If you thought that you have never faced Natural Language Processing in your life, then you’re greatly mistaken. To “meet” NLP, you have to open Google and say the familiar phrase: “Okay, Google” by clicking on the microphone icon.
Natural language Processing is a pretty widespread technology within many industries. But today, we would like to focus on a more specific NLP use case – Healthcare.
Nowadays, a significant part of electronic health record data companies still store in an unstructured format – the so-called “text bubble”. Of course, as EHR adoption increases and health data abounds, medical organizations have more analytics-driven opportunities to improve healthcare delivery and outcomes. However, such systems are still having problems, using all available data to their fullest potential: scientific articles, clinical recommendations, descriptions of diseases, and complaints. But even if the data in such documents are semi-structured, it’s impossible to use it immediately without post-processing.
It’s problematic to extract valuable knowledge from the “text bubble”. The simplest algorithms can check a document for the basic features such as words or phrases identification. But this is still not enough: the doctor is always curious in details to make the correct diagnosis. So, the NLP technologies should leverage artificial intelligence (AI) to help analytics systems understand and effectively work with unstructured data.
Below, we’re going to discuss the following points:
- What is NLP & How does it work
- Standard NLP techniques we can use in the Healthcare
- Best Use Cases of NLP in Healthcare
- What about Azati?
Keep reading, and you will learn many worthwhile things!
What is Natural Language Processing & How does it work
Natural Language Processing (NLP) is based on the intersection of computer science, artificial intelligence, linguistics, and consists of computational linguistics—rule-based modeling—and statistical, ML, and deep learning models.
This technology is focused on “understanding” the whole meaning of human-generated data, speaker or writer’s intent, sentiment, and its further interpretation to a machine-readable form.
The quality of the trustworthy outcomes depends on the input data, so it is necessary to conduct data pre-processing.
Data pre-processing means putting your text in a predictable form for the subsequent computer analysis. Therefore, it is essential to adapt all data sets appropriately to subsequent handling.
There are several data pre-processing approaches:
Tokenization
Tokenization is the very first step in data processing. At this stage, the text should be divided into smaller parts – sentences and separate words.
Text normalization
For high-quality data processing, first if all we have to normalize text. This means a series of operations: all words are converted to one register, punctuation marks are removed, abbreviations are deciphered, numbers are represented in the textual form, etc.
Stop word removal
Stop words usually means commonly used words that do not add additional information to the text. Words like “the”, “is”, “a” have no value and only add noise to the data. So this step aims to clean sentences from useless information.
Stemming
Stemming brings different variations of the word (for example, “look”, “looking”, “looked”) to its original form (for example, “look”), removes all word appendages (prefix, suffix, ending), and leaves only the basic part of the word.
Lemmatization
Lemmatization is quite similar to stemming, but it has one difference: in this case, we use more intelligent algorithms. For example, the term “caring” may end in “care” more often than “car” as in stemming.
Part-of-speech tagging
Morphological markup is the process of tagging the tokenized part of a sentence. The most popular markup defines words as nouns, adjectives, verbs, and other parts of people’s speech.
Standard NLP techniques we can use in Healthcare
Now we’re going to talk about natural language processing use-cases in Healthcare and your benefit from this technology, but first, we have to define a few fundamental NLP techniques:
Optical Character Recognition (OCR):
OCR works when it’s necessary to process handwritten or printed text into digital format – for example, scanning a document and turning it into a PDF format. If this data has been processed once, it can be used by an NLP pipeline for further analysis.
The technology eliminates paper storage and greatly improves the document workflow within a company. OCR has considerably impacted how information is stored, exchanged, and edited.
Healthcare industry also started to implement OCR . It helps digitize clinical notes, medical history records, patient intake forms, discharge summaries, medical tests, etc.
Named Entity Recognition (NER):
Let us describe what NER is in easy words. When a human reads a book, he quickly understands that some phrases are the hero’s name, other – is a city, even if he first encountered such a title. For a computer similar recognition of real-world subjects turned out to be quite a difficult task, but still, the machines cope with it – and every year, it gets better.
Named-entity recognition (NER) is a process that extracts data and classifies atomic elements in text into predefined categories such as persons, organizations, places, quantities, monetary values, percentages, and more.
Sentiment Analysis:
It is the process of text analysis to determine the emotional tone it conveys. Sentiment analysis helps to clarify the author’s attitude to the topic. Such tools categorize texts into positive, neutral, and negative manners.
Nowadays, sentiment analysis has quite extensive use-cases. The use of technology in Healthcare includes social networks, corporate websites, or any medical internet portal where we can collect customer feedback. It helps to get an accurate picture of the user experience with the hospital services, doctors, etc.
