新しいAIツールがCOVID-19ワクチンの摂取を予測する(New AI tool predicts COVID-19 vaccine uptake)

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2024-03-18 ノースウェスタン大学

ノースウェスタン大学とシンシナティ大学の研究者による新研究では、機械学習アルゴリズムと認知科学の組み合わせを用いた予測モデルが、ワクチン接種に対する個々の人々の反応を予測し、ワクチン接種キャンペーンの効果を向上させることができる可能性が示されています。調査では、3,476人の成人がCOVID-19パンデミック中の2021年に調査され、その結果がワクチン受け入れ率を予測するために使用されました。研究は、政策立案者や医療従事者にとって興味深い洞察を提供しています。

<関連情報>

少人数で解釈可能な判断変数と人口統計学的変数のセットを用いたCOVID-19ワクチン接種率の予測: 横断的認知科学研究 Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Nicole L Vike ;  Sumra Bari ;  Leandros Stefanopoulos ;  Shamal Lalvani ;  Byoung Woo Kim ;  Nicos Maglaveras ;  Martin Block ;  Hans C Breiter ;  Aggelos K Katsaggelos
Journal of Medical Internet Research  Published: April 12, 2023
DOI:https://preprints.jmir.org/preprint/47979

新しいAIツールがCOVID-19ワクチンの摂取を予測する(New AI tool predicts COVID-19 vaccine uptake)

Abstract

Background:Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake.

Objective:This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction.

Methods:We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R2>0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake. Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction.

Results:Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P<.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P<.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake.

Conclusions:The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).

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