Machine Learning-A New Paradigm Shift
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Machine Learning-A New Paradigm Shift

Kent Sorensen, CEO, DITA Exchange
Kent Sorensen, CEO, DITA Exchange

Kent Sorensen, CEO, DITA Exchange

Machine learning is finally making its way into mainstream conversations. On the surface, it sounds like the development of the artificial intelligence that we have been warned of in science fiction movies. Today, however, its applications are actually much closer to function than fiction. Machine learning brings us much of the tailored content, which we consume every day, ingraining itself in our daily routines whether we realize it or not.

“Software solutions can be built to identify and tag elements within structured content to include in data records”

Machine learning is used in a range of technology, delivering basic actions like effective web searches or personalized Facebook news feeds to more advanced implementations, like self-driving cars. But what does it actually mean to organizations that are seeking to deliver information and solutions to consumers in a world overloaded with information? Both software developers and consumers need to tap into machine learning to reach our intended audiences faster, with better quality and less effort.


In fast-paced industries dealing with large amounts of information, completing every task by hand is quickly becoming extinct. Machines can help companies in these types of industries enrich their information by learning how to quickly perform high-volume and repetitive tasks, saving time and resources.

In pharmaceuticals for example, the European Medicines Agency’s (EMA) Policy 70 requires that companies submitting clinical data for scientific review must also submit a version with redacted information. That redacted version must obscure any commercially confidential information and personal data. Currently, this process is completed manually. Because there can be such large documents, going through and redacting all the right information, which is time-consuming and can create a high risk of error.

With machine learning, the process could be much simpler. Machines can be taught to identify and suggest redactions and embed metadata tags for those suggestions in original source documents. These enriched documents can then be used for multiple purposes, like submitting information to a health care authority or releasing study outcomes to the public.


Many organizations create large documents in a somewhat ‘narrative’ or unstructuredway. In that form, the information is essentially “trapped” within the document making it difficult to use in other ways. In a regulated industry like pharmaceuticals, where there is always new information to share and standards to adhere to, there’s a distinct need to turn that unstructured information into data that can be extracted and maintained.

In another example from the life sciences industry, healthcare authorities are in the process of implementing the standards developed by the International Organization for Standardization (ISO) for the identification of medicinal products (IDMP). These are a set of common global standards for data elements, formats and terminologies for the unique identification and exchange of information on medicines. Following a phased implementation process, pharmaceutical companies will be required to submit data on medicines to the EMA—and eventually others—in accordance with these standards.

Unfortunately, much of that data is captured in documents that cannot be altered. With looming compliance deadlines for structured electronic submissions, companies need a way to extract the necessary data to plug into their databases. That’s where machine learning can come in. Software solutions can be built to identify and tag elements within structured content to include in data records. Not only can this newly tagged data be extracted from the content, it can also be actively linked to the data record for synchronicity.


The goal of any piece of content should be readability. An audience may not always have a full understanding of the topic, so the readers won’t get the grasp on the material that the author intended.

For instance, Simplified Technical English (STE), a controlled language to help users understand maintenance documentation, was first applicable to commercial aviation but is now a requirement for defense projects, like land and sea vehicles. Because of this, authors of maintenance manuals must memorize and write in this language, and the task is complicated.

Training a machine to recognize complex phrases and terminology can be compared to the rules outlined by STE and allow authors to improve the overall quality and readability of these documents. In an industry like aviation, no one wants their pilot to decipher complicated texts to troubleshoot a problem. Machine learning enables authors to ensure their content is in its most understandable form in order to serve its readers in a better way.

Industries beyond aviation and pharmaceuticals are finding new ways for machine learning to help them succeed. Virtually all industries need to connect data to action, and implementing machine learning is the most efficient way to do so. From software companies developing code, to organizations implementing solutions, businesses worldwide need to understand how machine learning can help them achieve their goals. The visionaries that embrace this modern technology to realize gains in efficiency and quality will be hard to beat.

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