Text Classification:
Text Classification is one of the significant NLP tasks of assigning an appropriate category to a sentence or document. Categories depend on the dataset selected and may vary from topic to topic. It has many ways to implement: spam filtering, email routing, sentiment analysis, and more. For example, it might be a situation when a healthcare provider uses text classification to identify risk-based patients on specific keywords or phrases in their medical records.
Topic Modeling:
Topic modeling can solve the lack of professional knowledge in interdisciplinary projects.
Let’s imagine such a situation: If I am a data analyst and need to learn something about electrocardiography in the shortest time. Topic modeling could say: you can find more about electrocardiography in such projects, here is the practice, here is the theory, here is the cardiology highlights, here are the studies of recent years. It allows you to navigate into groups by classifying by topics and finding necessary info quickly.
Best Use Cases of Natural Language Processing in Healthcare
Let us have a look at the most compelling use cases associated with Natural Language Processing in Healthcare:
1. AI Chatbots
Today’s digital world is overwhelmed with chatbots or virtual assistants across many industries, and Healthcare is no exception. With the advent of high-level technology, machine learning development, and medical knowledge accumulation, more intelligent bots – so-called Conversational AI Platforms-began. Such virtual assistants can analyze patient responses, ask clarifying questions, and make specific recommendations. The best of these systems help the doctor make a preliminary diagnosis by collecting the necessary data.
Thanks to integrating NLP technology into chatbots, they can now respond to patients as real humans do. So it allows taking into account natural language factors such as users’ mood and semantic analysis, “named entities” recognition, the topic of discussion, and others, which significantly increases the efficiency of communication between doctors and patients and reduces the workload on staff.
2. Clinical Trial Matching
CTM, perhaps, is the most notable in the “emerging” NLP use case category. It’s exciting and essential to use NLP to identify eligible patients for clinical trials. Near 20% of U.S. oncology trials fail to meet their enrollment targets. At the same time, pharmaceutical and life science companies still invest considerable sums in this manual form of recruitment and managing trials.
IBM Watson Health is one of the most notable use cases of clinical trial matching, which has devoted enormous resources to utilizing NLP while supporting oncology trials.
3. Speech Recognition for clinical documentation
Today, most doctors already use electronic medical records to simplify their work. But this approach still forces them to enter data manually based on patient complaints. Of course, this saves a lot of time in the future and does not require massive shelves with paper documents. Speech recognition helps NLP develop its roots in Healthcare. Thanks to it, clinicians can transcribe notes for efficient EHR data entry for nearly two decades. Speech-to-text conversion is now helping free clinics from the tedious handwriting.
The NLP technologies bring out relevant data from speech recognition equipment which will considerably modify analytical data. Such an approach has better outcomes for the clinicians and extensive possibilities.
4. Clinical Decision Support
Clinical decision support will be bolstered by NLP presence. But some solutions to support clinical decisions more acutely are being devised already.
For example, AI-driven NLP has been used to identify polyp descriptions in pathology reports that trigger guideline-based clinical decision support to help clinicians determine the best surveillance intervals for colonoscopy exams.
What about Azati?
Azati does not stand still and is always open to new investigations and technologies.
Our team researched current ML models and tools such as speech-to-text, text mining, finding similar phrases and mismatches using NLP. On its basis, our team built a unique solid model that can successfully solve project problems and create evaluation reports for pharmaceutical companies.
We have built an ML model that can automate tasks for marketing analysis in Healthcare with the following features:
- Questionnaire analysis from a group of doctors, and similar answers identification;
- Possibility to compare and correlate the aspects voiced by doctors with the messages that are embedded in the marketing strategy of pharmaceutical companies;
- Ability to generate reports from which you can see what doctors-practitioners are talking about and what they value regarding the specified pharmaceutical product (effectiveness, safety, ease of use, etc.) and how we can improve the marketing strategy for this product.
To learn more about how Azati implements NLP Technology – read our featured case study: NLP Solution For Pharmaceutical Marketing.
Summary
It is undeniable that new technologies are often more effective than old ones. Therefore, the popularity of NLP is only growing. MarketAndMarkets, a leading B2B research firm, predicts that the market for NLP-based software products, which currently stands at around $11.6 billion, will grow to $35.1 billion by 2026. Many areas of human activity can no longer do without speech recognition technology, semantic analysis, or optical character recognition due to the vast amount of unstructured data which grows alongside digital progress.
Azati experts have experience in creating products based on natural language processing. Drop us a line, and we will help you develop cutting-edge NLP-based solutions.
