Natural language processing is used in various industries and fields. In the healthcare industry, it has particularly been used for a long time and extensively. Some may say that Natural Language Processing for Healthcare is the key to the integration of technology and the provision of care. But what is it really? It’s a term that is used a lot but the correct definition might be elusive to people in the healthcare industry and most people in general.
Natural Language Processing Defined
Natural language processing, often abbreviated as NLP, is defined as the ability of a computer to understand human speech as it is spoken. Natural language processing is a key component of artificial intelligence (AI) and relies on machine learning, a specific type of AI that analyzes and makes use of patterns in data to improve a program’s understanding of speech. Put simple, artificial intelligence is made possible by natural language processing and natural language processing is made possible by machine learning. It’s complicated yes, hut a base understanding is enough to give you a general sense of the workings of NLP.
Uses of Natural Language Processing
Natural language processing is used for a variety of tasks in technology, healthcare, agriculture, pharmaceutical, banking, sports, automobile, and of course technological spaces. Let’s discuss a few of those:
Natural Language Processing for Healthcare
In the healthcare industry, Natural language processing can be leveraged by hospitals to improve the efficiency of documentation processes, improve the accuracy of documentation, and identify the most pertinent information from large databases. For example, a hospital might use natural language processing to pull a specific diagnosis from a physician’s unstructured notes and assign a billing code.
Natural language processing can be used to analyze parts of a sentence to better understand the grammatical construction of the sentence. It can carry out part of speech tagging and parsing.
Natural language processing carries out deep analytics. Deep analytics involves the application of advanced data processing techniques that are used to extract specific information from large or multi-source data sets. Deep analytics is used for complex queries and unstructured or semi-unstructured data. Natural language processing is used a lot in the financial industry and deep analytics is one of the aspects that’s used a lot. Deep analytics is also used in the scientific community and the drug research and pharmaceutical sector. It’s also used in any sector that mines big data.
Besides translating human language to machine language, Natural language processing also translates one human language to another. Natural language processing is increasingly being used for machine translation programs, in which one human language is automatically translated into another human language. Natural language processing is a class of technology that seeks to process, interpret and produce natural languages such as English, Mandarin French, and Spanish. Real world use of natural language doesn’t follow a well-formed set of rules and exhibits a large number of variations, exceptions and idiosyncratic qualities. As such, natural language processing is often tackled with artificial intelligence techniques designed to automate the learning process.
Named Entity Extraction
In data mining, a named entity definition is a phrase or word that clearly identifies one item from a set of other items that have similar attributes. Examples include first and last names, age, geographic locations, addresses, phone numbers, email addresses, company names, etc. Named entity extraction, sometimes also called named entity recognition, makes it easier to mine and store data.
Natural language processing can be used to produce a readable summary from a large chunk of text. For example, one might use automatic summarization to produce a short summary of a dense academic article. This is obviously, used a lot in the academic field and by researchers.
As shown above, the benefits of natural language processing are innumerable with more benefits being discovered still.
Types of Natural Language Processing for Healthcare
The following are common types of natural language processing:
- Optical Character Recognition
- Converting written or printed text into data.
- Speech Recognition
- Converting spoken words into data.
- Machine Translation
- Translating text from one language to another.
- Natural Language Generation
- Formatting information as a natural language. For example, a weather system that generates a weather forecast in paragraph form.
- Sentiment Analysis
- Interpreting basic information from language such as the topic being discussed and whether comments are positive or negative. For example, brand engagement metrics might look at how many people are talking about a brand and how much of that talk is positive.
- Semantic Search
- Understanding and answering questions posed in a natural language. For example, accurately processing a search query such as “give me the address of that bar I went to last weekend.”
- Machine Learning
- Using natural language to train artificial intelligence. This can be learning related to language itself or another topic such as economics.
- Natural Language Programming Tools that allow end users to create or customize computer programs with natural language. For example “make me an app that suggests new careers paths to me based on people with similar skills and experiences who have successfully improved their salary and quality of life with a new career.” This is used a lot in job boards or some job networking sites like LinkedIn or Monter.
- Affective Computing
- Affective computing is the machine equivalent of emotional intelligence. Allows user interfaces to understand human ideas and emotional states and to take on human-like conversations.