Natural language processing (NLP) tools for health analytics
Natural language processing (NLP) is the process of using computer algorithms to identify key elements in language and extract meaning from unstructured spoken or written text. NLP combines artificial intelligence, computational linguistics, and other machine learning disciplines.
In the healthcare industry, NLP has many applications such as interpreting clinical documents in an electronic health record. Natural language processing is important in clinical decision support systems by extracting meaningful information from free-text query interfaces. It may reduce transcription costs by allowing providers to dictate their notes, or generate tailored educational materials for patients ready for discharge. At a high-level NLP includes processes such as structure extraction, tokenization, tagging, part of speech identification and lemmatization.
“cTAKES is a natural language processing system for extraction of information from electronic medical record clinical free-text. Originally developed at the Mayo Clinic, it has expanded to being used by various institutions internationally.”
cTAKES is relatively difficult to install and use, especially if the service needs to be shared by several systems. I have integrated cTakes into an easy to use spring boot application that provides REST web services for clinical document annotation. The repository is here.
[github-clone username=”dermatologist” repository=”ctakes-spring-boot”]
You need a UMLS username and password for deploying the application. RysannMD is an efficient and fast system for annotating clinical documents developed at Ryerson University. Some of my other experiments with NLP are available here.
Are you working on any NLP projects in medicine?