## 8 Conclusions  
In this paper, we present the first review of the various interrelated NER and RE methods in the context of clinical text. Our literature survey highlights the increasing popularity of various traditional machine learning-based approaches and deep learning models over the past few years, which has somewhat led to a sharp decline in the usage of rule-based methods for both NER and RE or dictionary-based methods for NER only. Hence, hybrid approaches by combining machine learning-based and rule/dictionary-based approaches have the potential to be one of the dominant approaches for these tasks in the future. On top of that, various other machine learning approaches, deep learning approaches, and language model-based approaches for clinical NER and RE will most probably continue to come up in good numbers in the next few years. GANs, which can automatically discover patterns in the data, can potentially also be a good architecture for clinical NER and RE. In the case of both NER and RE, the F1-score is the most frequently used evaluation metric. For unsupervised clinical RE, some work used different similarity measures such as Manhattan, Binary, and Cosine similarities to measure the goodness of the various unsupervised clustering approaches. A few clinical NER papers have mentioned the usage of \(t\)-tests on the models to find out their statistical significance. Other popular metrics used in ML-like sensitivity, specificity, ROC, and AUC can also be used in the future to evaluate the performance of the different approaches used for both NER and RE. We also believe that the community will move towards a standardization of clinical terms and that the future models developed will aim to use these terms. Standardization will help us integrate data from multiple sources and will also help in NER reconciliation. Clinical NER tasks vary based on different domains; we observed that none of them use transfer learning approaches to train their models from different domains. Developing deep learning models using transfer learning from different clinical NER tasks can be a promising future research direction. In the case of clinical RE, relationships are mostly extracted between entities present in a sentence and the types of relationships are mostly multiclass but not multilabel in most cases. Therefore, from the computational angle, it may be worthwhile to carry out more research on RE across sentences besides also multilabel RE but these tasks require data preparation and annotation in some specific formats. The contributions of the authors are listed as follows: conceptualization, P.B., S.S. and P.G. ; methodology, P.B. and S.S.; investigation, P.B. and S.S.; resources, W.C.S.IV, R.K., J.P. and P.G. ; writing--original draft preparation, P.B. and S.S.; writing--review and editing, P.G., W.C.S.IV and R.K.; supervision, R.K., J.P. and P.G. ; project administration, R.K., J.P. and P.G. ; funding acquisition, R.K. and J.P. All authors have read and agreed to the published version of the manuscript. This research has been funded by the US Veterans Health Administration-National Radiation Oncology Program (VHA-NROP). The results, discussions, and conclusions reported in this paper are completely those of the authors and are independent from the funding sources. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflicts of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: