I needed this yesterday. 

None of us appreciate it when our clinician’s head is buried in a computer, when what we really want is to be heard and taken care of. But, when so much has to be done within a very short timeframe, what if we as a provider miss an important clinical clue? There has got to be a better way…

Rapid and efficient diagnoses are why tools such as the first automatic blood pressure monitor were invented.  Of course, the days of Seymour B. London’s 1965 design - a prototype using a blood pressure cuff, a column of mercury, a microphone and a fish tank pump - are long gone. Now all vital signs can be checked within a few minutes, including blood pressure, electric heart signals, blood oxygen, and temperature - far more quickly and accurately than a rushed human with an armful of heavy equipment in a noisy clinical setting.

At Linguamatics our goal is to provide healthcare professionals with software that helps them do their jobs better. Giving physicians time back to be more personally attentive during the patient visit, is a high priority. Patients want to be heard. Just have a look at how many bad physician reviews follow the theme of a negative bedside manner - even if the physician achieves the right clinical outcome.

In the spirit of decreasing human error, increasing patient-physician face-time (and of course, the alternative use of a fish tank pump), we at Linguamatics are delighted to introduce our I2E Asynchronous Messaging Pipeline (aka AMP).


I2E Asynchronous Messaging Pipeline (AMP) Extract, Transform, Load (ETL) technology automates the NLP text mining of real-time documents at scale

Cambridge, UK & Boston, USA – December 6th, 2016 – Text analytics provider Linguamatics today introduced the I2E Asynchronous Messaging Pipeline (AMP) platform to help healthcare professionals find critical clinical insights faster using Natural Language Processing (NLP).

The addition of I2E AMP to Linguamatics’ award winning NLP text mining solution, I2E, makes the management of background healthcare workflows more efficient, and provides scalability as NLP text mining requirements grow. By automating the text mining of real-time documents, I2E AMP can provide healthcare professionals with rapid insights and help them make timely – and potentially critical – clinical decisions.


Innovative ETL (Extract, Transform, Load) technology frees 80% of unstructured data trapped in Data Lakes, enabling high-value knowledge discovery and decision support

Cambridge, UK & Boston, USA – 30th November, 2016 – Text analytics provider Linguamatics today released the latest version of their award-winning natural language processing (NLP) text mining platform, I2E 5.0.

Game-changing capabilities in I2E 5.0 include normalization of concepts (e.g. dates, measurements, gene mutations) within unstructured text, advanced range search and a new query language EASL. These capabilities tackle the variety in big data, and accelerate insights from unstructured, semi-structured and structured data sources.

Normalization and range search helps users find key information (e.g. a particular temperature or a range of temperatures) in unstructured text sources regardless of how the information is expressed, and boosts ETL operations by identifying, extracting and standardizing data. Given that around 80-90% of big data is unstructured, these new text mining capabilities allow huge amounts of data to be processed that previously had to be read manually.


Uncovering new toxicities from chronic non-rodent studies

Preclinical toxicology studies are an essential part of the drug discovery-development pipeline, to support the safe conduct of clinical trials. And drug safety is, of course, one of the most critical aspects to ensure during drug development.

We were pleased to see the recent publication by Merck on a text-mining approach to assess the value of chronic non-rodent toxicology studies. 

Preclinical safety assessment groups employ a variety of animal models and assays to satisfy regulatory agency requirements to identify and characterize drug toxicities, describe drug exposures, and provide qualitative and quantitative risk assessments for human exposure. These require considerable resource investment, however the results are often “locked away” in internal reports. This means re-use of these valuable data is difficult and costly.

This is a common situation within the pharmaceutical industry – where critical information is locked away in textual reports, such as the informed scientific conclusions of pathologists, histologists, safety experts. Natural language processing can overcome the barriers, extracting structured facts from unstructured documents, and Merck’s paper describes an evaluation of a text mining workflow to access these important data.


Press Release: Natural Language Processing (NLP) to Optimize Clinical Trials: I2E Hackathon at the Linguamatics Text Mining Summit - Using text mining to address healthcare information challenges