Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

NLP or Natural Language Processing is a technology under Artificial Intelligence that helps machines understand human language. With the help of NLP, engineers blend the field of computer science and linguistics. They understand the structure and components of the languages and use machine learning and NLP algorithms to enhance a model’s capabilities to understand, analyze, and decode the meaning of any text and speech.

Natural Language Processing is not a single technique but involves numerous different techniques. However, the main goal of these technologies is the same. They reduce the communication gap between humans and computers. A major example of NLP that we see around us is Apple’s Siri. Siri can navigate your iPhone by understanding the speech, translating it, and following commands.

How NLP Works?

There are numerous languages on our planet, which increases diversity, disorganization, ambiguousness, and complexity. All these languages contain separate semantic and syntactic rules. Natural language helps computers understand languages that they can interpret. You need to provide relevant data for training that they can process and understand. Through machine learning algorithms, these machines use training data as input and generate new outcomes. Machine learning algorithms help machines develop new knowledge bank through statistical analysis techniques.

Creating machines’ ability to process the data and transform it into language that machines can understand is a complex technique. Natural Language Processing helps the machine understand the grammatical structure of the text through semantic and syntax analysis. Moreover, this technology enables the machine to identify the words and create a relation to make sense of the context. You can organize and clean your data through various techniques. For instance, stop word removal, lemmatization and stemming, part of speech tagging, and tokenization are some techniques that you can use. After the dataset gets ready, you need to build an algorithm so your computer can interpret the natural language and execute different complicated tasks. There are two algorithms that you can use machine learning algorithms and a rule-based approach.

Advantages of NLP

The machine learning algorithm is growing increasingly popular due to its learning ability. These algorithms use training data and create new unknown data on their own. You only need to input the basic data for training so the model can analyze and recognize the pattern of the data.

With a deep machine learning algorithm, you can perform multiple NLP tasks such as intent detection, topic classification, keyword extraction, sentiment analysis, and more. These tasks help your model work efficiently and quickly.

NLP and Healthcare

The advancement of natural language processing is drastically increasing in the healthcare industry because of its ability to find, analyze, and interpret patients from the database. You can use machine learning, medical algorithm, and NLP technology to generate insights from previous data. You can accurately organize unstructured data in healthcare, improve the methods, understand the quality, and find accurate results for the patients’ treatment.

Traditionally, physicians spend a lot of time understanding how their patients feel and note all the details in the chart notes. Every time patients arrive, they find the chart notes and analyze the patients again. However, NLP, EHRs, or electronic health record systems, record all the data that doctors insert on the computer. All the data that EHRs stores convert into free-text form.

Benefits of NLP

  1. Improving Data Usability of EHR

EHR interface makes tasks that involve finding relevant and critical patient information difficult. NLP will help EHR find patient information in an easy and simple method. NLP will arrange the interface in various sections and associate those sections or words with different patients. When clinicians search the data with those words, they will find the patient’s related information. This will help them generate data that they might not be able to find. This NLP approach will help improve treatment.

  1. Enabling Predictive Analysis

Natural learning processing can also enable you with predictive analytics to improve the population’s health concerns. For instance, the United States is experiencing an increasing rate of suicide. Experts suggest that they analyze their population’s response to find how people respond to social media before they commit suicide. This will help them to early intervention in the future.

  1. Boosting Phenotyping Capabilities

Phenotype is an expression of the traits that an organism has. These traits include biochemical processes, appearance, or behavior. Phenotyping comprises categorizing and grouping the patients and providing deep and focused information and data about the traits they contain. Clinicians can easily extract the structure of the data and use it for analysis with the help of phenotyping. Experts can analyze and extract the unstructured data with these NLP tools. You can gather additional data by creating phenotypes of the patients.

By applying the NLP approach in our medical industries, we can generate pathology reports with extensive patient data such as their condition, cancer stage, growth location, genetic status, previous treatments, and medications. We were unable to find such details with traditional methods for pathology reports. NLP plays a vital role in the extraction of data to find solutions for complex problems.

  1. Improving Health System Quality

Hospitals can easily report the federal government and other associated agencies so they can improve the quality of our country’s health system. NLP can accelerate the health sector’s improvement as the officials can generate large samples of patients and use them for real-time analysis.


NLP is emerging roots in the healthcare sector from providing high-end solutions for medical applications or performing other tasks such as billing or reimbursement of the patients’ payments. Natural language processing has wide implications for the development of the healthcare industry. However, data scientists are introducing new and innovative technologies and algorithms with specific and accurate capabilities to set new industry grounds. NLP should provide easy to understand data presentation and data extraction that patients can understand.


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