Sunday February 23, 2020

This AI Model may Predict Heart Diseases

AI may predict long-term risks of heart attack, cardiac death

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Heart attack
Researchers have found that Artificial Intelligence can be used to predict heart attacks and cardiac deaths. Pixabay

Researchers have found that machine learning, patterns and inferences computers use to learn to perform tasks, can predict the long-term risk of heart attack and cardiac death.

According to the study, published in the journal Cardiovascular Research, machine learning appears to be better at predicting heart attacks and cardiac deaths than the standard clinical risk assessment used by cardiologists.

“Our study showed that machine learning integration of clinical risk factors and imaging measures can accurately personalise the patient’s risk of suffering an adverse event such as heart attack or cardiac death,” said the study researchers from the Biomedical Imaging Research Institute in US

For the findings, the research team studied subjects from the imaging arm of a prospective, randomised research trial, who underwent coronary artery calcium scoring with available cardiac CT scans and long-term follow-up.

Participants here were asymptomatic, middle-aged subjects, with cardiovascular risk factors, but no known coronary artery disease.

Researchers used machine learning to assess the risk of myocardial infarction and cardiac death in the subjects, and then compared the predictions with the actual experiences of the subjects over fifteen years.

Heart Health
Diet, exercise and marital status are some of the factors that can affect the heart health. Pixabay

Subjects here answered a questionnaire to identify cardiovascular risk factors and to describe their diets, exercise and marital status. The final study consisted of 1,912 subjects, fifteen years after they were first studied.

76 subjects presented an event of myocardial infarction and/or cardiac death during this follow-up time. The subjects’ predicted machine learning scores aligned accurately with the actual distribution of observed events.

The atherosclerotic cardiovascular disease risk score, the standard clinical risk assessment used by cardiologists, overestimated the risk of events in the higher risk categories. Machine learning did not.

In unadjusted analysis, high predicted machine learning risk was significantly associated with a higher risk of a cardiac event.

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“While machine learning models are sometimes regarded as “black boxes”, we have also tried to demystify machine learning; in this manuscript, we describe individual predictions for two patients as examples,” said researchers

“When applied after the scan, such individualised predictions can help guide recommendations for the patient, to decrease their risk of suffering an adverse cardiac event,” they added. (IANS)

Next Story

Here’s Why Information Overload May Not be Good

The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms

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Information
In situations where people do not have background knowledge, they become more confident with the new information and make better decisions. Pixabay

Information overload may not always be a good thing. Researchers have found that in certain circumstances, having more background information may actually lead people to take worse decisions.

The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms and how healthcare professionals and financial advisors present this new information to their patients and clients.

“Being accurate is not enough for information to be useful,” said Samantha Kleinberg, Associate Professor of Computer Science at Stevens Institute of Technology in New Jersey, US.”It’s assumed that AI and Machine Learning will uncover great information, we’ll give it to people and they’ll make good decisions. However, the basic point of the paper is that there is a step missing: we need to help people build upon what they already know and understand how they will use the new information,” Kleinberg added.

For example, when doctors communicate information to patients, such as recommending blood pressure medication or explaining risk factors for diabetes, people may be thinking about the cost of medication or alternative ways to reach the same goal.

“So, if you don’t understand all these other beliefs, it’s really hard to treat them in an effective way,” said Kleinberg. For the study, the researchers asked 4,000 participants a series of questions about topics with which they would have varying degrees of familiarity.

Some participants were asked to make decisions on scenarios they could not possibly be familiar with. Other participants were asked about more familiar topics i.e. choosing how to reduce risk in a retirement portfolio or deciding between specific meals and activities to manage bodyweight.

The team compared whether people did better or worse with new information or were just using what they already knew. The researchers found that prior knowledge got in the way of choosing the best outcome. Kleinberg found the same to be true when she posed a problem about health and exercise, as it relates to diabetes.

When people without diabetes read the problem, they treated the new information at face value, believed it and used it successfully. People with diabetes, however, started second-guessing what they knew and as in the previous example, did much worse. “In situations where people do not have background knowledge, they become more confident with the new information and make better decisions,” said Kleinberg.

AI
The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms and how healthcare professionals and financial advisors present this new information to their patients and clients. Pixabay

“So there’s a big difference in how we interpret the information we are given and how it affects our decision making when it relates to things we already know vs. when it’s in a new or unfamiliar setting,” she added.

Kleinberg cautioned that the point of the paper is not that information is bad. She argued only that in order to help people make better decisions, it is important to better understand what people already know and tailor information based on that mental model.

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Started in 1870, Stevens Institute of Technology is one of the oldest technological institutes in the US. (IANS)