## 1 Introduction
### Related Work  
Applications of Deep Learning methods to the EHR are already numerous, both using structured data such as medication prescriptions, diagnosis and billing codes, and lab measurements, as well as using unstructured medical images. They are used for instance to perform information extraction, representation learning, prediction, phenotyping, and de-identification [24]. [PERSON] et al. [25] applied a deep Convolutional Neural Network to detect diabetic retinopathy in images of the retina, showing that the judgment of licensed ophthalmologists can be matched, while [PERSON] et al. [26] used the same type of neural network to classify types of skin cancer with performance that matches that of board-certified dermatologists. Another approach by [PERSON] et al. [27] using Recurrent Neural Networks managed to diagnose the most common conditions in Intensive Care Unit patients better than several baselines using irregularly performed measurements. [PERSON] et al. [28] applied Restricted Boltzmann Machines to data from wearable devices, outperforming other methods of monitoring the state of patients with Parkinson Disease. In addition, other different approaches applied to various types of structured clinical measurements exist (e.g., [29; 30; 31; 32]).  
Violence from patients directed at staff or other patients is seen in almost any psychiatric treatment facility. [PERSON] [33] reports the prevalence of a violence incident happening during admission in 35 different facilities, which lies between 2% and 44%, averaging 17% over these different sites. Individual patient factors that are associated with violence risk, such as a history of substance abuse and a history of violent behavior are well described (e.g., [34; 35; 36; 37; 38]). Meta analyses, however, reveal that only a small amount of these factors is found to be robust when comparing the results of various studies on different populations [39; 40]. The task of predicting the occurrence of violence incidents has been shown to be even more challenging, especially when no structured instruments are used [41]. Psychiatrists in training for example do not perform much better than random at this task with an Area Under Curve (AUC) of the Receiver Operator Curve of 0.52, while using a structured instrument improves the performance (AUC = 0.67) to the level of a trained psychiatist(AUC = 0.71) [42]. A substantial proportion of health care professionals therefore makes use of risk assessment instruments [43], of which the Violence Risk Appraisal Guide [44], Structured Assessment of Violence Risk in Youth [45], and Historical Clinical Risk Management-20 [46] are most commonly used [47]. A meta study by [PERSON] et al. [48] reports that the median predictive performance of these three instruments falls in a relatively narrow range between 0.70 and 0.74 AUC, showing that prediction of violence incidents is possible with moderately good results. There is however also a large variation in performance of these instruments over different sites [49]. It seems that the heterogeneity of psychiatric patient populations inhibits straightforward generalization of measuring instruments' predictive performance to other treatment facilities. This has caused serious discussion, and sometimes even skepticism on the usability of these instruments in practice [50; 51]. Given that these instruments are furthermore considered to be time-consuming and thus expensive [52], predicting violence incidents from clinical text that is already registered could be considered an important contribution to the field of personalized medicine [53]. In this work we therefore apply several machine learning techniques to this problem, in order to determine if prediction of violence incidents from EHR data is possible, and if so what Deep Learning or classical techniques should be applied.