通过知识感知与多模态预测建模解码虚拟医疗保健的成功
Decoding Virtual Healthcare Success through Knowledge-Aware and Multimodal Predictive Modeling
AI总结：
本研究提出一种融合多模态数据与动态知识网络的预测建模方法，以解决在线咨询数据稀疏与碎片化问题，有效提升了虚拟医疗咨询成功预测的准确性与可解释性。
摘要：
在线医疗保健咨询改变了患者寻求医疗建议的方式，在提供便利的同时，也为确保咨询成功引入了新的挑战。预测在线咨询是否会成功，对于改善患者体验和维持平台竞争力至关重要。然而，由于患者护理过程的碎片化，以及虚拟与传统医疗保健系统之间缺乏整合，这种预测本身十分困难。此外，从在线平台收集的数据，包括文本对话、交互序列和行为轨迹，通常是稀疏且不完整的。本研究开发了一种预测建模方法，该方法融合多模态数据与动态构建的知识网络，以捕获患者、医生和咨询情境之间的潜在关系。通过整合异构信息源并揭示数字交互的不断演变结构，该模型提高了咨询成功预测的准确性和可解释性。研究结果为设计通过数据驱动智能结合在线和离线服务的混合医疗保健生态系统提供了启示。
英文摘要：
Online healthcare consultations have transformed how patients seek medical advice, offering convenience while introducing new challenges for ensuring consultation success. Predicting whether an online consultation will be successful is critical for improving patient experiences and sustaining platform competitiveness. Yet, such prediction is inherently difficult due to the fragmented nature of patients' care journeys and the lack of integration between virtual and traditional healthcare systems. Furthermore, the data collected from online platforms, including textual conversations, interaction sequences, and behavioral traces, are often sparse and incomplete. This study develops a predictive modeling approach that fuses multimodal data and dynamically constructed knowledge networks to capture latent relationships among patients, physicians, and consultation contexts. By integrating heterogeneous information sources and uncovering the evolving structure of digital interactions, the model enhances the accuracy and interpretability of consultation success prediction. The findings offer implications for designing hybrid healthcare ecosystems that combine online and offline services through data-driven intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
