. This approach certainly requires a lot of effort where experts spend time defining the entities, and then, it is used as a lookup while parsing the clinical notes.
* Machine Learning-Based Approach: The purpose of this approach is to completely automate the NER process. Commonly used ML algorithms such as Random Forest (RE), Support Vector Machines (SVMs), and Neural Networks (NN) are used to learn the pattern (entities and boundaries) using the training set. Once the training is over, the model can classify the clinical text into predefined classes. This approach is garnering much attention due to recent advancements in ML and the easy availability of computational resources. The majority of the articles collected for this survey used this approach.
* Conditional Random Field (CRF)-Based Approach: The CRF approaches fall under the ML category and mostly solve a label sequencing problem, where for a given input sequence \(X=x_{1}x_{2}x_{3}\), CRF tries to find the best label sequence \(Y^{*}=y_{1}y_{2}y_{3}\). At first, the entities are annotated with tags; in general, the BIO (Beginning, Inside, and Outside of Entity) schema is used for annotation, where each word is assigned to a label. The input for CRF models is mostly designed by humans and represented as a bag-of-words style vector. [PERSON] et al. [4] introduced seven tags and three CRF baselines using different features. All of the commonly used CRF-based implementations in clinical NER can be found in the CRF++ package. In Tables A1 and A2, we observe that there are many models using CRFs for NER with good accuracy.
* Deep Learning-Based Methods: This is similar to the CRF label sequencing problem using the BIO schema, where the input is a raw sequence of words. An added layer performs the word embedding by converting words into densely valued vectors. In the training phase, it learns the dependencies and features to determine entities. Deep learning methods are very popular for clinical NER as they achieve state-of-the-art results and can also detect hidden features automatically. The first neural network architecture for NER was proposed by [PERSON] et al. [19], with a convolution layer, several standard layers, and a non-linear layer. This architecture achieved state-of-the-art performance in clinical NER. Details on the CNN model for clinical NER can be found in [17]. New studies have recently shown that RNNs (Recurrent Neural Networks) perform much better than CNNs and are capable of capturing long-term dependencies for sequence data. [PERSON] et al. [55] introduced Long Short-Term Memory (LSTM), a popular implementation of RNN architecture, for this problem. [PERSON] et al. [4] evaluated the performance of CNNs, RNNs, and CRFs with different features and concluded that the RNN implementation outperformed the other two.  
* Hybrid Approaches: here, any of the above approaches are combined and then used to determine entities.