Text mining uses sophisticated natural language processing (NLP) techniques to quickly analyze massive volumes of biomedical literature. It can transform your organization’s approach across the drug development pipeline – from early phase drug discovery and clinical trial development to pharmacovigilance.
Imagine being able to give your laboratory scientists a head start by extracting candidate relationships between whole classes of concepts like genes and diseases from your organization’s existing information resources, at scale and with confidence. Or envision supporting your pharmacovigilance with incredibly precise NLP search strategies, vastly reducing time spent on false positives and focusing their efforts on tracking down meaningful issues. These and more are the promise of a text mining initiative done right.
But before you begin, here are few things your team needs to understand about text mining:
Text Mining goes Deeper than Simple search
Text mining does not just identify specific topics within documents. It uses intelligent algorithms that can pinpoint topics in relation to other sections of text. The really useful part about this is that you can discover new relevant data to go with your preexisting topics. So, the algorithm doesn’t just spit out topics that you identify, but it also identifies new information based on the relationships between text segments within the document. So if you want to analyze for text relative to “ice cream” it might come up with anything from favorite flavors to vegan alternatives, and even more that you might never think to associate. You can then categorize, analyze, and draw specific insights from this data with ease.
Text mining relies on data that’s ready to be mined
Across the life sciences / pharmaceutical industry, information is often siloed and stored in multiple varying formats, reducing its usefulness. Structured or semi-structured content may be in multiple schemas, requiring additional data cleansing and normalization work. Scientific literature may be licensed for only certain uses, and subscription agreements typically do not include permission to conduct text mining activities. Each of these is an obstacle to realizing benefits from your organization’s existing content investments and its text mining efforts.
A strong relationship between bioinformaticians and information managers is key
Collaboration between informatics and information management teams can overcome these challenges. Information managers understand how scientific literature and other resources are consumed within the organization. They also manage external publisher relationships – ensuring a fit between licensed content and the information needs of the organization. Bioinformaticians and other informatics professionals understand data interoperability and the information architecture required to practically apply text mining to solve organizational problems.
Text Mining has a multitude of use cases
Data analytics give you the power to draw associations between unexpected topics. What kind of circumstances would call for this sort of data association? Well, think about your brand. How do your consumers really see you? Boolean searches on social media data will give you details on topics you’re already aware of, but what else are consumers talking about? Text mining can identify relationships between your brand and unexpected strings of text. These strings will help to identify topics that you had not heard customers mention yet. They’re not obvious, but for that reason they’re often overlooked when considering a brand image.
This applies to a number of areas and industries. Doctors might use text mining software to analyze medical documents and draw associations between symptoms to identify diseases. Email services use text mining to identify spam emails based on wording, just as search engines use its concepts to rank websites by relevance to your search terms.
WHAT KIND OF DATA IS USED?
This is not personal private data that is being analyzed. The text comes from documents such as product listings, blog comment sections, forums, and social media profiles. The data was made public by the posters because they actually want their opinions heard on a topic. The only trouble is, there are so many posts across numerous platforms, and it’s hard to keep up. That’s why it takes mining algorithms to extract the information.
To ignore this data would be to ignore vital feedback for various topics, whether related to travel destinations, celebrity gossip, or academic forums. From public relations to brand management to product development, the data gleaned from these posts provide valuable insights into the minds of consumers.
Have a clear goal in mind – but be realistic about instant benefits
Like most organizational efforts, a text mining initiative is more likely to succeed if stakeholders agree on what success looks like at the outset. Identify an appropriate use case by looking for situations where internal teams struggle to synthesize findings from large amounts of data, have difficulty staying on top of current findings, or suffer from low signal-to-noise ratios in their information resources.
Even with the right use case, keep your expectations reasonable. It takes time and effort to source a proper text mining solution, conduct proofs of concept, evangelize internally, and ultimately scale your efforts across the organization. While you and your team might recognize this, your stakeholders need to share a similar understanding.
By working together on text mining programs, bioinformaticians and information managers can deliver real insights to the organization from relevant data, leading to enhanced drug discovery, more efficient literature monitoring, and fewer information dead ends.