Our established large annual conferences, such as the annual Text Mining Summit (U.S.) and Spring Text Mining Conference (Europe), are the Linguamatics annual showpiece events, but attendees also find our smaller, local events very appealing, as they offer a different, but highly valuable, experience. These seminars, for both existing and future clients, provide a variety of presentations, end user case studies and plenty of opportunities to network and collaborate with other organizations and NLP experts.
Linguamatics recognized in inaugural KLAS report NLP 2016
(Cambridge, UK and Boston, USA – June 20, 2016) Linguamatics is pleased to be recognized in the first report on Natural Language Processing (NLP) by highly-respected, independent healthcare IT analysts KLAS. The report, titled “Natural Language Processing – Glimpses into the future of unstructured data mining”, will be required reading for many healthcare professionals and serves as further evidence of the growing importance of NLP within the healthcare sector.
Linguamatics are highlighted as having KLAS-validated live use in the Research and Population Health category for at-risk patients and risk adjustment, in the Coding and Documentation category for patient summary and reconciliation, and validated sales consideration in the Research Cohort ID/Clinical Trials use case.
Relevant quotes concerning Linguamatics in the KLAS report include:
There’s a Clay Shirky quote I like, that focuses on the importance of communication: “When we change the way we communicate, we change society”. There are so many different ways of communicating these days, with web meetings, calls, email, skype, twitter, and more. Indeed, global communication has never been easier, and making connections with colleagues, collaborators and customers across the world can be carried out from wherever we find ourselves, from our desks, coffee shops, airport lounges or even remote resorts. While “thinking global” is often almost taken for granted these days, it’s important to keep in mind the power of local networks and face-to-face human interaction.
At Linguamatics, we are fortunate to be based within one of Europe’s key technology clusters. The Cambridge technology cluster includes many different networks engaged in research and development in vital areas including genomics, personalized medicine, rare diseases, big data technologies, and more.
Linguamatics has recently become affiliated with the Milner Therapeutics Consortium, a new network dedicated to the conversion of basic science into therapies. Its mission is to accelerate academic research towards medical advancement by forging close collaborative interactions with industry.
Shifting payment models based on quality and value are fueling the demand for insights into the health of populations. This demand requires the analysis of vast amounts of patient data. For example, before healthcare organizations can implement pre-emptive care programs, they must first identify the relative risk of their patient population. This is based on a variety of clinical, financial, and lifestyle factors, including:
- Problem list of patients, especially chronic conditions
- Procedures, medications and other hospital data
- Claims information
- Risk factors such as tobacco, alcohol and drug use
- Availability and accessibility of health services and social support.
As illustrated in Figure 1, a healthcare population typically includes a relatively small percentage of the highest-risk patients, though these least healthy patients usually account for the biggest percentage of overall healthcare costs.
Figure 1: Level of patient risk associated with population segments and
their cost implications; a relatively small segment of the population
accounts for a disproportionate percentage of healthcare costs
With new and exciting technologies, it often happens that one particular application or use case leads the way initially… and then, when the euphoria turns into commercial reality, people start looking at other applications where the new technology can also bring value. In text mining, the same holds true. Pharma companies have now been using NLP text mining technologies for many years, in areas such as target validation, gene-disease associations, clinical trial optimization, and patent analytics, for example. As they become comfortable and, indeed, expert in these areas, attention has turned to areas where the core technology needs to be adapted or tweaked to meet a specific requirement.
For example, when looking to apply NLP to the time-consuming and costly business of discovering new, novel compounds, users hit a significant issue; trying to understand every single component part of some of the long chemical names. Not an insurmountable problem, but one that needed time, expertise and determination.