Machine Learning in Clinical Research

by Maria Birger

The task of conducting clinical research is not an easy one. No doubt, research is a vital part of the medical process. However, the fact remains that clinical trials can be altogether time-consuming, expensive, and incredibly challenging to organize. Too often, enormous amounts of work and resources will get invested in projects that turn up with bust results.

Although the need for clinical trials is inarguable, there is no reason to submit to these headache-causing work rituals. There have been many pushes within the community to incorporate the best possible tools on the market to ease the process of clinical research.

One such tool is Machine Learning.

What is Machine Learning?

For those of you who don't know, and for those of you who need a reminder.

The terms artificial intelligence (AI) and machine learning are gaining traction in nearly every field, including those related to business, education, and even entertainment. The truth of the matter is, modern AI applications do not operate quite like the science fiction movies imagine them to. Most AI and machine learning experienced today are a lot more subtle than a robot servant or even a self-driving car. These applications are about saving time, saving money, and optimizing efficiency. Most importantly, they are 100% applicable to the clinical research process.

So, what exactly is AI, and what is machine learning? AI is the subsection of computer science that is inspired by the mechanisms of the human brain. To put it simply, AI is how programmers enable computers to think and even make independent decisions. Meanwhile, machine learning is a subsection of AI focusing on the training element for the machine to start working as it is meant to. This process resembles how a parent might teach their child, as opposed to the traditional computer programming approach.

Machine Learning Types

The possibilities are endless, and they all apply to clinical research. There are many forms of machine learning, including supervised, and unsupervised.

Supervised learning

Supervised learning requires a human trainer for reinforcement, and often includes the classification of objects. This type of learning is about engaging with a new piece of data and figuring out which label it most resembles. A programmer is responsible for reinforcing the machine by telling it if it got the label right or not. Machines can get trained to classify a vast array of subjects with incredible accuracy. In the medical field, this may be seen as a computer reads the results of an examination to diagnose a patient's illness.

Unsupervised learning

On the other hand, unsupervised learning is about finding patterns in data sets and does not heavily rely on human interference. In this case, a computer can take on an extensive data set of seemingly similar data points, and find critical differences to group them into clusters. In medicine, unsupervised learning may be used to differentiate different diseases that may appear similar because of symptoms.

How does machine learning affect medical research?

Explore the modern approach to clinical trials.

Recently, there has been a push from the FDA to incorporate advanced technology in clinical research. A legislative movement was signed into law in 2016, called the 21st Century Cures Act. This act focuses on advancing the production of medical products and approaching the process of clinical trials with a modern perspective to incorporate new technologies.

The reasoning is obvious. Advanced technology advances research, which in turn advances the medical and pharmaceutical community as a whole. There's no doubt that AI and machine learning are included in this push forward as these tools offer great promise in accomplishing the proposed goals. Harvard Data Science review says that AI and machine learning can help accelerate the trial and approval process, save money, minimize failures, and create more funding for future projects.

1. Tackle Big Data Sets

The clinical process is a hard and meticulous one. The implementation of AI can make the process easier by tackling more massive data sets and making breakthroughs quicker. Doing so will ease the process and pave the way for the development of treatment plans.

2. Cover Subject Recruitment

Machine learning tactics can be incorporated throughout the study, starting with subject recruitment, which is often considered one of the more time-consuming parts of the process. With the assistance of AI, large sets of data can be speedily combed through to find suitable patients for clinical trials. There will be less need to rely on doctor's offices for recruitment. With proper consent, AI may even be used to access social media as a pool for available subjects of clinical research. With more options for patients in the trials, studies can become more targeted in their criteria for participants in areas including genetics, gender, and many others. Tactics such as these offer tremendous promise for collecting diverse and relevant samples for clinical research.

3. Monitor Patient Compliance

AI and machine learning may also be incorporated in terms of monitoring patient compliance. If a participant must take a medicine at a specific time of day, then AI can record each time the patient complies with the protocol. Similarly, if a participant forgets to take medication, AI may also be used to remind the participant to take it. Reading this, you may think that it's easier said than done, but there is no doubt that technology will get to this point before you know it, making it the new normal. For example, picture a "smart pill dispenser," which can count the number of pills left in a container.

4. Predict Outcomes

Some of the most significant promises in this area of clinical research tools are in the predictive abilities of machine learning. There is promising research that incorporates this strategy in predicting the effectiveness of treatments, the course of disease development, outcomes of a diagnosis, and even which drugs will be most likely to pass through clinical trials.

5. Increase Organization

Another considerable benefit of incorporating machine learning into the trial process is that it can significantly help with the organization of research and findings. It starts with an idea as simple as storing all of the collected data online. With machine learning, these giant electronic data sets can flow from one trail to the next in a manner that is neat and straight forward.

6. Speed Up & Save Money

Overall, the implementation of AI and machine learning in clinical trials is about accelerating the process's speed. Machine learning uses up fewer resources and decreases the astronomical costs. Machine learning is about getting treatments through trials onto the market faster.

7. Save Time

Many menial tasks in the research process take up the valuable time of human researchers. In many ways incorporating machine learning can save significant time on menial tasks like in the process of subject recruitment described above. In many cases, modern AI can cover a majority of tasks on its own with the researchers' slightest supervision. Most of the mundane day-to-day tasks can become automated, allowing researchers to focus on the more essential elements.

Are you thinking of applying machine learning to your projects?

Where should you begin?

At this point, hopefully, the argument supporting the benefits of AI and machine learning is very convincing. However, an individual new to these tools may become overwhelmed thinking about how to jumpstart the process.

Certain aspects of this process are a given. Data sets are necessary, but most labs and companies are already sitting on a wealth of useful data. On the other hand, the actual application will be more specific, depending on the project at hand. Machine learning can help find patterns between data points, recognize patterns, and so much more, bringing labs closer to developing new drugs and treatment plans.

You don't have to configure it all on your own. Call Tizbi for a free consultation, and jumpstart your next ML project.

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