## 1 Introduction  
A majority of health care providers currently digitally stores data that has been captured for the sake of delivering care in an Electronic Health Record (EHR) [1; 2]. Subsequently, health care providers have started exploring these historical datasets to improve the quality of their care [3; 4]. Applying machine learning techniques to the various data that are gathered can, for instance, offer new insights into the etiology of disease, provide decision support to clinical professionals in the care process, aid in performing diagnosis, and improve the operations of a health care institution [5; 6; 7; 8].  
The structured data in a patient record (e.g., diagnosis, medication, lab measurements) are relatively straightforward to analyze using well-known and well-researched statistical methods, in practice however, a lot of information in EHR is captured in an unstructured free text form that is more difficult to analyze [9; 10]. Despite this difficulty, the merits of utilizing clinical text for research purposes are currently being discovered in many areas of research, such as adverse event detection, phenotyping, and predictive analysis (e.g., [11; 12; 13; 14]). These approaches use well established methods for classification of text like bag-of-words and n-grams for representing text, and Naive Bayes and Support Vector Machine models for classifying text.  
Although good results have been obtained with these approaches, novel Deep Learning techniques, such as Word Embeddings and Recurrent Neural Networks, have emerged recently, challenging the superiority of these classical approaches. Recent advances have subsequently enabled applying Deep Learning techniques to Natural Language Processing (NLP) problems. Most notably, the introduction of the word2 vec [15] and paragraph2 vec [16] algorithms for learning representations of text have improved state-of-the-art results in several NLP tasks [17; 18]. These Deep Learning techniques are currently also being applied to clinical text, for example by [PERSON] et al. [19] who included clinical text among other data types in predicting the effect of clinical interventions for Intensive Care Unit patients. [PERSON] et al. [20] introduced Deep Patient, a dense vector representation of a patient through time that is partially based on clinical text, based on which good predictions of developing several diseases can be made. Other approaches have focused on extracting medical concepts [21], Named Entity Recognition [22], or de-identification of medical text [23]. These approaches show that Deep Learning techniques applied to clinical text can yield state-of-the-art results in several cases. However, whether this generalizes to other clinical datasets, for example in different medical domains or in datasets with different sample sizes remains unclear.  
In this work, we present a new case, comparing Deep Learning and classical machine learning techniques applied to classification of clinical text. We do so by performing an experimental evaluation of several techniques for representation and subsequent classification of text, applied to a novel and previously unused dataset from the Psychiatry Department of the University Medical Center Utrecht in The Netherlands. The task of this classification problem is to predict which patients will show violent behavior during their admission, based on clinical texts that are available at the start of their admission. Assessment of violence risk is a problem that causes a high burden for both patient and hospital staff. It has been described well in psychiatry literature, but has never been approached by applying machine learning techniques to EHR data.