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Spring is a lovely time to be in Cambridge – winter is finally moving on, the spring bulbs are out and the trees are in blossom. Time for Linguamatics Spring Text Mining Conference, which again this year was blessed with lovely sunshine. And of course, the opportunity to hear the latest about Linguamatics products and some new and fascinating use cases from our customers.

In March 2019, attendees from across pharma and healthcare came to our Spring Text Mining Conference, for hands-on workshops, a Healthcare Hackathon, networking and great presentations. The presentations covered innovations in using Natural Language Processing (NLP) to get more value from a range of unstructured text, covering electronic medical records, regulatory documents and patient social media verbatims.


Digital transformation often induces a disruption in our systems, in the way we use technology, human intelligence and processes to enhance business performance. Life science organizations are generally embracing the necessary digital revolution but digital transformation demands data transformation, which includes developing strategies to access information buried in text.

Data-driven decision making

During the upcoming Bio-IT World Conference & Expo, Jane Reed, Linguamatics Head of Life Science Strategy, will present a talk on "Natural Language Processing: enabling data-driven rather than document-driven decision making". Natural Language processing (NLP) allows organizations to focus on data-driven rather than document-driven decision making in a timely manner. The technology is already helping people in life sciences and healthcare, even non-programmers, to transform unstructured text into actionable structured data that can be rapidly visualized and analyzed, for decision support from bench to bedside.


Throughout my journey of becoming a physician, I have been privy to lots of varying medical opinions/practices. One thing I have noticed is a lack of taking the entire patient, that is all the information available about a patient, into consideration. Now, working for a company specializing in Natural Language Processing (NLP), I find myself wondering how could NLP have helped in these situations?

One stop shopping

I once came across a chain-smoking cardiologist, who would eat fast food everyday. He found it pretty humorous when I cracked a joke about his office proximity to the chain restaurant. I suggested that he put in a revolving door connected to the restaurant and start a campaign of “One Stop Shopping! Get your arteries filled and roto-rootered all within walking distance!” That is if you can walk about 100 feet with a lifestyle such as that. His response? Something about not being a nutritional expert: “my expertise is the heart!” At least, he did always tell his patients not to do what he did.


Recently, I found myself in a discussion with some colleagues around whether artificial intelligence (AI) could increase commercial engagement and sales productivity; specifically, the Linguamatics flavour of AI, Natural Language Processing (NLP). My first reaction was no – our customers tend to use NLP to pull out critical information for safety assessment from internal reports, genotype-phenotype associations from literature, inclusion/exclusion criteria from clinical trial records; and many more examples that impact drug R&D.

But as the discussion progressed, I realized that as our customers drill more and more into the power of NLP to unlock value from real world data, the answer is actually yes. NLP enables data-driven, rather than document-driven decision support, by extracting key concepts and context from unstructured documents, which can then be rapidly reviewed and analysed. So, since much real world data is unstructured text, NLP can bring real productivity gains.

Challenges for pharma medical field teams

Let me give you some background, and then some examples.

Over recent years, pharma sales reps and medical science liaison staff (MSLs) have faced increasing challenges around access to Key Opinion Leaders (KOLs), physicians and prescribers, due to a more restrictive regulatory environment, new healthcare business models and evolving economic conditions. The boundaries for how pharma sales reps can interact with physicians are more limited, for example the “lunch and learn” meetings that used to be a key tool have been significantly curtailed. In parallel, the pressures on physicians to see more patients also reduces the time they have to learn about new drugs or improved therapies.


It's been more than a few months since the last blog post about I2E - enough time for two versions to be released!

In this post, I will highlight one feature from each release, as well as one part of the product that improves with every release.

I2E 5.4.1 - Blank Smart Query Slots

Previously, Smart Queries required a search term in each field: you had to include a Phase (or multiple Phases). In I2E 5.4.1, it is now possible to design the smart query with a field that is allowed to be blank. For example, you may want to make it possible to leave the Phase field blank (that is, allow clinical trials with any Phase) but still search for an Indication (or multiple Indications).

This applies to Dates, Authors, Locations, etc. any Smart query item can have its field blank. A great example of this is the collection of Table queries in the Resources query tree: they now allow table headers to contain Any item.

The software screenshot shows that an out-of-the-box Table Extraction queries supports the new ability to allow a Smart query field to match Anything

Fig. 1 Out-of-the-box Table Extraction queries support the new ability to allow a Smart query field to match Anything