New MeaningCloud health demo: tagging drug names, symptoms, diseases, and adverse drug reactions

Documents in the health domain show specific vocabulary and linguistic structure. If you take a look at clinical Records or Electronic Health Records (EHR), you will see that it is also made up of unstructured data (that is, free text). This free text contains weird names of drugs and diseases that are even difficult to read. For all these reasons, text analytics techniques must be adapted to the health domain.

We have put together a number of resources in a demo that shows how MeaningCloud can tag drug names, symptoms, diseases, procedures, and so on.

See the free demo: https://www.meaningcloud.com/health-demo

Health text tagging demo picture

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What is Real World Evidence and why does it matter?

Real World Evidence. Blurred image of a hospital

Real World Evidence (AKA “Real World Data”) is a worldwide trend in Health and Life Sciences. New kinds of data, such as electronic health records and data mining tools are now available and allow us to extract information and knowledge. We can detect medical treatment costs, treatment efficiency (cost, benefits, and risks), references to drugs, side effects, or long-term results.  Text analytics is an essential component of this area of knowledge.

Austerity measures and related price cuts have put unprecedented pressure on the pharmaceutical industry. Manufacturers are being asked to provide information related not only to safety, appropriate use, and effectiveness but also to clinical and economic value. Although randomized clinical trials (RCTs) remain the gold standard of clinical tests, factors such as varying responses to a drug in real life, not completing the course of prescriptions, or using unauthorized medication before or during the trial limit the generalizability of results from randomized clinical trials.
Real World Evidence (also called “Real World Data”)  has been fueled by new data technologies that leverage the valuable information contained in electronic medical records and personal information repositories. This post is a review of those Real World Evidence sources and of the benefits that Pharmaceutical and Life Science companies can derive from them.

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The amazing deeds of text analytics superheroes

In the last few years, the explosion of user-generated content in social media (networks, forums, communities, etc.) has significantly increased the need to extract information from unstructured content. Oddly enough, text analytics superheroes, wondrous as their achievements are, are just average guys who figured out what they could do with the right technology.

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RapidMiner: Impact of topics on the sentiment of textual product reviews

This is the second of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. Read the first one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this tutorial we shall attempt to extract a rule set that will predict the positivity/negativity of a review based on MeaningCloud’s topics extraction feature as well as sentiment analysis.

To be more specific, we will try to give an answer to the following question:

  • Which topics have the most impact in a customer review and how do they affect the sentiment of the review that the user has provided?

For this purpose, we will use a dataset of food reviews that comes from Amazon. The dataset can be found here.

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Recorded webinar: Integrate the most advanced text analytics into your predictive models

Last April 27th we delivered our webinar “Integrate the most advanced text analytics into your predictive models”, where we presented our new MeaningCloud Extension for RapidMiner. Thank you all for your interest.

During the session we covered these items:

  • Analytics platforms. Introduction to RapidMiner.
  • Text analytics. Introduction to MeaningCloud.
  • Combining text and data analytics. MeaningCloud Extension for RapidMiner.
  • Practical case demo.
  • Application scenarios.
  • How this Extension is different.
  • Product roadmap.

IMPORTANT: The data analyzed during the webinar can be found in this tutorial, along  with the applied RapidMiner workflows and models.

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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RapidMiner: Relationship between product scores and text review sentiment

This is the first of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. See the second one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this tutorial we shall analyze a set of food reviews from Amazon. We will use the MeaningCloud sentiment API and try to see how users score products and whether their review description of a certain product corresponds to the score that they have assigned – more specifically we will try to see

  • How closely the review sentiment corresponds to the manually assigned score (which we already have available in our dataset).

The dataset that we will be using throughout the tutorial can be found here. First thing we need to do is download the CSV to our computer.

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You can now use MeaningCloud with RapidMiner

Expand text analytics with the tools to create the most sophisticated predictive models

At MeaningCloud, we have just launched a feature that enables users to incorporate our text analytics into complex predictive models based on structured data. With our new Extension for RapidMiner you can directly embed our semantic analysis engines into the process pipelines defined in this popular analytical tool.

RapidMiner is an open-source platform for data science, recognized as a leader in the field of advanced analytics tools. RapidMiner is used for preparing data, creating predictive models, validating them, and embedding them into business processes quickly and easily .

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