ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance.
ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance.
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DOI：
10.3389/fdata.2022.1056728
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发表时间：
2022
影响因子：
3.1
通讯作者：
Sheth, Amit
中科院分区：
文献类型：
--
作者：
Roy, Kaushik;Gaur, Manas;Soltani, Misagh;Rawte, Vipula;Kalyan, Ashwin;Sheth, Amit
虚拟心理健康助理（VMHAs）用于医疗保健，为患者提供咨询和暗示性护理等服务。它们不用于患者诊断协助，因为它们不能遵守用于获得临床诊断的安全约束和专业临床过程知识（ProKnow）。在这项工作中，我们将ProKnow定义为一组有序的信息，这些信息映射到一个领域的专家的基于证据的指南或概念理解类别。我们还引入了一个新的诊断对话数据集，由医疗保健专业人员使用的安全约束和ProKnow指导（ProKnow-data）。我们开发了一种用于自然语言问题生成（NLG）的方法，该方法可以交互式地收集患者的诊断信息（ProKnow-algo）。我们展示了在这个数据集上使用最先进的大规模语言模型（LMs）的局限性。ProKnow-algo通过显式地对安全性、知识获取和可解释性建模来合并过程知识。由于评估的计算指标不能直接转化为临床设置，我们让专家临床医生参与设计评估指标，测试四个属性：安全性、逻辑一致性和可解释性的知识捕获，同时最大限度地减少标准交叉熵损失，以保持基于分布语义的与基础事实的相似性。使用ProKnow-algo的LMs在抑郁和焦虑领域生成了89%的安全问题（测试属性：安全）。此外，没有ProKnow-algo世代问题没有坚持临床过程知识在ProKnow-data（测试属性：知识捕获）。相比之下，基于proknow -algo的世代在衡量知识获取的指标上减少了96%。通过计算与抑郁和焦虑知识库中的概念的相似性来评估生成问题的可解释性。总的来说，无论lm的类型如何，ProKnow-algo在安全性、可解释性和过程引导问题生成方面比简单的预训练lm平均提高了82%。为了可再现性，我们将在验收后公开ProKnow-data和ProKnow-algo的代码库。
Virtual Mental Health Assistants (VMHAs) are utilized in health care to provide patient services such as counseling and suggestive care. They are not used for patient diagnostic assistance because they cannot adhere to safety constraints and specialized clinical process knowledge (ProKnow) used to obtain clinical diagnoses. In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We also introduce a new dataset of diagnostic conversations guided by safety constraints and ProKnow that healthcare professionals use (ProKnow-data). We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively (ProKnow-algo). We demonstrate the limitations of using state-of-the-art large-scale language models (LMs) on this dataset. ProKnow-algo incorporates the process knowledge through explicitly modeling safety, knowledge capture, and explainability. As computational metrics for evaluation do not directly translate to clinical settings, we involve expert clinicians in designing evaluation metrics that test four properties: safety, logical coherence, and knowledge capture for explainability while minimizing the standard cross entropy loss to preserve distribution semantics-based similarity to the ground truth. LMs with ProKnow-algo generated 89% safer questions in the depression and anxiety domain (tested property: safety). Further, without ProKnow-algo generations question did not adhere to clinical process knowledge in ProKnow-data (tested property: knowledge capture). In comparison, ProKnow-algo-based generations yield a 96% reduction in our metrics to measure knowledge capture. The explainability of the generated question is assessed by computing similarity with concepts in depression and anxiety knowledge bases. Overall, irrespective of the type of LMs, ProKnow-algo achieved an averaged 82% improvement over simple pre-trained LMs on safety, explainability, and process-guided question generation. For reproducibility, we will make ProKnow-data and the code repository of ProKnow-algo publicly available upon acceptance.
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影响因子：
3.2
作者：
Sezgin E;Sirrianni J;Linwood SL
通讯作者：
Linwood SL
影响因子：
0.5
作者：
Kroenke, K;Spitzer, RL
通讯作者：
Spitzer, RL
影响因子：
3.2
作者：
Gaur, Manas;Faldu, Keyur;Sheth, Amit
通讯作者：
Sheth, Amit
影响因子：
8.5
作者：
Saha, Tulika;Gupta, Dhawal;Bhattacharyya, Pushpak
通讯作者：
Bhattacharyya, Pushpak
DOI：
10.1145/3110025.3123028
发表时间：
2017-07
期刊：
Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining
影响因子：
--
作者：
Yazdavar AH;Al-Olimat HS;Ebrahimi M;Bajaj G;Banerjee T;Thirunarayan K;Pathak J;Sheth A
通讯作者：
Sheth A