Decoding Virtual Healthcare Success through Knowledge-Aware and Multimodal Predictive Modeling
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917051312898048 |
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| author | Geng, Shuang Zhang, Wenli Xie, Jiaheng Liang, Gemin Niu, Ben Ram, Sudha |
| author_facet | Geng, Shuang Zhang, Wenli Xie, Jiaheng Liang, Gemin Niu, Ben Ram, Sudha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_03833 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Decoding Virtual Healthcare Success through Knowledge-Aware and Multimodal Predictive Modeling Geng, Shuang Zhang, Wenli Xie, Jiaheng Liang, Gemin Niu, Ben Ram, Sudha Machine Learning K.5 H.4.m 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. |
| title | Decoding Virtual Healthcare Success through Knowledge-Aware and Multimodal Predictive Modeling |
| topic | Machine Learning K.5 H.4.m |
| url | https://arxiv.org/abs/2306.03833 |