## 1 Introduction  
The electronic health record (EHR) is a collection of patients' health information in a digital format. Text-based medical records are an important resource for the EHR and an enriched knowledge source for medical research. One of the major limitations of the large-scale use of the EHR is the privacy of information in medical corpora. The Health Insurance Portability and Accountability Act (HIPAA) in the United States defines 18 types of protected health information (PHI) that need to be removed from medical records before circulating these for secondary usage. The PHI items encompasses name, phone number, geographic location, medical record number, social security number, etc. [1, 2]. Itis impractical to de-identify large-scale data manually, since this can be expensive, time-consuming, and prone to error. Therefore, a reliable automated de-identification system is highly desired. Healthcare and biomedical research has been significantly impacted by the utilization of natural language processing (NLP). Named entity recognition (NER) is a basic functionality of clinical NLP. It is defined as the identification of desired entities from texts. The main task of NER is to identify and classify specific words and meaningful phrases [3]. NER from clinical texts has been an area of increasing interest in recent years in the medical domain and drive clinical decision support (CDS) to enable healthcare providers to make personalized patient care decisions. Medical reports consist of both coded and unstructured texts. Although coded data can easily be de-identified, it is extremely challenging to de-identify unstructured texts. The NER techniques can be divided into four categories: lexicon-based, heuristic-based, machine learning, and hybrid techniques [4; 5; 6]. The majority of the primitive NER systems applied lexicon- and heuristic-based techniques. These systems deployed rules derived from syntactic-lexical patterns as well as information lists to classify and identify named entities (NE) [7; 8; 9; 10]. Since these approaches exploit language-related knowledge, these are considered to be highly efficient [11]. However, there are a few limitations of these techniques, as they are domain-specific, expensive, and involve human expertise in that domain. Due to these limitations, researchers have shifted their interests towards machine-learning-based techniques. There have been numerous efforts to improve the performance of clinical NER systems by undertaking various strategies to exploit the existing infrastructure of machine learning algorithms. Machine learning techniques can be either unsupervised, semi-supervised, or supervised. Some studies used an ensemble of multiple machine learning methods [12; 13]. Hybrid machine learning models with high confidence rules have also been applied [14]. Multiple studies have used unsupervised models using clustering algorithms [15; 16]. The existing literature shows that supervised machine learning algorithms have been incorporated in most of the best-performing NER systems. Multiple algorithms are used to build supervised NER models, such as conditional random fields (CRF), maximum entropy (ME), and structured support vector machines (SVMs) [17; 18; 19]. These algorithms build NER models by exploiting the predefined multidimensional feature sets from text datasets. The performance of a supervised model depends largely on the selection of proper features. In this study, we investigated the performance of a CRF-based NER model by using multiple features and their combinations. The objective of this study was to identify the best-performing features for a de-identification NER model. We did not aim to build a high-performance model. In fact, our aim was to find the features which work the best to build such a model. The major contribution of this study is that it helps to identify the best possible features from the wide range of features mentioned in the existing literature.