New Research Finds Artificial Intelligence Can Predict Premature Death
A University of Nottingham study says Artificial Intelligence (AI) and Machine Learning (ML) can predict premature death, a capability that could revolutionize preventative healthcare.
In a study of over half a million people between the ages of 40 and 69, Nottingham’s team of healthcare data scientists and doctors have developed and tested their system of computer-based ML algorithms to predict the risk of early death due to chronic disease. And they say it works.
According to the team, they found their AI system was not only “very accurate in its predictions,” but that it “performed better than the current standard approach to prediction developed by human experts.” The studywas published by PLOS ONE in a special collections edition of “Machine Learning in Health and Biomedicine.”
In their study, Nottingham researchers used health data collected from people recruited to the UK Biobank between 2006 and 2010 and followed up until 2016. The UK Biobank is a major national resource for health research in the UK, with the goal of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia. The 500,000 people who took part in the project have undergone measures, provided blood, urine and saliva samples for future analysis, detailed information about themselves and agreed to have their health followed.
Researchers said they have advanced the field of AI with their new study on mortality prediction. “We have taken a major step forward in this field by developing a unique and holistic approach to predicting a person’s risk of premature death, by machine learning,” said Dr. Stephen Weng, assistant professor of Epidemiology and Data Science at the University of Nottingham the United Kingdom.
“Preventative healthcare is a growing priority in the fight against serious diseases, so we have been working for a number of years to improve the accuracy of computerized health risk assessment in the general population,” he continued. “Most applications focus on a single disease area, but predicting death due to several different disease outcomes is highly complex, especially given environmental and individual factors that may affect them.”
Weng said his team’s system uses computers to build new risk prediction models that take into account a wide range of demographic, biometric, clinical and lifestyle factors for each individual assessed, including their daily dietary consumption of fruit, vegetables and meat.
“We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and ‘hospital episodes’ statistics,” Weng said. “We found machine-learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert.”
Artificial Intelligence and ML models dubbed “Random Forest” and “Deep Learning” were used in the study. They were pitched against the traditionally-used “Cox Regression” prediction model based on age and gender – found to be the least accurate at predicting mortality, Weng said.
The new study builds on previous work by the Nottingham team which showed that four different AI algorithms—Random Forest, Logistic Regression, Gradient Boosting and Neural Networks—were significantly better at predicting cardiovascular disease than an established algorithm used in current cardiology guidelines.
That study was in keeping with the currently most prominent area of research—the field of diagnostics and prognosis—which has seen rapid growth in the use of ML. Traditionally, prognostics have relied on statistics to predict, for example, a person’s future risk of developing heart disease. And these have demonstrated high predictive accuracy, verified and replicated with numerous validation studies. “Thus, the challenge for applications and algorithms developed using ML is to not only enhance what can be achieved with a traditional method but to also develop and report them in a similarly transparent and replicable way,” the authors wrote.
“In the era of big data, there is great optimism that machine-learning (ML) can potentially revolutionize health care, offer approaches for diagnostic assessment and personalize therapeutic decisions on a par with, or superior, to clinicians,” the authors wrote. “ML techniques rely on machine-guided computational methods rather than human-guided data analysis to fit a ‘function’ to the data in more standard statistical methods. While ML can still use familiar models such as Logistic Regression, many other ML techniques do not use a pre-determined equation. Artificial neural networks, for example, seek to determine the ‘best function’ which efficiently models all complex and non-linear interactions between variables while minimizing the error between predicted and observed outcomes.”
Prognostic modelling using standard methods is well-established, particularly for predicting a person’s risk of a single disease. “Our recent research has used ML approaches for prognostic modelling using routine primary care data,” the authors wrote. “This demonstrated improved accuracy for prediction of cardiovascular disease…Machine learning may offer the potential to also explore outcomes of even greater complexity and multi-factorial causation, such as premature death.”
The Nottingham team says the further study will tell whether their AI algorithms will be successful in other population groups. They hope to continue to explore those as well as other ways to implement these systems into routine healthcare. The researchers predict that AI will play a vital part in the development of future tools capable of delivering personalized medicine and tailoring risk management to individual patients.
The University of Nottingham is a research-intensive university ranked among the world’s top 100. Some 44,000 students attend Nottingham on campuses in the UK, China and Malaysia.