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A powerful new tool in artificial intelligence is able to predict whether someone is willing to be vaccinated against COVID-19.
The predictive system uses a small set of data from demographics and personal judgments such as aversion to risk or loss.
The findings frame a new technology that could have broad applications for predicting mental health and result in more effective public health campaigns.
A team led by researchers at the University of Cincinnati and Northwestern University created a predictive model using an integrated system of mathematical equations describing the lawful patterns in reward and aversion judgment with machine learning.
“We used a small number of variables and minimal computational resources to make predictions,” said lead author Nicole Vike, a senior research associate in UC’s College of Engineering and Applied Science.
“COVID-19 is unlikely to be the last pandemic we see in the next decades. Having a new form of AI for prediction in public health provides a valuable tool that could help prepare hospitals for predicting vaccination rates and consequential infection rates.”
The study was published in the Journal of Medical Internet Research Public Health and Surveillance.
Researchers surveyed 3,476 adults across the United States in 2021 during the COVID-19 pandemic. At the time of the survey, the first vaccines had been available for more than a year.
Respondents provided information such as where they live, income, highest education level completed, ethnicity and access to the internet. The respondents’ demographics mirrored those of the United States based on U.S. Census Bureau figures.
Participants were asked if they had received either of the available COVID-19 vaccines. About 73% of respondents said they were vaccinated, slightly more than the 70% of the nation’s population that had been vaccinated in 2021.
Further, they were asked if they routinely followed four recommendations designed to prevent the spread of the virus: wearing a mask, social distancing, washing their hands and not gathering in large groups.
Participants were asked to rate how much they liked or disliked a randomly sequenced set of 48 pictures on a seven-point scale of 3 to -3. The pictures were from the International Affective Picture Set, a large set of emotionally evocative color photographs, in six categories: sports, disasters, cute animals, aggressive animals, nature and food.
Vike said the goal of this exercise is to quantify mathematical features of people’s judgments as they observe mildly emotional stimuli. Measures from this task include concepts familiar to behavioral economists — or even people who gamble — such aversion to risk (the point at which someone is willing to accept potential loss for a potential reward) and aversion to loss. This is the willingness to avoid risk by, for example, obtaining insurance.
“The framework by which we judge what is rewarding or aversive is fundamental to how we make medical decisions,” said co-senior author Hans Breiter, a professor of computer science at UC. “A seminal paper in 2017 hypothesized the existence of a standard model of the mind. Using a small set of variables from mathematical psychology to predict medical behavior would support such a model. The work of this collaborative team has provided such support and argues that the mind is a set of equations akin to what is used in particle physics.”
The judgment variables and demographics were compared between respondents who were vaccinated and those who were not. Three machine learning approaches were used to test how well the respondents’ judgment, demographics and attitudes toward COVID-19 precautions predicted whether they would get the vaccine.
The study demonstrates that artificial intelligence can make accurate predictions about human attitudes with surprisingly little data or reliance on expensive and time-consuming clinical assessments.
“We found that a small set of demographic variables and 15 judgment variables predict vaccine uptake with moderate to high accuracy and high precision,” the study said. “In an age of big-data machine learning approaches, the current work provides an argument for using fewer but more interpretable variables.”
“The study is anti-big-data,” said co-senior author Aggelos Katsaggelos, an endowed professor of electrical engineering and computer science at Northwestern University. “It can work very simply. It doesn’t need super-computation, it’s inexpensive and can be applied with anyone who has a smartphone. We refer to it as computational cognition AI. It is likely you will be seeing other applications regarding alterations in judgment in the very near future.”