Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence.

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence.
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DOI:
10.1038/s41370-018-0052-y
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发表时间:
2020-01
影响因子:
4.5
通讯作者:
Price PS
Price PS
中科院分区:
医学3区
文献类型:
--
作者:
Brandon N;Dionisio KL;Isaacs K;Tornero-Velez R;Kapraun D;Setzer RW;Price PS

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接触化学品是风险评估中的一个关键考虑因素,因为它为毒理学信息增添了现实背景。描述个人在何处以及如何度过时间对于描述消费品和室内环境中化学品暴露的特征非常重要。在这里,我们创建了一个基于代理的模型(ABM),模拟纵向模式在人类行为。通过将ABM建立在人工智能(AI)系统的基础上,我们创建了模拟人类决定执行与确定化学品和其他压力源暴露相关的行为的代理。我们在一个名为基于代理的人类活动模式模型(ABMHAP)的计算机程序中实现ABM,该程序预测睡眠,饮食,通勤和工作的纵向模式。然后,我们表明,ABMHAP是能够模拟的行为在较长的时间。我们建议,这个框架,以及基于它的模型,可以产生纵向的人类行为数据,用于暴露评估。
Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. We implement the ABM in a computer program called the Agent-Based Model of Human Activity Patterns (ABMHAP) that predicts the longitudinal patterns for sleeping, eating, commuting, and working. We then show that ABMHAP is capable of simulating behavior over extended periods of time. We propose that this framework, and models based on it, can generate longitudinal human behavior data for use in exposure assessments.
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