AXAI-CDSS: An Affective Explainable AI-Driven Clinical Decision Support System for Cannabis Use
As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. However, many artificial intelligence (AI) models lack transparency and interpretability due to their opaque nature, limiti...
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| Vydané v: | 2025 International Conference on Activity and Behavior Computing (ABC) s. 1 - 14 |
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21.04.2025
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| Abstract | As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. However, many artificial intelligence (AI) models lack transparency and interpretability due to their opaque nature, limiting their trust and adoption in real-world medical applications, such as clinical decision support systems (CDSS). To address this issue, this paper enhances algorithm explainability underlying CDSS by integrating multiple Explainable Artificial Intelligence (XAI) methods and applying causal inference techniques to clarify the models' predictive decisions under various scenarios. By providing deeper interpretability of the XAI outputs using Large Language Models (LLMs), we provide users with more personalized and accessible insights to overcome the challenges posed by AI's "black box" nature. Our system dynamically adjusts feedback based on user queries and emotional states, combining text-based sentiment analysis with real-time facial emotion recognition to ensure responses are empathetic, contextadaptive, and user-centered. This approach bridges the gap between the learning demands of interpretability and the need for intuitive understanding, enabling non-technical users such as clinicians and clinical researchers to interact effectively with AI models. Ultimately, this approach improves usability, enhances perceived trustworthiness, and increases the impact of CDSS in healthcare applications. |
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| AbstractList | As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. However, many artificial intelligence (AI) models lack transparency and interpretability due to their opaque nature, limiting their trust and adoption in real-world medical applications, such as clinical decision support systems (CDSS). To address this issue, this paper enhances algorithm explainability underlying CDSS by integrating multiple Explainable Artificial Intelligence (XAI) methods and applying causal inference techniques to clarify the models' predictive decisions under various scenarios. By providing deeper interpretability of the XAI outputs using Large Language Models (LLMs), we provide users with more personalized and accessible insights to overcome the challenges posed by AI's "black box" nature. Our system dynamically adjusts feedback based on user queries and emotional states, combining text-based sentiment analysis with real-time facial emotion recognition to ensure responses are empathetic, contextadaptive, and user-centered. This approach bridges the gap between the learning demands of interpretability and the need for intuitive understanding, enabling non-technical users such as clinicians and clinical researchers to interact effectively with AI models. Ultimately, this approach improves usability, enhances perceived trustworthiness, and increases the impact of CDSS in healthcare applications. |
| Author | Dey, Anind Bae, Sang Won Zhang, Tongze Chung, Tammy |
| Author_xml | – sequence: 1 givenname: Tongze surname: Zhang fullname: Zhang, Tongze organization: Stevens Institute of Technology,Hoboken,New Jersey – sequence: 2 givenname: Tammy surname: Chung fullname: Chung, Tammy organization: Rutgers University,Newark,New Jersey – sequence: 3 givenname: Anind surname: Dey fullname: Dey, Anind organization: University of Washington,Seattle,Washington – sequence: 4 givenname: Sang Won surname: Bae fullname: Bae, Sang Won organization: Stevens Institute of Technology,Hoboken,New Jersey |
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| Snippet | As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its... |
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| SubjectTerms | Affective Computing Algorithmic Decisions Artificial intelligence Cannabis Intoxication Cannabis Use Disorder Cannabis-Intoxicated Behaviors Causal Inference Clinical Decision Support Systems (CDSS) Decision support systems Emotion recognition Explainable AI Explainable Artificial Intelligence (XAI) Facial Emotion Recognition Healthcare AI Inference algorithms Large language models Large Language Models (LLMs) Medical services Passive Sensing Personalized Intervention Predictive models Real-time systems Sentiment analysis Transparency Trustworthy AI |
| Title | AXAI-CDSS: An Affective Explainable AI-Driven Clinical Decision Support System for Cannabis Use |
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