Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults.

Tongze Zhang, Tammy Chung, Anind Dey, Sang Won Bae
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Abstract

As an increasing number of states adopt more permissive cannabis regulations, the necessity of gaining a comprehensive understanding of cannabis's effects on young adults has grown exponentially, driven by its escalating prevalence of use. By leveraging popular eXplainable Artificial Intelligence (XAI) techniques such as SHAP (SHapley Additive exPlanations), rule-based explanations, intrinsically interpretable models, and counterfactual explanations, we undertake an exploratory but in-depth examination of the impact of cannabis use on individual behavioral patterns and physiological states. 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.

探索算法的可解释性:为个性化临床决策支持生成可解释的人工智能见解,重点关注青少年大麻中毒问题。
随着越来越多的州采用更加宽松的大麻法规,全面了解大麻对青壮年的影响的必要性在大麻使用率不断攀升的推动下呈指数增长。通过利用流行的可解释人工智能(XAI)技术,如 SHAP(SHapley Additive exPlanations)、基于规则的解释、内在可解释模型和反事实解释,我们对使用大麻对个人行为模式和生理状态的影响进行了探索性但深入的研究。这项研究探索了通过将可解释人工智能(XAI)技术与传感器数据相结合来促进算法决策的可能性,目的是为研究人员和临床医生提供个性化的大麻中毒行为分析。SHAP 可分析特定因素(如环境噪声或心率)的重要性并量化其影响,使临床医生能够准确定位有影响的行为和环境条件。SkopeRules 简化了对特定活动或环境使用大麻的理解。决策树可清晰显示影响大麻消费的各种因素是如何相互作用的。反事实模型有助于确定可能改变大麻使用结果的行为或条件的关键变化,从而指导有效的个性化干预策略。这种多维分析方法不仅揭示了吸食大麻后行为和生理状态的变化,如活动状态的频繁波动、非传统的睡眠模式以及不同时间和地点的特定吸食习惯,还强调了吸食大麻后个体差异的重要意义。这些见解对临床医生深入了解患者的不同需求以及制定有针对性的干预策略具有深远的意义。此外,我们的研究结果还强调了 XAI 技术在提高临床决策支持系统(CDSS)的透明度和可解释性方面可发挥的关键作用,尤其是在药物滥用治疗方面。这项研究极大地促进了正在进行的旨在推进临床实践的倡议,这些临床实践旨在预防和减少与大麻有关的健康危害,并将 XAI 定位为临床医生和研究人员的辅助工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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