Posts from October 2017

November always brings to mind Thanksgiving and turkey, but for those of us in medical informatics it means it’s time for AMIA’s national symposium and this time it is back in Washington DC. With political upheaval in healthcare and the opioid epidemic making headlines across the nation there will be no shortage of talking points. AMIA brings together some of the best and the brightest minds in medical informatics and a great place to engage with each other to highlight opportunities for IT to improve the lives of Americans with technology.

This will be my fifth time going to AMIA, a relative novice compared to many, but I find the scale of this event much more palatable than the behemoth that is HIMSS, with a more forward-looking vibe. Linguamatics have many events planned for AMIA this year, including a pre-symposium talk, a presentation on physician metrics by MUSC and a Learning Showcase presentation. Check out the details below and stop by and see us at booth #205.

Using Text Mining to Identify Risk of Opioid Medication Abuse

Presented by Erin Tavano, Clinical Data Scientist, Linguamatics

8:30AM – 4:30PM, Saturday November 4, 2017, Georgetown East


Media reports on the opioid abuse crisis in the U.S. increasingly dominate our TV screens and news feeds. But when do we start recognizing these statistics as actual people in need of help—what if we can identify at-risk individuals while they still have options?

Early recognition of opioid misuse is key to identifying people at risk and getting them timely treatment, but those “dancing with the devil” are hard to find in the early stages, and rarely come forward on their own.

In an article for HITECH Answers, Linguamatics’ Dr. Elizabeth Marshall reflects on how the crisis has affected her on a personal level. She looks at how using natural language processing (NLP) to analyze structured and unstructured data from sources such as clinician notes could help identify patterns that reveal possible opioid abuse. Dr. Marshall also suggests further measures that organizations, institutions, and communities might take to combat this growing epidemic.

Read the full article here.


Linguamatics I2E takes NLP Text Mining to New Heights

Last week I was at the Linguamatics Text Mining Summit – for a feast of new experiences. The Summit was hosted at Wentworth-by-the-Sea, a new venue for the TMS, and it was a wonderful showcase for 3 days of workshops, round table discussions, and talks.  

In addition to a new location, the attendees learned about fresh ways of approaching challenges across both life science and healthcare.  Some of the innovations came from the recently released features of I2E 5.2, many others from our customers.

A relatively new use of Natural Language Processing (a well-established AI technology), is to power machine learning (ML). David Milward (Linguamatics) discussed how I2E both utilises ML and can also effectively feed ML workflows with high quality data. Simon Beaulah (Linguamatics) gave an overview of new applications of I2E NLP in healthcare; several of which involve using NLP to fuel ML models. These include predicting 30-day readmissions; extraction of cardiac risk factors; or patient stratification of heart failure risk from echocardiogram metrics.

A broad range of NLP Text Analytics Applications across Life Science and Healthcare

Using text mining for ETL (extract transform and load) is becoming more widespread. Several of our customers talked about the power of NLP to extract structured data from unstructured text, and load the results into databases, warehouses or data lakes for broader access and decision support:


There’s growing interest in the use of machine learning to solve challenges across the drug-discovery pipeline within the biopharmaceutical community. The availability of high quality data for training algorithms is vital to machine learning success - but much of this information is tied up in unstructured, or semi-structured text sources. Natural language processing (NLP) is the key to extracting the wealth of data hidden in unstructured text, and Linguamatics’ customers have been finding out first-hand what this approach can do for them.

Using Linguamatics I2E NLP text mining:

  • Eli Lilly researchers mine adverse event data to identify potential new uses for existing drugs.
  • A top-10 pharma company process and understand unstructured “voice of the customer” call feeds, to categorize the feeds and help build predictive models.
  • Roche and Humboldt University of Berlin identified MEDLINE abstracts containing both the protein target and specific disease indication of a known set of cancer therapeutics, and applied machine learning to predict the success or failure of drugs in Phase II or III with high accuracy.

Read the full “Data-driven NLP plus machine learning” application note to find out more about how NLP can support effective machine learning projects.