Linguamatics is pleased to announce the latest release of its award-winning natural language processing (NLP)-based text mining and analytics platform, I2E 4.4.

This latest release expands the range of online content access available through I2E OnDemand to include FDA AERS data, from the US Food and Drug Administration’s Adverse Events Reporting System.
 

Software enhancements

I2E 4.4 also adds a number of important software enhancements, including an NLP plugin framework to support non-English languages, enhanced capabilities for viewing chemical structures, better extraction of information from tables, and a new human - readable query language.

FDA AERS is typically used to monitor and discover safety issues in drugs released for public use. This new addition to Linguamatics’ cloud based I2E OnDemand platform allows users to immediately start mining this valuable safety data source without the overhead of downloading, processing and maintaining the information themselves.

The availability of a new multi-language plug-in framework in I2E 4.4 builds on the theme of this release to extend text mining to a wider range of content.

Text miners can now analyze documents written in a new language by plugging in an appropriate third-party language module.
 

Extended support for PowerPoint, Word and Excel

I2E 4.4 also delivers a number of further improvements, such as extended support for Microsoft PowerPoint, Word and Excel documents. This allows efficient review of document repositories, including extraction of information from tables.
 


A new speaker has just been announced for the annual Text Mining Conference hosted in Cambridge, UK. This annual conference has been running for over 10 years and features text analytics use cases particularly across pharma and life science. Information professionals across top 100 pharma and life science organizations gather to share insight, best practice and discuss the future of text mining technology.

Eleanor Yelland will be presenting on: I2E in mental health: Analysis of online transcripts used in cognitive behavioural therapy.

Eleanor is a PhD Student in the Division of Psychiatry at University College London. Her PhD is a partnership with Linguamatics and Ieso Digital Health, who provide text-based online cognitive behavioural therapy.

The project focuses on the language within the treatment sessions and how text mining methods can be applied to best use this to learn about and improve treatment provision. The work primarily involves identifying potentially relevant linguistic characteristics, measuring these and building statistical models of their relationship with therapy outcome scores.  

This adds to a world-class list of speakers across pharma and healthcare who will be presenting at the conference, including:

Jonathan Hartmann, Georgetown University Medical Center: Evolution of I2E to improve patient care

Thierry Breyette, Novo Nordisk: Generating Actionable Insights from Real World Data

Cassie Gregson, AstraZeneca: Application of Text Mining to Clinical Research


Faced with the challenges of Accountable Care, the triple aim and Meaningful Use, NLP will help CMIOs to improve insights into patient and population health.

CAMBRIDGE, UK and BOSTON, USA - November 10th, 2015 – Chief Medical Informatics Officers (CMIOs) at US healthcare providers see that Accountable Care, the triple aim and Meaningful Use are all creating an unprecedented demand for more insights from patient data. Since much of the key information is locked away in unstructured data, the overwhelming majority of CMIOs believe that rapidly increasing the use of Natural Language Processing (NLP) will be a critical step in accessing this data and thus improving the delivery of patient care. These are the key findings of a recent study prepared by Linguamatics, with support from the American Medical Informatics Association (AMIA).

Linguamatics commissioned their “Assessing the Role of Clinical NLP in the Delivery of Patient Care” report, with the aim of discovering how CMIOs envisage using NLP in new applications that both enhance patient care and improve hospital efficiency. The report succeeded in uncovering four key areas where CMIOs foresee key developments:

• The CMIOs surveyed considered the potential improvement in the quality of care and patient safety, and the resulting reduction in costs that could be generated with predictive models, to be the most important application involving NLP. For example, CMIOs expressed interest in applications such as predicting hospital readmissions or outmigration/patient leakage.


I’m thrilled to see that Linguamatics I2E 4.3 is named as a KMWorld 2015 Trend-Setting Product.  Linguamatics I2E has a proven track record in delivering best of breed text mining capabilities across a broad range of application areas. Its agile nature allows tuning of query strategies to deliver the precision and recall needed for specific tasks, but at an enterprise scale.

According to customers, I2E gets to actionable results at least 10 times faster than a traditional keyword search. In many cases, I2E will produce successful results for projects that would otherwise be impossible or intractable.

Actionable information extracted using I2E can be presented in a variety of ways depending on your needs. NLP-based text mining provides the capability to look through unstructured text (typically in large sets of documents, from scientific reports, patents, or electronic healthcare records, pathology and radiology reports); and use sophisticated queries to automatically identify and extract out structured data (concepts and associations) to enable the system to interpret the meaning of the text. 

 


Linguamatics I2E natural language processing technology to automatically extract clinical attributes from pathology reports across eight hospital groups in Stratified Medicine Programme.

LONDON and CAMBRIDGE, UK, September 1st, 2015 – Cancer Research UK and Linguamatics announced today they will work on a joint project to apply Linguamatics’ natural language processing (NLP) text analytics platform, I2E, to automatically extract clinical attributes from cancer pathology reports and improve annotation of clinical samples relating to Cancer Research UK’s Stratified Medicine Programme (SMP). This project will allow the analysis of detailed patient characteristics alongside large volumes of genetic data, enabling more effective research into the causes and personalised treatment of cancer.

Dr Ian Walker, Director of Clinical Research and Strategic Partnerships at Cancer Research UK, said: “Pathology reports tell us a range of important information about a patient’s cancer, but the way this data is recorded can vary widely, which makes it harder to spot trends or other significant information that could have a bearing on treatment decisions or prognosis. This collaboration should help translate these reports into more meaningful data, which should help our researchers better understand the disease and accelerate advances in personalised medicine.”