How machine learning can help pharma and healthcare

Gishan, our Senior ML Consultant, helps our clients to make smarter, more effective digital solutions. Using machine learning to provide users with better service and better outcomes.

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Here are some of his thoughts on how machine learning can help Digital Pharma, MedTech, and healthcare go further…

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Machine learning is the next step

Machine learning is now the next step in our technology evolution. Many mobile apps now have ML embedded, from retail apps to social media apps. It used to be that machine learning was used as an addition, something that could be added in later, to add gains if and where possible, but now more and more apps are being designed with machine learning in mind, right from the start.

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This is the impact machine learning is having on today’s software design and consultancy, and is what we can offer to any of our clients, within digital health and beyond.

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Machine learning for better clinical trials

However, it’s not just mobile apps that benefit from machine learning; healthcare does too. Within pharma and clinical trials we have a great deal of experience in supporting clients with their digital solutions, for example we partnered with Exco inTouch (ERT) to develop Gather eCOA, an industry-changing clinical trial platform.

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Machine learning can also play a hugely beneficial role within pharma, the development of drugs, and within clinical trials. In that ML could be used to predict the likely effects of a new drug, on a certain population, or individual, and thereby help to make clinical trials safer, or indeed reduce the number of participants needed.

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Clinical trials take a great deal of work, time, and money, and there is a level of risk involved in any human-based trial. Therefore if machine learning is provided with the DNA data (and/or any other relevant health data) of a sample, the model could then be trained to see what affects a new drug could potentially have on a population or on specific individuals.  

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It is therefore possible to augment a clinical trial, to be more automated, and to use it to replace a number of human participants. Or to support trials to be more informed.

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Machine learning can save lives

Another area that machine learning could help with is within Electronic Health Records (EHRs) within the UK. The health records held by GPs and the NHS for every patient in the country.

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Machine learning can be used to detect patterns within health records pertaining to a particular health condition, and then suggest if certain individuals will face the issue, and when.

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For example, machine learning could be used to explore health records for data about heart attacks, and the cholesterol levels that related to every case. In order to build a model to suggest the likelihood of a heart attack, at a certain age, in any individual, based on their cholesterol level (and any other related key data).

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Therefore machine learning can be predictive and forecast future threats, which would help doctors to give greater clarity to patients, and support, in order for both parties to make more informed decisions on how to move forward. This in turn could help patients to make healthier choices that will help them to live better, longer lives.

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Better accuracy, less misdiagnosis  

Another way machine learning can save lives is by being used for scans and X-rays, which is now already quite well established. For example, machine learning can be used to spot issues that need to be spotted within an MRI scan. This is done by training machine learning with historical sets of MRI scans to learn to detect particular issues within newly presented scans.

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Furthermore, machine learning can provide better accuracy within scans. For example, in a scan of the stomach, using historical data and machine learning, it may be possible to provide more accuracy within any issue found, to provide a better understanding of the situation.

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A further benefit of this, conversely, is that machine learning helps to avoid misdiagnosis, which is a serious issue within healthcare. Symptoms can be unclear or confusing or overlap, and in certain situations there can be a high-level chance of misdiagnosis. So in these situations machine learning can offer vital technological support to avoid mistakes that could lead to incorrect treatment (and the physical as well as emotional detriment of a patient).  

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Machine learning for digital health

As stated above, machine learning can be used for mobile apps to help them perform better for individual users. We are currently using machine learning to help people manage their emotions and boost their productivity, for one of our client partners, Talk It Out.

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We’re here to help healthcare go further

We’re really excited about how ML can be used across the healthcare spectrum, and look forward to working on new R&D projects with our partners to use machine learning to its full potential. To help clients offer better services, to improve patient outcomes, and to help save lives.

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If you’d like to know more about our capabilities and projects within machine learning, or want to tell us about a trial or project you might like support with, then please email us at hello@nuom.co.uk

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