Levi Yosi, Brandeau Margaret L, Shmueli Erez, Yamin Dan
Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv, Israel.
Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Sci Rep. 2024 Mar 12;14(1):6012. doi: 10.1038/s41598-024-56561-w.
Vaccines stand out as one of the most effective tools in our arsenal for reducing morbidity and mortality. Nonetheless, public hesitancy towards vaccination often stems from concerns about potential side effects, which can vary from person to person. As of now, there are no automated systems available to proactively warn against potential side effects or gauge their severity following vaccination. We have developed machine learning (ML) models designed to predict and detect the severity of post-vaccination side effects. Our study involved 2111 participants who had received at least one dose of either a COVID-19 or influenza vaccine. Each participant was equipped with a Garmin Vivosmart 4 smartwatch and was required to complete a daily self-reported questionnaire regarding local and systemic reactions through a dedicated mobile application. Our XGBoost models yielded an area under the receiver operating characteristic curve (AUROC) of 0.69 and 0.74 in predicting and detecting moderate to severe side effects, respectively. These predictions were primarily based on variables such as vaccine type (influenza vs. COVID-19), the individual's history of side effects from previous vaccines, and specific data collected from the smartwatches prior to vaccine administration, including resting heart rate, heart rate, and heart rate variability. In conclusion, our findings suggest that wearable devices can provide an objective and continuous method for predicting and monitoring moderate to severe vaccine side effects. This technology has the potential to improve clinical trials by automating the classification of vaccine severity.
疫苗是我们减少发病率和死亡率的最有效工具之一。尽管如此，公众对疫苗接种的犹豫往往源于对潜在副作用的担忧，而这些副作用因人而异。截至目前，尚无自动系统可在接种疫苗后主动警告潜在副作用或评估其严重程度。我们开发了机器学习（ML）模型，旨在预测和检测接种疫苗后的副作用严重程度。我们的研究涉及2111名至少接种过一剂新冠疫苗或流感疫苗的参与者。每位参与者都配备了佳明Vivosmart 4智能手表，并被要求通过专用移动应用程序完成一份关于局部和全身反应的每日自我报告问卷。我们的XGBoost模型在预测和检测中度至重度副作用方面，受试者工作特征曲线下面积（AUROC）分别为0.69和0.74。这些预测主要基于疫苗类型（流感疫苗与新冠疫苗）、个体既往疫苗副作用史以及疫苗接种前从智能手表收集的特定数据，包括静息心率、心率和心率变异性等变量。总之，我们的研究结果表明，可穿戴设备可为预测和监测中度至重度疫苗副作用提供一种客观且持续的方法。这项技术有可能通过自动对疫苗严重程度进行分类来改善临床试验。
