In this post a discussion is provided on natural language processing, its definition, pipeline and application in the healthcare industry.
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. It encompasses the development of algorithms and models that enable machines to understand, interpret, and generate human-like language. NLP involves a wide range of tasks, including speech recognition, language translation, sentiment analysis, and text summarization. The primary objective of NLP is to equip machines with the ability to comprehend and respond to natural language input in a manner that reflects a deep understanding of semantics, syntax, and context. By leveraging linguistic and computational techniques, NLP systems aim to bridge the communication gap between humans and machines, enabling more intuitive and natural interactions in various applications, such as virtual assistants, chatbots, and language translation services. As NLP continues to advance, its impact extends across diverse domains, revolutionizing how we interact with technology and facilitating more seamless communication between humans and computers.
Well human beings are the most advanced species on earth there is no doubt in that and our success as human beings is because of our ability to communicate and share information. That's where the concept of developing a language comes in and when we talk about the human language it is one of the most diverse and complex parts of us. Considering a total of six thousand and five hundred languages that exist.The reality of having machines talk and respond to us in a human-like manner is already a reality which keeps getting more and more realistic with every passing day the people you ask for queries on websites, your smart assistants even make calls over the internet. all of them have one thing in common, none of them are actually human now
How do they manage to sound and seem so human-like?
How do they respond to me so intelligently?
How are they so articulate?
This, my friends, is the magic of natural language processing. Natural language processing refers to the branch of artificial intelligence that gives the machines the ability to read, understand and derive meaning from human languages. nlp combines the field of linguistics and computer science to decipher writing structure and guideline, to make models which can comprehend, break down and separate significant details from text and speech. everyday humans interact with each other through public social media transferring vast quantities of freely available data to each other. This data is extremely useful in understanding human behavior and customer habits. Data analysts and machine learning experts utilize this data to give machines the ability to mimic human linguistic behavior. This helps save millions in terms of manpower and time as you don't need to always have a person present at the other end of a phone.
Daily Natural Language Processing use cases
NLP is also a lot more widespread than one may realise. We use it every day in seemingly normal and insignificant situations. don't know how to correctly spell a word? auto correct has you covered, need to see if your article or thesis will get flagged for copyright violations that's ok a plagiarism checker will search through the web and find any cases of published documents which may match your work line by line.
Natural Language Processing Pipeline
Blogging gives your site a voice, so let your business’ personality shine through. Choose a great image to feature in your post or add a video for extra engagement. Are you ready to get started? Simply create a new post now.while n l p seems really cool yet a cutting edge and complicated technology concept it is actually pretty easy to learn. You start off with a document or an article to make your algorithm understand what is going on in it. You need to process it into a form which is easily comprehensible by the machine. This is no different than making a child learn to read for the first time. you start off by performing segmentation which is to break the entire document down into its constituent sentences you can do this by segmenting the article along its punctuations like full stops and commas for the algorithm to understand these sentences we get the words in a sentence and to explain them individually to our algorithm so we break down our sentence into its constituent words and store them. This is called tokenizing where each word is called a token. We can make the learning process faster by getting rid of non-essential words which do not add much meaning to our statement and are just there to make our statement sound. Now that we have the basic form of our document we need to explain it to our machine. We first start off by explaining that some words like skipping, skips, skipped are the same word with added prefixes and suffixes; this is called stemming. We also identify the base words for different words; tens, mood, gender etc. this is called lemmatization. stemming from the base word lemma now we explain the concept of nouns, verbs, articles and other parts of speech to the machine by adding these tags to our words. This is called part of speechtagging. Next we introduce our machine to pop culture references and everyday names by flagging names of movies, important personalities or locations etc. that may occur in the document. This is named entity tagging. Once we have our base words in tags we use a machine learning algorithm like naive bayes to teach our model human sentiments and speech. At the end of the day most of the techniques used in nlp are simple grammar techniques that we have been taught in school.
Natural Language Processing in Healthcare
Thanks to machine learning we can extract knowledge from medical records, call centre conversations, medical voice sound bites, medical forms, regulatory filings, research reports, insurance claims, pharmaceutical documentation and more. This ultimately helps doctors and care teams get holistic use of their patients quickly. health plans help to see population trends for their members. This is possible only due to a field known as natural language processing. In Pharma, NLP helps to draw insights from drug development research. NLP is a field in AI which is concerned with programming computers to process and analyse large bodies of human communication that can live in many different formats such as written texts, spoken utterances or even official documentation. organisations can use one of google's natural language services to specifically help, process, structured and unstructured healthcare language data using nlp. The healthcare natural language contains four key features that help you find, assess and link knowledge in your data. It has the ability to map text to medical concepts which is referred to as knowledge extraction it also identifies and connects related medical attributes which is known as relation extraction. NLP can assess surrounding factors that could be clinically relevant known as context assessment and standardises medical concepts so they can be analysed across systems known as knowledge linking. Another way of thinking about nlp is that it can extract critical clinical information like medications, medical conditions as well as understand context like negation such as this patient does not have diabetes.It also understands temporality such as this patient needs to start chemotherapy tomorrow and even infer there are relationships between things such as side effects or medication dosage. pharmaceutical researchers are also enabled via a standard patient discovery interface for population health. Using NLP, clinical trial documentation can be surfaced to match patients or find novel treatments. This can both increase their number of participants as well as process the high volume of feedback.
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