Could Machine Learning be used to enhance, or even better, improve the success of a clinical trial? Gareth Baxendale FBCS CITP, Head of Technology for the NIHR Clinical Research Network and Vice Chair of the BCS Health and Care Executive, explores the opportunities offered by ML.

There is much excitement around machine learning and the opportunities it can provide. It’s already beginning to permeate every area of our digital life. In fact, you’re likely to make use of machine learning almost every day without even knowing it. For example, Amazon’s Echo and Apple’s Siri use machine learning for speech recognition while Google’s image search uses machine learning to ‘understand’ the components that make up a picture. It does a pretty good job too, spawning the meme ‘Chihuahua or blueberry muffin?’. Feel free to ‘Google’ it.

Machine learning is a branch of the more commonly understood field of artificial intelligence, the preserve of many Hollywood ‘rise-of-the-machines’ dystopian movie story lines. In short artificial intelligence attempts to mimic human intelligence or behaviours while machine learning attempts to analyse, map and associate 'patterns' and 'behaviours' in multiple data sets to support intelligent, data-driven decision making, based on ‘new’ knowledge and understanding.

It’s this ‘new’ knowledge that is the exciting part. This knowledge can be used to predict, for example, a patient's diagnosis, course of treatment or even their level of risk with the added advantage of potentially reducing human error. Given the obvious benefits, it’s well worth exploring the opportunities machine learning has to offer.

The clinical trial

At its heart, a clinical trial is a set of questions that need answering to determine the efficacy and safety of a particular biomedical, pharmaceutical or behavioural intervention. Some trials are focussed on developing new treatments, some consider new combinations, others apply existing treatments in a different therapy area and many undertake comprehensive reviews of existing treatments considering their longer term efficacy and safety.

Significant amounts of data will be collected during a trial in order to provide robust and reliable answers to the questions posed. And herein lies the first opportunity for the application of machine learning.

Trial data

During a clinical trial various data sets will be generated and collected by the investigator and their study staff using case report forms, or by the patients themselves by filling in a questionnaire, maintaining a diary or using a custom app. This data could include medical reports from ECG, MRI or blood results. Machine learning can be applied to this data to surface ‘new’ information that otherwise may not be found.

Take for example Berg Health who are using a machine learning platform they've named ‘Interrogative Biology’, which allows them to identify biomarkers for drug discovery and monitor patient responses during a clinical trial. They state, ‘We can build models with the platform using the patient's own biology in order to stratify the population by response to the trial drug as well as monitor patient response over time at a biological level, which may lead to more successful trials.’

Likewise in the area of cancer trials, are taking advantage of machine learning to analyse thousands of images which, it is hoped, can then be used to identify or predict early signs of cancer and potentially personalise a course of treatment for the patient.

Recently the NIHR School for Primary Care Research carried out a study that used electronic medical records from 378,000 patients in general practices across England, taken from the Clinical Practice Research Datalink (CPRD). Data on key risk factors, such as smoking status and blood pressure, were used to develop and test four different machine learning algorithms for predicting cardiovascular risk.

The report suggests that ‘these algorithms were better than existing medical risk models at both predicting the number of people who would develop cardiovascular disease and excluding people who would not get heart problems.’


Another key area for clinical trials is recruitment and the identification of suitable and willing patients to participate and complete the trial.

Cincinnati Children's Hospital Medical Centre are using machine learning to understand why people accept or decline an invitation to participate in a clinical trial. Recruiting sufficient numbers of participants to answer the research question is a challenge in medical research. In their study, 60 per cent of patients approached with traditional recruitment methods agreed to participate.

Researchers are predicting that their new automated algorithm could help push acceptance levels up to about 72 per cent.


When a clinical trial is completed the outcomes of the trial are published. Even in this final activity machine learning may be able to help.

King’s College is running a machine learning project called ‘Robot Reviewer’ funded by the Medical Research Council (MRC) and others, to develop a system that will automate bias assessment in systematic reviews in support of both evidence-based medicine and clinical trials. These syntheses will enable decision makers to consider the entirety of the relevant published evidence.

The future

It will likely be some time before humans would take an ‘unsupervised’ approach to decision making based on machine learning. However, it's almost certain that in the near future all medical diagnostics, monitoring and treatment plans will, in-part, come from knowledge derived from and recommended by machine learning platforms. I'm also confident that it has very real and tangible applications for clinical trials which can only be a good thing for health research and patients the world over.