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Published in 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), 2021
This paper proposes a hybrid scalable health information exchange platform in Thailand. Such system expedites the process of referrel, general treatment, and safety in emergency cases. The results show that interoperability, privacy, and security can be implemented with Fast Healthcare Interoperability Resources (FHIR), pseudonymization, and access control respectively.
Recommended citation: Taechoyotin, P., Prasertsom, P., Phanhong, M., Wongsutthikoson, P., Laohasurayodhin, R., Pasuthip, N., & Ruktantichoke, B. (2021, August). Health link: scalable health information exchange platform in Thailand. In 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP) (pp. 101-106). IEEE.
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Published in Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), 2024
This paper utilized contrastive learning to create better image-text embeddings/representation of scientific figure and captions leading higher performance in information retrieval task. The captions were augmented with metadata found only in scientific manuscripts which resulted in improved grouping between figure/caption within the same field, section, and topic.
Recommended citation: Taechoyotin, P., & Acuna, D. (2024, August). MISTI: Metadata-Informed Scientific Text and Image Representation through Contrastive Learning. In Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024) (pp. 155-164).
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Published in Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges, 2024
This paper proposes a multi-agent framework that generates peer review comments based on text, figures, common knowledge, and a database of known literature.
Recommended citation: Taechoyotin, P., Wang, G., Zeng, T., Sides, B., & Acuna, D. (2024, October). MAMORX: Multi-agent multi-modal scientific review generation with external knowledge. In Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges.
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Published in arXiv preprint arXiv:2505.11718, 2025
This paper proposes a multi-objective reward function to quantify the quality of peer review comments and train a LLM via Reinforcement Learning based-on the reward function for better peer review comments.
Recommended citation: Taechoyotin, P., & Acuna, D. (2025). REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning. arXiv preprint arXiv:2505.11718.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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