User's Cognitive-Behavior-Based Preference Access Under Disease-Specific Online Medical Inquiry Text Mining
User's Cognitive-Behavior-Based Preference Access Under Disease-Specific Online Medical Inquiry Text Mining
复制标题
特定疾病在线医疗咨询文本挖掘下基于用户认知行为的偏好访问
DOI：
10.1109/tem.2022.3143432
复制
发表时间：
2022
影响因子：
5.8
通讯作者：
Wei Wei
中科院分区：
文献类型：
--
作者：
Xin Liu;Yanju Zhou;Zongrun Wang;Wei Wang;Ajay Kumar;Wei Wei
实质性的真实的病例可以从特定疾病的在线医疗询问文本中形成。因此，用户偏好可以构成潜在的丰富的商业医疗价值，并为医疗服务推荐提供决策支持。针对特定疾病的在线医疗查询文本，有必要挖掘用户偏好。然而，在特定疾病查询文本的背景下，用户偏好会随着认知行为决策而改变。更重要的是，用户偏好所处的特定疾病在线查询文本的语境会在真实的时间内影响用户的认知行为决策。然而，现有的偏好访问方法具有相对较低的精度，因为它们没有考虑不同用户在各种情境下的认知行为与其偏好之间的内在联系。我们扩展了上下文感知偏好模型，提出了一种基于认知行为的方法来评估用户的偏好。将各种疾病主题的在线医疗咨询文本中的上下文信息以及用户和文本属性对其认知行为的影响抽象为概念模型（包括水平、有用性、风险和有效性认知），以获得相互影响和调整关系。此外，在多维文本空间和多维疾病空间中获得了更准确的用户认知参考。基于大量真实世界数据集，我们的实验揭示了AP@R作为评价标准。与上下文感知算法和ELPCAP算法相比，本文提出的方法在加入认知行为后，显著提高了偏好预测的精度，表明该方法能够有效挖掘用户认知行为-偏好关系。
Substantial real cases can be formed from disease-specific online medical inquiry texts. Therefore, user preferences can constitute potentially rich commercial medical value and provide decision support for medical service recommendations. It is necessary to mine user preferences in disease-specific online medical inquiry texts. However, user preferences will change with cognitive behavior decisions in the context of disease-specific inquiry texts. More importantly, the context of disease-specific online inquiry texts in which user preferences are located will affect users' cognitive behavior decisions in real time. However, the existing preference access methods have relatively low precision since they fail to consider the inherent connection between different users’ cognitive behaviors in various contexts and their preferences. We expanded the contextual perception preference model to propose a cognitive-behavior-based method to assess user preference. The contextual information (in online medical inquiry texts on various disease topics) and the impacts of user and text attributes on their cognitive behaviors were abstracted into concept models (including level, usefulness, risk, and effectiveness cognition) to obtain the mutual influences and adjusted relationships. Moreover, more accurate user cognition references were obtained within the multidimensional text space and multidimensional disease space. Based on a large-volume real-world dataset, our experiments revealed the AP@R as the evaluation standard. The method proposed in this article, compared with the contextual perception and ELPCAP algorithms, significantly improves the precision of preference prediction after the addition of cognitive behaviors, suggesting that the method can effectively mine the user cognitive behavior-preference relationships.
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影响因子：
6.1
作者：
Christian Homburg;Laura Ehm;M. Artz
通讯作者：
Christian Homburg;Laura Ehm;M. Artz
DOI：
10.1145/1935826.1935882
发表时间：
2011-02
期刊：
--
影响因子：
--
作者：
Michael J. Welch;Uri Schonfeld;Dan He;Junghoo Cho
通讯作者：
Michael J. Welch;Uri Schonfeld;Dan He;Junghoo Cho
在线医疗社区的信息搜索：社会自我和个人自我的双重影响
影响因子：
5.8
作者：
Liu Na;Tong Yu;Chan Hock Chuan
通讯作者：
Chan Hock Chuan
DOI：
10.1016/j.knosys.2018.05.039
发表时间：
2018-05
期刊：
Knowl. Based Syst.
影响因子：
--
作者：
N. Kamis;F. Chiclana;J. Levesley
通讯作者：
N. Kamis;F. Chiclana;J. Levesley
影响因子：
5.4
作者：
M. Michelson;Sofus A. Macskassy
通讯作者：
M. Michelson;Sofus A. Macskassy