## 2 Related Works
The researchers compared the performance of the NLP algorithm with ICD codes in detecting partner violence by analyzing 1,064,735 emergency department admissions in a level 1 trauma center in the USA from 2012 to 2020. The developed NLP model detected partner violence cases totaling 7399 with an accuracy rate of 99.5%, using 23 basic terms and 49 extended terms to detect situations related to partner violence. The current study showed that this NLP algorithm outperformed the ICD codes in the better detection of partner violence and drastically reduced false positive cases. The authors asserted that the method of the current NLP approach could be integrated into health services' early detection and intervention processes concerning partner violence. Besides, it was underlined that the real-time use of this algorithm may reinforce mechanisms of intervention by the early detection of events related to partner violence. The results of this study have shown how NLP techniques can be an effective tool in the detection and solution of social problems in health services [18]. [PERSON] et al. (2024) developed deep learning models that are able to predict violence and threats against healthcare workers based on clinical notes. The main purpose of the study is to develop artificial intelligence-based tools that are capable of providing early intervention against increased violence in healthcare. The researchers aimed at predicting violent incidents using two models: first, applying a document classification model based on clinical notes; and, secondly, a regression model using structured data. These achieved successful results for the document classification model, with a score of 0.75 F1, and for the structured data model, with a score of 0.72 F1. Both models were proven capable of performing beyond human expertise to surpass the 0.5 F1 score that had been recorded by the team studying psychiatry for the same set of documents. The study further developed an NER model to identify the risk factors leading to violence in clinical notes. The model recorded a general F1 score of 0.7 in identifying violence risk factors. The research suggests that these models can be used specifically to prevent violence in healthcare and generalize to other hospital systems. The results suggest that artificial intelligence and deep learning techniques can play an important role in the early detection of violence against healthcare workers [19]. A fresh, new approach was worked on and proposed by [PERSON] regarding the ways and means of finding or the prevention of workplace violence using modern day AI technologies in sync and in cooperation with the traditional methodology as early as 2024. The purpose is the verification against how well AI-enabled detective devices support time-effective and accurate detection in instances related to violence in healthcare professional practice. The researchers applied NLP to analyze about 71,000 nurse handover notes for pediatric patients in a large New England city. It doubled the number of violent incidents that could not be detected using traditional methods--a testament to the importance of AI-based surveillance systems in healthcare. The study has focused on the fact that the machine learning feature of AI can detect incidents of violence in a more detailed and nuanced way. However, the system has certain limitations too; for example, the exact type of the incidents was not possible to determine. Researchers have suggested that violence is often underreported, and AI-based systems may make significant contributions to correcting this situation. The present study has pointed out the potential of AI technologies in the improvement of safety in healthcare and laid a foundation for further advanced solution development in this area in the future [20]. Figure 2 shows the quality-method scheme of studies in the literature.