Fetching the paper…
Reading the bibliography…
The recommendation of medication is a vital aspect of intelligent healthcare systems, as it involves prescribing the most suitable drugs based on a patient's specific health needs.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
E. Choi, M. T. Bahadori, J. Sun, J. Kulas, A. Schuetz, and W. Stewart, “Retain: An interpretable predictive model for healthcare using reverse time attention mechanism,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
Y. Zhang, R. Chen, J. Tang, W. F. Stewart, and J. Sun, “Leap: learning to prescribe effective and safe treatment combinations for multimorbidity,” in proceedings of the 23rd ACM SIGKDD international conference on knowledge Discovery and data Mining , 2017, pp. 1315–1324
2017
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Shang, C. Xiao, T. Ma, H. Li, and J. Sun, “Gamenet: Graph augmented memory networks for recommending medication combination,” in proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 1126–1133
2019
Earlier work this paper cites.
J. Gruendner, T. Schwachhofer, P. Sippl, N. Wolf, M. Erpenbeck, C. Gulden, L. A. Kapsner, J. Zierk, S. Mate, M. Stürzl et al. , “Ketos: Clinical decision support and machine learning as a service–a training and deployment platform based on docker, omop-cdm, and fhir web services,” PloS one , vol. 14, no. 10, p. e0223010, 2019
2019
Earlier work this paper cites.
I. Rahmawati and V. I. D. Prastika, “Physician knowledge and responsibility of prescription policy,” Jurnal Administrasi Kesehatan Indonesia Volume , vol. 8, no. 1, 2020
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,” International Journal of Computer Vision , vol. 129, pp. 1789–1819, 2021
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
R. Wu, Z. Qiu, J. Jiang, G. Qi, and X. Wu, “Conditional generation net for medication recommendation,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 935–945
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.
V. Borisov, K. Sessler, T. Leemann, M. Pawelczyk, and G. Kasneci, “Language models are realistic tabular data generators,” in The Eleventh International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia et al. , “Glm-130b: An open bilingual pre-trained model,” in The Eleventh International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
S. Geng, S. Liu, Z. Fu, Y. Ge, and Y. Zhang, “Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5),” in Proceedings of the 16th ACM Conference on Recommender Systems , 2022, pp. 299–315
2022
Cited alongside, same era.
K. Tirumala, A. Markosyan, L. Zettlemoyer, and A. Aghajanyan, “Memorization without overfitting: Analyzing the training dynamics of large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 38 274–38 290, 2022
2022
OpenAI, “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Wang, C. Liu, N. Xi, Z. Qiang, S. Zhao, B. Qin, and T. Liu, “Huatuo: Tuning llama model with chinese medical knowledge,” 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International Conference on Machine Learning . PMLR, 2022, pp. 12 888–12 900
2022
Cited alongside, same era.
Y. Tan, C. Kong, L. Yu, P. Li, C. Chen, X. Zheng, V. S. Hertzberg, and C. Yang, “4sdrug: Symptom-based set-to-set small and safe drug recommendation,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022, pp. 3970–3980
2022
Cited alongside, same era.
S. Bhoi, M.-L. Lee, W. Hsu, and N. C. Tan, “Refine: A fine-grained medication recommendation system using deep learning and personalized drug interaction modeling,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023
2023
Cited alongside, same era.
Z. Ali, Y. Huang, I. Ullah, J. Feng, C. Deng, N. Thierry, A. Khan, A. U. Jan, X. Shen, W. Rui et al. , “Deep learning for medication recommendation: a systematic survey,” Data Intelligence , vol. 5, no. 2, pp. 303–354, 2023
2023
Cited alongside, same era.
Y. Li, Z. Li, K. Zhang, R. Dan, and Y. Zhang, “Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge,” arXiv e-prints , pp. arXiv–2303, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Zhang, X. Wu, Q. Fang, S. Qian, and C. Xu, “Knowledge-enhanced attributed multi-task learning for medicine recommendation,” ACM Transactions on Information Systems , vol. 41, no. 1, pp. 1–24, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Q. Liu, X. Wu, X. Zhao, Y. Zhu, D. Xu, F. Tian, and Y. Zheng, “When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2024, pp. 1104–1114
2024
Closest in time.