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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.


The past few weeks have been busy: we’re fresh from our Text Mining Summit, which included a dedicated training session for users who wished to develop against the I2E Web Services API.

I also had the opportunity to go on site to a customer to provide some focused API training.

These sessions generated lots of interesting questions about automating processes from an administration perspective as well as a user perspective.

As I was presenting some high-level slides during the Text Mining Summit, I noted that I was mixing up put and post (and sometimes place and push!) in a way that is forgivable when using them as English verbs, but unhelpful when trying to explain a RESTful Web Service.

So after the Summit, I went back to our Developers Guide and back to my notes and started over, to create a helpful explanation of when you POST and when you PUT to the I2E Server.

Both PUT and POST are methods to transfer data to the server and there are some use cases when they can be used interchangeably.

One example of that is creating a new file called newfile.txt in the Source Data collection on the I2E server:

PUT url=http://i2eserver:8334/api;type=data/newfile.txt data=filecontent

POST url=http://i2eserver:8334/api;type=data/?base=newfile.txt data=filecontent
 

The end result will be the same in either case, but as you can see from the URL, the resource name is set differently:


The Linguamatics Text Mining Summit has become Linguamatics flagship US event over the past few years attracting a wide variety of attendees from Pharma, Biotech and Healthcare industries.

This year was no exception; the Summit drew a record crowd of over 85 attendees and a fantastic line up of speakers including: AMIA, Shire Pharmaceuticals, Huntsman Cancer Institute, AstraZeneca, Regeneron Pharmaceuticals, Merck, Georgetown University Medical Center, UNC Charlotte, City of Hope, Pfizer and Roche.

The Summit took place at the Hyatt Regency, Newport, Rhode Island in the beautiful surroundings of the Narragansett bay from October 7-9, 2013.

Delegates were provided with an excellent opportunity to explore trends in text mining and analytics, natural language processing and knowledge discovery.

Delegates discovered how I2E is delivering valuable intelligence from text in a range of applications, including the mining of scientific literature, news feeds, Electronic Health Records (EHRs), clinical trial data, FDA drug labels and more. Customer presentations demonstrated how I2E helps workers in knowledge driven organizations meet the challenge of information overload, maximize the value of their information assets and increase speed to insight.

One customer presentation explained how a “Recent I2E literature search saved $180K and 2-3 months [of] time”.