MeaningCloud Release: new Language Identification API and more

As we recently advanced, during these last few months we have been working on new functionality. We are planning to start releasing it over the next few months.

In the latest release of MeaningCloud we have included some of this functionality:

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Voice of the Patient Analysis in Forums

Voice of the Patient Analysis

In a previous post (Listening to the Voice of the Patient), we made an account of ongoing initiatives by public administration, hospitals and pharma companies intended to listen, understand and engage patients in the whole healthcare system. We also provided evidence that forums are a paramount source of information when talking about the Voice of the Patient Analysis.

As it was already shown, the Voice of the Patient (VoP) can be analyzed from multiple perspectives. In this post, we cover the point of view of the Pharma Industry.

Pharma Market Intelligence - Voice of the Patient

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MeaningCloud sponsors prize for Author Profiling Research

Author Profiling ResearchCLEF Initiative and Conference

MeaningCloud sponsors the prize to the best team at the 5th International Competition on Author Profiling Research, PAN@CLEF 2017. This competition is part of PAN (Plagiarism, Authorship and Social Software Misuse), a series of scientific events and shared tasks on digital text forensics. The 17th evaluation lab on digital text forensics will be held as part of the CLEF conference in Dublin, Ireland, on September 11-14, 2017.

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Surveys and HR: why do you need open-ended questions?

open and closed questions

Since everyone wants to understand employees better, text-based data sources are a key factor for any organization to understand the “whys” and act on them to make improvements. Open-ended questions are one of the most effective ways to gather employee opinions; they offer them an open forum to make suggestions and present innovative ideas.

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Listening to the Voice of the Patient

Voice of the Patient and Patient-Centered Health Care

National Library of Medicine

The modern definition of “patient centered health care” was stated in the National Library of Medicine’s MED-LINE subject heading (MeSH), introduced in 1995, which reads, “Design of patient care wherein institutional resources and personnel are organized around patients rather than around specialized departments.”

Following this design criterion, patients’ safety and well-being are the priority for all the agents involved in this industry: caregivers, pharmaceutical companies, medical device manufacturers, health insurers, and government agencies. And, being the center of our health systems, listening and engaging patients becomes the cornerstone of any quality improvement initiative. That’s why the so called “Voice of the Patient” is getting an increasing attention by all the stakeholders involved.

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Recorded webinar: Why You Need Deep Semantic Analytics

Last July 13th we delivered our webinar “Why You Need Deep Semantic Analytics”, where we explained how to achieve a deep, automatic understanding of complex documents. Thank you all for your interest.

During the session we covered these items:

  • Automatic understanding of unstructured documents.
  • What is Deep Semantic Analytics? Comparison with conventional text analytics.
  • Where it can be applied.
  • Case study: due diligence process.
  • Ideal features of a Deep Semantic Analytics solution.
  • MeaningCloud Roadmap in Deep Semantic Analytics.

IMPORTANT: you can find a more literary explanation of some of the items we covered, including the due diligence practical case, in this article.

Interested? Here you have the presentation and the recording of the webinar.

(También presentamos este webinar en español. Tenéis la grabación aquí.)
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Deep Semantic Analytics: A Case Study

Scenarios that can benefit from unstructured content analysis are becoming more and more frequent: from industry or company news to processing contracts or medical records. However, as we know, this content does not lend itself to automatic analysis.

Text analytics has come to meet this need, providing powerful tools that allow us to discover topics, mentions, polarity, etc. in free-form text. This ability has made it possible to achieve an initial level of automatic understanding and analysis of unstructured documents, which has empowered a generation of context-sensitive semantic applications in areas such as Voice of the Customer analysis or knowledge management.

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Real World Evidence Definition

Real World Evidence Definition

Real World Evidence is information on health care that is derived from multiple sources outside typical clinical research settings, including electronic health records (EHRs), claims and billing data, product and disease registries, and data gathered through personal devices and health applications.

The most quoted definition of Real World Data comes from the area of pharmacoeconomics. The ISPOR (International Society for Pharmacoeconomics and Outcomes Research) defines Real World Data as:

“Data used for decision making that are not collected in conventional randomized controlled trials (RCTs)”

Other definitions of Real World Data, Real World Evidence and Evidence from Clinical Experience can be found in the following figure, taken from a working paper of the Margolis Center for Health Policy, Duke University (see References below).

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MeaningCloud Sponsors the Real World Evidence Forum 2017

Real World Evidence Forum

Real-World Evidence Forum

Real-World Evidence Forum Philadelphia, July 17-18

At MeaningCloud, we are proud to sponsor the Real World Evidence Forum. The RWE Forum, taking place on July 17-18, 2017 in Philadelphia, will bring together clinical health professionals to address:

  • How to operationalize the process of collecting real-world data.
  • How to utilize real-world evidence to demonstrate both the clinical effectiveness and cost-effectiveness of drugs.

Attendees will gain a better understanding of how electronic data sources are changing the way real-world data is being collected. This conference will offer attendees insight into how real-world evidence will help decrease costs, define innovative outcomes and minimize the number of patients exposed to potentially harmful medications.

Text Analytics and Real World Evidence

MeaningCloud, as a Text Analytics provider, has evolved a highly specialized offering for the Health and Pharma industries. We count among our clients some the largest companies in the Pharmaceutical industry.

Join us in Philadelphia. If you are interested in attending the Real World Evidence Forum next July 17-18, just drop us a line to info@meaningcloud.com. We have a surprise for you!

Stay tuned to access our presentation at the conference, that we will publish on this blog. In the meanwhile, if you are curious about how our technology works in the health area, just take a look at our Text Analytics Health Demo.

Looking forward to seeing you at the Real-World Evidence Forum!

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Why you need Deep Semantic Analytics (webinar)

Achieve a deep, automated understanding of complex documents

Conventional Text Analytics enable a first level of automatic understanding of unstructured content, achieved through its ability to extract mentions of entities and concepts, assign general categories or identify the polarity of opinions and facts that appear in the text. However, these isolated information elements do not reflect the wealth of information provided by these documents and impose limitations when it comes to finding, relating or analyzing them automatically.

Deep Semantic Analytics represents a step beyond conventional text analytics by providing features such as snippet-level granular categorization, detection of complex patterns, and extraction of semantic relationships between information elements in the document.

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