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Effective patient care in digital healthcare requires large language models (LLMs) that not only answer questions but also actively gather critical information through well-crafted inquiries.
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J. Yang, H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, S. Zhong, B. Yin, and X. Hu, “Harnessing the power of llms in practice: A survey on chatgpt and beyond,” ACM Transactions on Knowledge Discovery from Data , 2023
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Z. Yang, I. Azimi, S. Jafarlou, S. Labbaf, J. Borelli, N. Dutt, and A. M. Rahmani, “Loneliness forecasting using multi-modal wearable and mobile sensing in everyday settings,” in 2023 IEEE 19th International Conference on Body Sensor Networks (BSN) . IEEE, 2023, pp. 1–4
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2023
M. Cheng, Z. Zhou, B. Zhang, Z. Wang, J. Gan, Z. Ren, W. Feng, Y. Lyu, H. Zhang, and X. Diao, “Efflex: Efficient and flexible pipeline for spatio-temporal trajectory graph modeling and representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 2546–2555
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H. Alikhani, Z. Wang, A. Kanduri, P. Lilieberg, A. M. Rahmani, and N. Dutt, “Seal: Sensing efficient active learning on wearables through context-awareness,” in 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE) . IEEE, 2024, pp. 1–2
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