Tutorial: Neuro-symbolic AI for Mental Healthcare
Tutorial: Neuro-symbolic AI for Mental Healthcare
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教程：用于心理保健的神经符号人工智能
DOI：
10.1145/3564121.3564817
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Sheth, Amit P
中科院分区：
文献类型：
--
作者：
Roy, Kaushik;Lokala, Usha;Gaur, Manas;Sheth, Amit P
在意识到对慢性精神健康（MH）患者进行早期干预的重要性后，用于精神保健（MHCare）的人工智能（AI）系统一直在不断发展。社交媒体（SocMedia）成为支持寻求MHCare的患者的首选平台。没有社会耻辱的同伴支持团体的创建导致患者从临床环境过渡到SocMedia支持的互动，以获得快速帮助。研究人员开始探索SocMedia的内容，寻找线索，展示不同MH条件之间的相关性或因果关系，以设计更好的干预策略。基于用户级分类的AI系统旨在利用来自各种MH条件的不同SocMedia数据来预测MH条件。随后，研究人员创建了分类方案来衡量每种MH状况的严重程度。这种特别方案、工程特征和模型不仅需要大量数据，而且无法对结果进行临床可接受和可解释的推理。为了改善MHCare的Neural-AI，需要注入临床医生在决策中使用的临床符号知识。神经人工智能系统在MH中的一个有影响力的用例是会话系统。这些系统需要分类和生成之间的协调，以促进对话代理（CA）的人性化对话。目前具有深度语言模型的CA在其世代中缺乏事实正确性，医学相关性和安全性，这与无法解释的统计分类技术相矛盾。这个讲座式的教程将展示我们对注入临床知识的神经符号方法的研究，以改善Neural-AI系统的结果，从而改善MHCare的干预措施：（a）我们将讨论如何使用不同的临床知识来创建专门的数据集，以有效地训练Neural-AI系统。(b)心血管疾病患者基于性别差异表达不同的MH症状。我们将证明，知识注入的神经人工智能系统可以识别这些患者的性别特异性MH症状。(c)我们将描述注入临床过程知识的策略，以改进生成相关问题和响应的语言模型。
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, to predict MH conditions. Subsequently, researchers created classification schemes to measure the severity of each MH condition. Such ad-hoc schemes, engineered features, and models not only require a large amount of data but fail to allow clinically acceptable and explainable reasoning over the outcomes. To improve Neural-AI for MHCare, infusion of clinical symbolic knowledge that clinicans use in decision making is required. An impactful use case of Neural-AI systems in MH is conversational systems. These systems require coordination between classification and generation to facilitate humanistic conversation in conversational agents (CA). Current CAs with deep language models lack factual correctness, medical relevance, and safety in their generations, which intertwine with unexplainable statistical classification techniques. This lecture-style tutorial will demonstrate our investigations into Neuro-symbolic methods of infusing clinical knowledge to improve the outcomes of Neural-AI systems to improve interventions for MHCare:(a) We will discuss the use of diverse clinical knowledge in creating specialized datasets to train Neural-AI systems effectively. (b) Patients with cardiovascular disease express MH symptoms differently based on gender differences. We will show that knowledge-infused Neural-AI systems can identify gender-specific MH symptoms in such patients. (c) We will describe strategies for infusing clinical process knowledge as heuristics and constraints to improve language models in generating relevant questions and responses.
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DOI：
10.18653/v1/2022.clpsych-1.12
发表时间：
2022-05
期刊：
ArXiv
影响因子：
--
作者：
Shrey Gupta;Anmol Agarwal;Manas Gaur;Kaushik Roy;Vignesh Narayanan;P. Kumaraguru;Amit P. Sheth
通讯作者：
Shrey Gupta;Anmol Agarwal;Manas Gaur;Kaushik Roy;Vignesh Narayanan;P. Kumaraguru;Amit P. Sheth
影响因子：
2.6
作者：
A. Sheth;K. Thirunarayan
通讯作者：
A. Sheth;K. Thirunarayan
DAO：社交媒体和暗网上药物使用流行病学本体论
影响因子：
8.5
作者：
Usha Lokala;Raminta Daniulaityte;Francois R. Lamy;Manas Gaur;K. Thirunarayan;Ugur Kursuncu;A. Sheth
通讯作者：
A. Sheth
流程注入知识的人工智能：迈向用户级可解释性、可解释性和安全性
影响因子：
3.2
作者：
Sheth, Amit;Gaur, Manas;Roy, Kaushik;Venkataraman, Revathy;Khandelwal, Vedant
通讯作者：
Khandelwal, Vedant