Rehospitalization is a serious problem in medicine.

Medical aspects are complicated by end of life care issues as well as a regulatory environment in which hospitals can experience financial penalties for "excess" rehospitalization rates. Existing rehospitalization predictive models, most of which are based on administrative data, have poor statistical performance, as do models that employ limited physiologic data.

At Linguamatics' upcoming seminar in San Francisco, Dr. Escobar will present work on a new rehospitalization model that employs data from a comprehensive electronic medical record and which could be instantiated in real time.

He will also present a "road map" to explain how data from natural language processing can be incorporated into this model as well as on future strategies for instantiation of NLP engines into routine clinical operations.

Dr. Escobar is a research scientist at the Kaiser Permanente Division of Research in Oakland as well as being the Regional Director for Hospital Operations Research for Kaiser Permanente Northern California.

An expert on risk adjustment and predictive modeling, Dr. Escobar has published over 130 peer-reviewed articles and is currently in the middle of deploying a real-time early warning system for deterioration outside the intensive care unit at two Kaiser Permanente hospitals.


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.


Last year Georgetown University Medical Center launched the Center for Innovation in Leadership and Education (CENTILE).

In June I presented a poster at the first CENTILE  Colloquium for GUMC Educators in the Health Professions.

My poster Using iPads to Enhance Teaching and Learning on Patient Rounds explained how I have used iPads over the last four years on patient rounds to improve the education of medical students and residents at GUMC. I plan to continue to be involved with CENTILE in the future as I explore further innovative uses of technology in education.

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It’s always good to see NLP being used in a clinical care, a recent story about Microsoft and Washington University in Seattle using NLP in pneumonia detection in the ICU is a good example of this.

The project, called deCIPHER, uses a combination of Microsoft linguistics and machine learning to assess clinical information from electronic medical records and derive a diagnosis.

The system was trained against a cohort of 100 patients who had already been diagnosed with pneumonia and used a machine learning framework to build a predictive model based on extracted clinical factors. The system accurately predicted 84% of positive patients and the team are assessing incorporating the model into an ICU dashboard.

Last year Kaiser Permanente also published a paper on pneumonia diagnosis in relation to the ICU and using chest radiograph reports, using Linguamatics I2E for information extraction and also applying machine learning to the resulting clinical factors.

From a total of 194,615 ICU reports, Kaiser Permanente empirically developed a lexicon to categorize pneumonia-relevant terms and uncertainty profiles.


More than 35,000 healthcare industry professionals are expected to attend the 2014 Annual HIMSS Conference & Exhibition in Orlando to discuss health IT issues and view innovative solutions designed to transform healthcare.

Linguamatics is proud to be an exhibitor at this annual event that helps health IT professionals find the right solutions for their organizations.

Hillary Clinton, 67th Secretary of State of the United States, leads a keynote roster that also includes Mark Bertolini, chairman, CEO and president of AETNA, and Erik Weihenmayer, a world-class blind adventurer.

On the exhibit floor, the enhanced HIMSS Interoperability Showcase will feature an interactive environment where health IT solution providers can collaborate to maximize the collective impact of their technologies and connect with decision makers.

Linguamatics will showcase its leading clinical Natural Language Processing platform, which transforms the unstructured text from electronic health records into patient insights. Demonstrations include information extraction from pathology reports and patient narratives, and matching patients to clinical trials based on inclusion and exclusion criteria.

To learn more about Linguamatics, visit us at booth #1794 during HIMSS14, February 23-27, 2014, at the Orange County Convention or take a look through our website.

For more information about HIMSS14 and to register, visit www.himssconference.org.