Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that studies how machines understand human language in other words, NLP deals with the interaction between computers and humans using the natural language. Its goal is to build systems that can make sense of text and perform tasks like translation, grammar checking, or topic classification, clinical solutions and etc.
Natural Language Processing (NLP) systems generate structured information from unstructured free text.
NLP clinical solutions
Many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated will be important for prioritizing development of new approaches for clinical NLP.
Important clinical information is often recorded in unstructured free text, and converting it to a structured format can be a time Consuming task that may not successfully capture all facets of the information. However, there are at least two large incentives for translating unstructured data into structured data: i) the reduction of time required for manual expert review and ii) the secondary use of these data for large scale automated processing. The former goal is an obvious benefit for anyone involved in clinical practice, where physicians and other experts examine patients’ electronic health records (EHRs) on a regular basis and spend
Given the rate at which unstructured clinical information is created, it is clear that automated solutions utilizing Natural Language Processing (NLP) are needed to analyze this text and generate structured representations.
Current NLP systems have proven to be useful for certain activities and have, for example, reduced the time required for screening candidates for clinical trial eligibility.
Besides, the FDA and the Centers for Disease Control and Prevention (CDC) recently launched a collaborative effort for the ‘‘Development of a Natural Language Processing (NLP) Web Service for Structuring and Standardizing Unstructured Clinical Information”.