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Government

Drug safety is, understandably, a prime concern for pharma organizations, regulators, health authorities and patients alike. While there is always risk associated with any medication or treatment, the aim is always to understand any risk so that it can be handled or mitigated appropriately.

The holy grail is, then, how can we predict risk effectively? This is a huge focus of many research initiatives and is being address at many levels – drug target, molecule, patient, population. With the recent flourishing of AI/ML, we’ve seen a blossoming of models to enable risk prediction.

Pilot paper demonstrates use of NLP for adverse events to feed machine learning

There is ongoing work at the FDA to develop models that can predict adverse events (AEs) using post-market safety data, for new drugs coming on the market. Two papers published this year use a combination of AI/ML tools, including NLP, ensemble models and classification algorithms. Both papers build upon pilot work. The pilot study of six drugs demonstrated that pharmacological target AE profiles, based on marketed drugs, can be used to predict unlabelled adverse events for a new drug at the time of approval.

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Pharma
Biotech
Academic Medical Center

Finding the missing pieces of the puzzle

I think one of the most-used phrases of 2020 is “these are unprecedented times.”

I did hear another phrase recently that I liked: it is a “dark privilege” to be living through this global pandemic. No doubt, we live in an interesting time! In decades to come, people will talk about 2020 and the impact of coronavirus on so many global factors – economy, climate, travel, and of course population and individual health. As well as the dark clouds, there is of course light – and much light comes from the collaborative efforts of institutions, healthcare organizations and governments to find the best responses to this challenge, particularly in the areas of drug repurposing and vaccine development.

And this, to me, is where natural language processing-based text mining comes in. Whenever scientists, researchers and clinicians are faced with a challenge, one critical asset is identifying as much information about the problem as possible. Whatever information there is, locally and globally, that can be found, gathered and understood, will enable the right decisions to be taken. Relevant information on the biology of SARS-CoV2, from COVID-19 as a disease, patient demographics and co-morbidities, global or regional spread, and possible drugs that might treat the symptoms and impacts of this disease is all just the tip of a data iceberg. Much of this data exists in unstructured text: scientific papers, preprints, clinical trial records, adverse event reports, electronic healthcare records, even news feeds and social media can all provide information on epidemiological factors, for example.

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Pharma
Biotech

Pharmaceutical leaders, from therapeutic experts to medical affairs directors, face challenges staying abreast of the latest development and research – leading them to spend significant time searching for information, rather than driving strategy and direction across the organization. As a result, they sometimes end up making decisions without having the full picture – potentially compromising innovation opportunities or delaying a response to competitive forces.

In an ideal world, leaders should be able to focus on key initiatives for their teams and organizations, rather than spend valuable time gathering and sorting through information. They need access to information that delivers the full view of their product or therapy area landscape.

The NLP Insights Hub is the latest evolution in our NLP technology offerings, designed to help pharma professionals solve their data deluge challenges. The NLP Insights Hub provides an end-to-end offering for business users, combining core NLP to extract critical information from text, with the power of dashboards to bring the data to life and enable understanding. Bringing together the key pieces of information from a wide range of structured and unstructured data sources in one hub, with visual analytics on top, enables efficient insights development, sparks innovation and optimizes decision-making.

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Provider
Academic Medical Center
Cancer Center

Zebras -vs- Horses

Watching the development of a newborn unfold is both exciting and terrifying. As a physician and now parent of a newborn, I can say with certainty that the logical side of me struggles when diagnosing my own child. My baby wasn’t even a day old when I was already convinced that she might have Hirschsprung Disease. Later I learned the nurse had changed her diaper (during my brief nap or rather, collapse, due to pure exhaustion) and forgot to mention her intestines are indeed doing their job. As you progress you learn, as in medical school, to assume the more common problems and be aware of when you should go down the less common diagnosis route. A blocked tear duct in babies can look scary but is relatively common (1 in 25) and often clears by non-invasive methods; babies really do cry relentlessly sometimes and this is completely ‘normal’ - you learn to realize when it’s simply just the trials and tribulations of a growing child- and when it’s not. In medicine this is referred to as the Zebras -vs- Horses Phenomenon - aka look for the most common diagnosis not the most rare first. 

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Pharma
Biotech
Provider
Payer
Academic Medical Center
Cancer Center
Government
Agrochem
FMCG

We rely on obtaining as much information about a topic as possible to mitigate risk and unknowns in business and in our daily lives.  That knowledge becomes the backbone of our decision support. The trouble is, there is so much information produced daily, that in our hectic lives we can rarely go through it all, let alone sift through the information to only focus on pertinent knowledge to retain.  But are you finding the most up-to-date information? Is the information you are relying on for your decision support, years or even decades old?

Decision support straight to your inbox

Linguamatics NLP provides an Alerting capability to reduce the time required to review and provide results that are appropriate to your needs. Alerting allows you to schedule NLP search queries to be run at desired intervals, whether it is monthly, weekly, or even daily to keep up-to-date with your newest indexed information. 

This knowledge can be delivered via email to an individual or groups with the most recent and relevant information at your fingertips at all times. This broadens the benefits gained from an NLP approach – recipients of these emails can be across the organization, not just Linguamatics hands-on users.

The range and application of the alerting can be as broad as you need. You are not limited to one question but can schedule as many query alerts as you would like, to differing groups of recipients, as appropriate. This flexibility enables many different groups (e.g. different therapeutic area leads, medical affairs teams, safety assessment groups, to name but a few) to keep up-to-date.

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