Posts from June 2020

Using NLP workflows to identify key social determinants of health

A year has gone by since I last blogged about the importance of social determinants of health (SDoH). Since then so much has happened in our lives. The COVID-19 pandemic has affected us all, in ways we could never imagine and it will continue to do so into the future.  When you review the SDoH risk factors, you realize that, due to the pandemic, responsible citizens are now enforcing one of these risk factors for safety reasons: social isolation.  It wasn’t too long ago that social isolation was branded a warning sign for mental health issues - not a recommendation for maintaining physical health.  


How research at Kaiser Permanente found that NLP can assist in identifying hospitalizations for worsening heart failure

Heart health is nothing to be taken lightly.  A healthy functioning heart is a key factor in providing every part of your body the blood it needs to provide nutrients and oxygen and remove the waste necessary for life. If there is presence of disease there are times when the heart decompensates, meaning that the heart is no longer able to maintain an efficient circulation allowing for this imperative exchange amongst tissues. According to the Center for Disease Control, in 2017 approximately 6.5 million adults in the United States had heart failure, and it was a major factor of mortality in 1 of 8 deaths. In 2017 it was reported that average wait times for a replacement heart in the U.S. is 191 days, and the cost can approach 1.4 million dollars. So, it is of no surprise that it is better to take care of the heart you have and imperative to capture decompensation as early as possible. Unfortunately, sometimes decompensation can be missed.

NLP workflows in contrast to manual abstraction

Imperative information is often hidden within free text and reports that are attachments within the Electronic Health Record (EHR). A myriad of key cardiology measures can be abstracted from EHRs, be it text based notes to test reports that are PDF attachments, for example: ejection fraction measurement; symptoms such as shortness of breath, fatigue and palpitations; New York Heart Association classifications, and B-type natriuretic peptide (BNP).


Early Scientific Intelligence pipeline gives 360 degree view of “novelty” in diabetes and obesity

We hear a lot these days about evidence-based decision making. Particularly in the current climate, it’s critical that governments, businesses, health organisations and individuals are guided by facts and evidence, not fiction and hearsay. But what do we really mean by evidence-based decision making? Ideally, before making any decision, you want to be able to gather all relevant information, synthesized from different relevant sources. This approach allows you to see the overall picture, drill down to details, understand and weigh up the evidence and therefore make the best decision possible.

Creating a hub of evidence

Getting a comprehensive view of the whole picture is something Linguamatics pharma and healthcare customers need for their decision making in many different arenas - and that often means being able to integrate information from unstructured textual data streams together with data from structured sources. Capturing and integrating the information from a range of document sources can build a landscape of knowledge, a “hub” of evidence. Evidence hubs can be developed for discovery, development, regulatory affairs, safety, patient risk; with input data sources and output dashboards or alerts tailored as needed.

Novo Nordisk are using this integrated approach for an “Early Scientific Intelligence” evidence hub. Sten Christensen & Brian Schurmann (Novo Nordisk) presented on this innovative project at our virtual NLP summit on Thursday 4th June 2020.