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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.


I saw this comment in a recent article by Seth Grimes, where he discusses the terms Text Analysis and Text Analytics.

Within the article Mr. Grimes states that text mining and text analytics are largely interchangeable terms:

“The terms “text analytics” and “text mining” are largely interchangeable. They name the same set of methods, software tools, and applications. Their distinction stems primarily from the background of the person using each — “text mining” seems most used by data miners, and “text analytics” by individuals and organizations in domains where the road to insight was paved by business intelligence tools and methods — so that the difference is largely a matter of dialect.”

Ref: Seth Grimes at the Huffington Post.

I asked Linguamatics CTO, David Milward, for his thoughts:

"There is certainly overlap, but I think there are cases of analytics that would not be classed as text mining and vice versa. Text analytics tends to be more about processing a document collection as a whole, text mining traditionally has more of the needle in a haystack connotation.

"For example, word clouds might be classified as text analytics, but not text mining. Use of natural language processing (NLP) for advanced searching is not so naturally classified under text analytics.


So, an I2E user has built a great query that detects side effects and adverse events for a drug. It looks ideal as a candidate for repeated use: for example, search MEDLINE whenever it is updated.

The I2E user has also saved this query as a Smart Query, meaning the drug can be changed when the query is run. Changing the settings of a smart query in the I2E client is easy: type in a few words, click to add a class or load in a list of alternatives, or some combination of those options.

So how can options like these be transferred to the I2E server as part of the query using the Web Services API?

The answer (if you haven’t guessed from the title of this post) is to use the I2E Query Notation, a simple textual syntax for specifying words, phrases, alternatives, classes and macros. It is based on the notation that has been in use in I2E Express queries for some time, so a compound search like:

["big brown bear" mouse* CAT^]

would find the phrase “big brown bear” or mouse (and variants like mice) or CAT (but not cat, Cat or cAt).

As well as words, phrases and alternatives, you can refer to classes in your I2E indexes using the identifier for the class (its “nodeid”) and the identifier for the ontology to which it belongs (its “supplierid”): together they uniquely identify a class in an index. The format is

/sn<supplierid.nodeid>


(Cambridge, England and Boston, USA – November, 18 2013) Linguamatics, the market-leader in natural language processing-based (NLP) text mining and analytics, today announced the launch of Linguamatics Health, a new clinical NLP suite that enables hospitals and research organizations to harness the information contained in unstructured fields of EHRs and patient narratives to drive healthcare analytics, advanced research and improved patient outcomes. 

Linguamatics Health provides the technology needed to extract meaningful information from the mass of data located in complex patient documentation such as pathology and radiology reports, physician notes, and discharge reports. The information is then used in data warehouses, predictive models and dashboards to improve hospital efficiency and support Meaningful Use initiatives.

The information can also be used to populate clinical annotations for biobanks and provide data for Clinical Trial Management Systems to improve disease understanding and clinical trial recruitment. 

“While the rapid adoption of EHRs in recent years has integrated many data silos together, healthcare providers are still faced with a large proportion of their data in unstructured form.

"To achieve the improvements in hospital efficiency and patient outcomes required to cope with rising costs and an aging patient population, hospitals, payers and other healthcare organizations need to make better use of unstructured text,” said Phil Hastings, Senior Vice President, Sales and Marketing, at Linguamatics.


Linguamatics, the market-leader in natural language processing (NLP)-based text mining and analytics, today announced that Huntsman Cancer Institute (HCI) at the University of Utah has deployed its NLP based I2E software platform to transform the immense stores of unstructured text in electronic health records (EHRs) into actionable information to drive improvements in cancer research, treatments and outcomes.

HCI is using Linguamatics I2E with its in-house clinical informatics infrastructure to extract discrete data from the unstructured text contained in surgical, pathology, radiology, and clinical notes related to hematology disease areas such as Leukemia and Lymphoma.

The resulting data is loaded into an integrated biobanking, clinical research, and genomic annotation platform. This enables HCI’s clinicians and principle investigators to harness the richest possible set of data for research into patient outcomes, comparative effectiveness, and genetic drivers of disease.

Analysis at this scale can find information that would often be missed when reading documents one at a time.

In addition HCI has a better range and quality of data to support clinical trial matching and increase numbers of patients on trials.

“Healthcare organizations face a major challenge to identify, capture and leverage valuable knowledge buried within vast stores of complex, unstructured patient data, and to do it in a reproducible and scalable way”, commented Phil Hastings, Senior Vice President, Sales and Marketing, at Linguamatics.