NLP at the bedside to improve patient care

IBM Watson gets a lot of attention in the medical field for trying to take capabilities that were demonstrated on the Jeopardy TV show and apply that cognitive reasoning to clinical care.

The complexities of disease combined with the mass of medical literature and clinical guidelines make this high dimensional problem an appropriate challenge for an industrial power house.

However, it should not be underestimated what can be achieved using sophisticated Natural Language Processing (NLP) for information retrieval in clinical decision support.

One of my favourite customer stories in recent years concerns our work with medical librarian Jonathan Hartmann from Dahlgren Memorial Library, the health sciences library at Georgetown University.

Jonathan’s role is to support the teams on the hospital’s paediatrics and internal medicine units on rounds at the Georgetown University Medical Center with access to the latest medical insights and publications relating to the current patient.

For example, should a patient with metastatic renal cell carcinoma be given warfin (an anticoagulant) for stroke prevention? Using his iPad at the bedside, Jonathan was able to quickly find journal articles that indicated cancer treatments and potentially cancer spread can indeed increase the risk of stroke.

You can read more about the story here.

From a technical perspective the use of NLP in this scenario is well hidden, as it should be, and simply ensures that the right information is provided to assist in clinical decision making.

Linguamatics developed the iPad application discussed in this story in close collaboration with Jonathan using our I2E NLP platform to power the information retrieval capabilities.

The challenge was to identify very specific journal articles using the exact characteristics of the patient, especially disease comorbidities and co-treatments.

We also had to ensure that while we gave specific results, we were also providing all relevant options and were not missing articles due to naming conventions or synonyms being used. This type of challenge is well suited to our I2E engine, which has been used widely for information retrieval in the pharmaceutical industry for over 10 years.

By using domain ontologies for diseases and medications for example to index the 22 million abstracts in MEDLINE we are able to ensure that each term that is entered is expanded with appropriate synonyms and abbreviations when the search is run.

Our NLP capabilities then allow us to filter down the hits to articles where those concepts are associated together in a relevant paper and are linguistically linked, as opposed to simply being in the same document.

This has been an exciting project and one that never fails to fire the imagination of the people we and Jonathan share it with. It shows how NLP can have a measurable impact at the bedside by allowing clinical decisions to be taken immediately, saving hours and sometimes days of time. Such projects also demonstrate the flexibility of NLP in addressing information challenges in healthcare, you can find out more about this here.

Jonathan will be sharing his experiences on using I2E to improve patient care at this year’s Linguamatics Text Mining Summit, which will take place on October 13-15 in Newport, RI.