Natural Language Processing (NLP) is a branch of computational linguistics, computer science, and artificial intelligence that helps computers analyze, understand, interpret and manipulate human language, in its pursuit to fill the gap between human communication and computer understanding. It was formulated to build software that generates and comprehends natural languages so that a user can have natural conversations with his or her computer instead of through programming or artificial languages like Java or C.
The ideal result of NLP is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
Specific tasks for NLP systems may include:
- Summarizing lengthy blocks of narrative text, such as a clinical note or academic journal article, by identifying key concepts or phrases present in the source material
- Mapping data elements present in unstructured text to structured fields in an electronic health record in order to improve clinical data integrity
- Converting data in the other direction from machine-readable formats into natural language for reporting and educational purposes
- Answering unique free-text queries that require the synthesis of multiple data sources
- Engaging in optical character recognition to turn images, like PDF documents or scans of care summaries and imaging reports, into text files that can then be parsed and analyzed
- Conducting speech recognition to allow users to dictate clinical notes or other information that can then be turned into text
NLP in Healthcare
In the healthcare industry, Natural language processing has many potential applications. NLP can enhance the completeness and accuracy of electronic health records (EHRs) by translating free text into standardized data. It can fill data warehouses and semantic data lakes with meaningful information accessed by free-text query interfaces. It may be able to make documentation requirements easier by allowing providers to dictate their notes or generate tailored educational materials for patients ready for discharge.
But perhaps of greatest interest right now, especially to providers in desperate need of point-of-care solutions for incredibly complex patient problems, NLP can be – and is being – used for clinical decision support. The most famous example of a machine learning NLP in the healthcare industry is IBM Watson, which has dominated headlines due to its growing expertise and applications in clinical decision support (CDS) for precision medicine and cancer care.
There is a huge amount of data in the healthcare space and finding the best ways to extract what’s relevant and bring it together to help clinicians make the best decisions for their patients is a new challenge that the industry faces. Natural language processing algorithms can be run against these medical data to automatically extract features or risk factors.
A few of the many examples of NLP in the clinical decision support and risk stratification realms include:
- In 2013, the Department of Veterans Affairs used NLP techniques to review more than 2 billion EHR documents for indications of PTSD, depression, and potential self-harm in veteran patients. The pilot was 80 percent accurate at identifying the difference between records of screenings for suicide and mentions of actual past suicide attempts.
- Researchers at MIT in 2012 were able to attain a 75 percent accuracy rate for deciphering the semantic meaning of specific clinical terms contained in free-text clinical notes, using a statistical probability model to assess surrounding terms and put ambiguous terms into context.
- Natural language processing was able to take the speech patterns of schizophrenic patients and identify which were likely to experience an onset of psychosis with 100 percent accuracy. The small proof-of-concept study employed an NLP system with “a novel combination of semantic coherence and syntactic assays as predictors of psychosis transition.”
- At the University of California Los Angeles, researchers analyzed electronic free text to flag patients with cirrhosis. By combining natural language processing of radiology reports with ICD-9 codes and lab data, the algorithm attained incredibly high levels of sensitivity and specificity.
- Researchers from the University of Alabama found that NLP identification of reportable cancer cases was 22.6 percent more accurate and precise than manual review of medical records. The system helped to separate cancer patients whose conditions should be reported to the Cancer Registry Control Panel from cases that did not have to be included in the registry.
Natural language processing in healthcare is currently in its initial phases, but its applications are already starting to create ripples in the healthcare sector. Cognitive computing and semantic big data analytics projects, both of which typically rely on NLP for their development, are seeing major investments from some recognizable names. From the most cutting-edge precision medicine applications to the simple task of coding a claim for billing and reimbursement, NLP has nearly limitless potential to turn unstructured data in electronic health records from burden to boon.
Being a healthcare solutions company, Wiseyak is aware of the potential of NLP in healthcare and is proactively working to develop NLP powered tools to better understand healthcare data. While we may still be in early stages of development, the end goal would be to incorporate these tools into the WiseMD platform in order to create a truly intelligent platform which can aid in diagnostics and treatment through clinical decision support.