This study explores the possibility of facilitating algorithmic decision-making by combining interpretable artificial intelligence (XAI) techniques with sensor data, with the aim of providing researchers and clinicians with personalized analyses of cannabis intoxication behavior. SHAP analyzes the importance and quantifies the impact of specific factors such as environmental noise or heart rate, enabling clinicians to pinpoint influential behaviors and environmental conditions. SkopeRules simplify the understanding of cannabis use for a specific activity or environmental use. Decision trees provide a clear visualization of how factors interact to influence cannabis consumption. Counterfactual models help identify key changes in behaviors or conditions that may alter cannabis use outcomes, to guide effective individualized intervention strategies. This multidimensional analytical approach not only unveils changes in behavioral and physiological states after cannabis use, such as frequent fluctuations in activity states, nontraditional sleep patterns, and specific use habits at different times and places, but also highlights the significance of individual differences in responses to cannabis use. These insights carry profound implications for clinicians seeking to gain a deeper understanding of the diverse needs of their patients and for tailoring precisely targeted intervention strategies. Furthermore, our findings highlight the pivotal role that XAI technologies could play in enhancing the transparency and interpretability of Clinical Decision Support Systems (CDSS), with a particular focus on substance misuse treatment. This research significantly contributes to ongoing initiatives aimed at advancing clinical practices that aim to prevent and reduce cannabis-related harms to health, positioning XAI as a supportive tool for clinicians and researchers alike.

翻译：本研究探索了通过结合可解释人工智能（XAI）技术与传感器数据来促进算法决策的可能性，旨在为研究人员和临床医生提供关于大麻中毒行为的个性化分析。SHAP分析方法量化了环境噪声或心率等特定因素的重要性及其影响，使临床医生能够精准定位关键行为与环境条件。SkopeRules简化了对特定活动或环境下大麻使用行为的理解。决策树清晰展示了各因素如何交互影响大麻消费行为。反事实模型帮助识别可能改变大麻使用结果的关键行为或条件变化，从而指导有效的个性化干预策略制定。这种多维分析方法不仅揭示了大麻使用后行为和生理状态的变化——如活动状态频繁波动、非传统睡眠模式以及不同时空下的特定使用习惯，还强调了个体对大麻反应差异的重要性。这些发现对临床医生深入理解患者多样化需求、制定精准靶向干预策略具有深远意义。此外，我们的研究结果突显了XAI技术在增强临床决策支持系统（CDSS）透明度和可解释性方面的关键作用，特别是在物质滥用治疗领域。本研究为推进旨在预防和减少大麻相关健康损害的临床实践提供了重要贡献，将XAI定位为临床医生和研究人员的辅助工具。