Primary Health Care: Open Access 2021, Vol.11, Issue 3, 373. 1 Research Article NLP/Deep Learning Techniques in Healthcare for Decision Making Kamal Jain 1 *, Vishal Prajapati 2 1 Data Science Evangelist, Mentor, United Arab Emirates 2 Department of Information Technology, G H Patel College of Engineering & Technology, Anand, Gujarat, India Corresponding Author* Kamal Jain Data Science Evangelist, Mentor, United Arab Emirates E-mail: kamaljain777@gmail.com Copyright: 2021 Jain K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received 09 March, 2021; Accepted 23 March, 2021; Published 30 March, 2021 Abstract Day by day the health care domain is generating millions of records of patients in a structured and unstructured way. By applying deep learning techniques, it can be converted into a well-structured form i.e. Electronic Health Record (EHR). Decision making is one of the key parts in the Healthcare domain. In the decision making process doctors must refer to many data like laboratory reports, diagnosis reports, medical images, demographic information about the patient, clinical notes, and map it collectively with the concepts of medical science. Here AI especially Natural Language Processing and deep learning can be helpful in many ways. The objective of this article is to depict the importance of Deep Learning and Natural Language processing EHRs. Based on EHR, the doctor can take quick decisions in case of an emergency. Apart from that, it can also be effective in clinical predictions, detect disease at an earlier stage, forecasting future need of regular check-ups, predictions of hospitalization soon if required. The provision of an enormous amount of clinical information particularly EHR has stimulated the expansion of deep learning techniques that assist within the rapid analysis of patient data. Keywords: Natural language processing, NLP, Deep learning, Data science, Artifcial intelligence, EHR, Healthcare, Electronic health records Introduction The ubiquitous adoption of electronic health records in hospitals and other healthcare facilities generates vast real-world information, which is very valuable for conducting clinical research. Over the past many years, electronic health records (EHR) systems are been widely adopted across clinics and hospitals. Analysis of this huge data is the foundation to provide improved healthcare to the patients However, manual review of this vast amount of data generated from multiple sources is costly and very time-consuming. It brings huge challenges while attempting to review this data meaningfully. Hence, the role of artifcial intelligence (AI) techniques is becoming important in enhancing clinical research and care. As a large number of EHR are locked in clinical narratives, Natural Language Processing (NLP) and Deep Learning (DL) techniques have been leveraged to extract information. While computer vision techniques can be used for medical imaging, NLP can be used for analysing unstructured information in the EHR, reinforcement learning techniques can be used in the context of robotics-assisted operations. NLP algorithms can be used to identify clinically relevant phenotypes while analysing text and determining the grammatical relationships between phrases. Rule- based NLP techniques can be used to get high sensitivity (identification of a large proportion of true cases) and high positive predictive value in clinical records. Healthcare is one of the domains where computer science is becoming very supportive of varied tasks. AI is increasingly being adopted across the healthcare industry ranging from basic level practices to specialization, and lots of the foremost exciting AI applications leverage language processing (NLP). The capabilities of these AI techniques potentially enable the identifcation of distinctive clinical characteristics among patients which further helps in clinical care and minimizing methodological heterogeneity in medical research concerning various health diseases. What is Natural Language Processing? Natural language processing (NLP) focuses on analysing text and speech to infer meaning from words. Recurrent neural networks (RNNs) - deep learning algorithms play a key role in processing sequential inputs like language, speech, and time-series data [1]. Deep learning is a subset of machine learning having the capacity of learning unsupervised data from unstructured or unlabeled data. On the other side deep learning will be able to learn optimal features from available data without human intervention. Natural language processing is used to describe the process of using computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written input. NLP requires skills in artifcial intelligence, computational linguistics, and other machine learning disciplines [2]. There are two approaches to NLP. 1. A rule-based approach where computer follows pre-defned rules in the program 2. Machine Learning-based approach where we can have supervised and unsupervised learning methods. In supervised learning, the computer learns latent rules through human guidance called annotation while in unsupervised learning there is no human interaction. NLP algorithms, frst extract information or concepts from EHRs, then process extracted information, and fnally classify patients into a subgroup as per rules and learners. Procedures for NLP are complex because it consists of multiple techniques together. NLP will map phrases or words to concepts of interest, and it needs careful pre-processing of text and it will be converted to document from the natural form [3]. A few examples of low-level NLP tasks (text pre-processing) include the following: 1. Sentence boundary detection, which is usually defned by a period 2. Tokenization (breaking a sentence into individual tokens) 3. Stemming (reducing word into a root form) 4. Lemmatization (process mapping a token) A few examples of higher-level NLP tasks include the following: 1. Named entity recognition 2. Setting up negation rules The Figure 1 depicts the text pre-processing and classifcation of an asthma patient. Role of Deep Learning/NLP in Healthcare Basically, in terms of users, we can categorize the healthcare information coming from below four sources - 1. Doctors 2. Patient 3. Paramedical staff 4. Pharmaceuticals Every post-process depends on the diagnosis of a disease. If the disease is identifed properly then one can get proper treatment. Sometimes the situation of patients gets serious due to delays in decision making (Table 1). Figure 2 represents, deep learning is an approach where there is a large scale network that is going to accept a variety of input data types like text, image, audio, time-series data, etc. for each data type learns a useful features in its lower level towers. The data from each pillar is then merged and flows through higher levels, allowing the Deep Neural Network to reach to conclusion based on reasoning and evidence across data types. Natural language processing can help healthcare in information extraction, unstructured data to structured data conversion, Document categorization,