Friday November 15, 2019
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AI-based System To Predict Premature Deaths

For the study, the team included over half a million people aged between 40 and 69

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Representational image.

Researchers have developed an Artificial Intelligence (AI)-based system to predict the risk of early deaths due to chronic disease in middle-aged adults.

The study, published by PLOS ONE journal, found that the new AI Machine Learning models known as “random forest” and “deep learning” were very accurate in its predictions and performed better than the current standard approach to prediction developed by human experts.

Such new risk prediction models take into account demographic, biometric, clinical and lifestyle factors for each individual, and assess even their dietary consumption of fruit, vegetables and meat per day, said Stephen Weng, Assistant Professor at the University of Nottingham in Britain.

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“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

The traditionally-used “Cox regression” prediction model, based on age and gender, was found to be the least accurate at predicting mortality and also a multivariate Cox model which worked better but tended to over-predict risk.

“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 computerised health risk assessment in the general population,” said Weng.

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For the study, the team included over half a million people aged between 40 and 69.

Although these techniques could be new to many in health research and difficult to follow, clearly reporting these methods in a transparent way could help with scientific verification and future development of AI for health care, said Joe Kai, Professor at the varsity. (IANS)

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This AI Tool Can Predict Mortality Of Heart Failure Patients

Researchers develop a tool that can predict mortality of heart failure patients

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This artificial intelligence (AI) tool can predict life expectancy in heart failure patients. Pixabay

Researchers have developed an artificial intelligence (AI) tool to predict life expectancy in heart failure patients.

The machine learning algorithm based on de-identified electronic health, records data of 5,822 hospitalised or ambulatory patients with heart failure at UC San Diego Health in the US.

“We wanted to develop a tool that predicted life expectancy in heart failure patients, there are apps where algorithms are finding out all kinds of things, like products you want to purchase,” said Avi Yagil, Professor at University of California.

“We needed a similar tool to make medical decisions. Predicting mortality is important in patients with heart failure. Current strategies for predicting risk, however, are only modestly successful and can be subjective,” Yagil added.

From this model, a risk score was derived that determined low and high risk of death by identifying eight readily available variables collected for the majority of patients with heart failure:Diastolic blood pressure, Creatinine, Blood urea nitrogen, White blood cell count, Platelets, Albumin and Red blood cell distribution.

Yagil said the newly developed model was able to accurately predict life expectancy 88 per cent of the time and performed substantially better than other popular published models.

“This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year,” said researcher Eric Adler.

Heart failure patients
The mortality of a heart failure patient can be predicted. Pixabay

“This is incredibly valuable. It allows us to make informed decisions based on a proven methodology and not have to look into a crystal ball,” he added.

The tool was additionally tested using de-identified patient data from the University of California San Francisco and a data base derived from 11 European medical centers.

“It was successful in those cohorts as well,” said Yagil.

“Being able to repurpose our findings in independent populations is of utmost importance, thus validating our methodology and its results,” Yagil added.

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Researchers said the partnership between physicists and cardiologists was critical to developing a reliable tool and extensive knowledge and experiences from both sides proved synergetic.

The study was published in the European Journal of Heart Failure. (IANS)