Looking for a book in a library is easy. It’s simply sitting there on the shelf, organized and indexed, waiting for you to pick it up.
Looking for the same book in a haphazardly arranged stack of books is not so difficult either. Sure, you’ll have to wade through the entire stack until you find it, but eventually you will.
Now, picture yourself standing at the foot of several towers of books. They’re spread out, sprawling and twisted, not unlike the center of a bustling metropolitan business district.
The state of medical information today is a combination of these three scenarios.
There are libraries, stacks, and unfathomably large cities of information out there.
How can you find the book you need quick enough? What if you’re not even sure what kind of book you’re looking for?
The problem is acute and utterly frustrating, especially when a loved one’s health is on the line.
As is the case with other problems, modern technology can help.
There are various types of technologies that can aid a researcher in their quest to find the right bit of information that just might make the difference.
Let’s dig into three.
1. Getting information: Crawlers
A crawler is a rather simple piece of software that traverses data sources such as websites and “fetches” the data that resides there.
You may think of it as a small worker, clawing its way through internet pages, files, and anything else that contains information and storing the data that it finds in an organized manner.
Designating a small army of such crawlers to monitor a website containing medical articles, for instance, can ensure that you always have the most up-to-date articles on hand.
2. Getting insights: Natural Language Processing
Natural Language Processing (NLP) algorithms are built to read and analyze large texts written in human-readable language (e.g. medical articles written in English). These algorithms can process the text and mark things such as important words, sentences, terms, names, etc.
Using NLP to gain insights into medical articles is extremely beneficial, since it can mark all articles that discuss a certain drug, for example, discern an article’s tone (i.e. positive or negative), and automatically provide insights that the researcher would otherwise have to personally read in order to discover.
3. Learning and applying: Machine learning
Machine learning is one of the hottest current trends in computer science. By having a computer learn from its past experience, much like we do, it can then know how to provide the most relevant type of information that’s being asked for.
For example, think of a medical researcher working on a case. The researcher sifts through medical articles, dismissing irrelevant ones and marking those that he or she may use later. A computer powered by machine-learning algorithms can learn from this kind of categorization.
Think of the next researcher who gets a similar case – the computer already knows which articles were more helpful in the past, so it will now show those articles first on the research platform. It will keep on learning while more research is done. This is just one example, but this technology’s potential is enormous.
In the best case scenario, technology can help us save time and labor so we can focus on decision making. It can help us gather massive amounts of information, digest it, and even suggest how we should use it for our own benefit.
Equipped with state-of-the-art technology, in the future, medical research will be condgucted based on personalized patient parameters and will outperform human researchers in both speed and quality. Lihtning-fast and precise, the technologies will be able to learn to provide actionable insights for amateur and professional researchers alike.
This is one of the many dreams that Medint is committed to achieving.
Keeping the importance of the human touch in mind, the concept of harnessing more and more of these technologies to help our patients is what drives us forward.
We’re already on it.
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