## 6 Discussion on Clinical Relationship Extraction
### Supervised Learning Methods
#### 6.3.1 Traditional Machine Learning and Deep Learning-Based Methods  
Supervised learning is defined as an ML task to learn a function that maps the input to the output of each input-output data point [88]. This requires the annotated data to be divided into training and testing samples. The model learns the function based on the values of the inputs and the outputs of the training examples. Analyzing the inputs and the outputs, the model comes up with an inferred function. Then, the efficiency of the inferred function is analyzed by testing the function on the testing set. Supervised ML algorithms can be classified into two categories: (i) traditional supervised learning algorithms and (ii) Artificial Neural Network (ANN) based algorithms. The traditional methods are heavily dependant on the well-defined features, and hence, their performance relies on the efficacy of the feature extraction process. Moreover, these shallow algorithms are found to be overshadowed by ANNs where data is large and of high dimension. Still, the shallow traditional ML algorithms perform better in the case of low-dimensional data or data with a limited number of training samples. ANNs can be very deep, depending on the number of hidden layers between the input and the output, leading to deep learning-based methods. The differences between the traditional shallow methods and ANNs are surveyed by [PERSON] et al. (2021) [89]. Examples of traditional algorithms include but are not limited to Support Vector Machines (SVM), Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Decision Trees, K-Nearest Neighbor (KNN), Node2 vec, etc., whereas Dense Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), autoencoders, etc. are some of the Deep Learning algorithms. These algorithms have been extensively used in clinical domain for a variety of tasks [90; 91; 92; 93; 94]. [PERSON] et al. (2011) [78] first used an SVM-based approach on adapted features to extract relations between entities spread across different sentences. Their work showed that the structured features used for intra-sentential RE can be adopted for inter-sentential RE as they both performed comparably. Later on, inter-sentential RE tasks were defined on clinical notes too. In the 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text [38], SVM-based supervised learning algorithm performed the best with an F1-score of 0.737 [39]. The domain-invariant CNN on multiple features for clinical RE used by [PERSON] et al. (2016) [76] showed a decent performance with various filter length combinations; filter combination of [4; 6] performed the best (precision: 0.7634, recall: 0.6735, and F1-score: 0.7116). [PERSON] et al. (2016) [74] used a C4.5 decision tree because of its superior performance on the features extracted from various biomedical literature for disease-mutation RE. It demonstrated improved performance when compared with the previous state-of-the-art models with F1-scores of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. The performance of a sparse deep autoencoder-based model introduced by [PERSON] et al. (2016) [95] outperformed the performance of a deep autoencoder on most of the clinical relation types. [PERSON] et al. (2017) [80] presented a multi-label structured SVM for Disorder Recognition in the 2013 Conference and Labs of the Evaluation Forum (CLEF) textual dataset. This model achieved an F1-score of 0.7343, i.e., 0.1428 higher than their baseline BIOHD1234 scheme. [PERSON] et al. (2017) [73] compared the performance of a rule-based approach with a feature-oriented SVM-based supervised learning approach for clinical RE, where the supervised learning model reported higher F1-scores. [PERSON] et al. (2018) [59] used a bidirectional LSTM-CRF for the clinical NER and a random forest-based binary classifier for the clinical RE.