| Keywords
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Machine Learning, Large Language Models, Uncertainty Estimation, Electronic Health Records +
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| Level
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Master +
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| OneLineSummary
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The student will investigate the potential of LLMs to simplify clinical note annotation along with uncertainty estimation, contributing to improved healthcare data management. +
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| Prerequisites
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Statistics; Neural Networks; Programming (Python) +
|
| References
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Yang, Zhichao, et al. "Multi-label few-sho … Yang, Zhichao, et al. "Multi-label few-shot ICD coding as autoregressive generation with prompt." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 4. 2023.
Liu, Leibo, et al. "Automated icd coding using extreme multi-label long text transformer-based models." Artificial Intelligence in Medicine (2023): 102662.
Hu, Edward J., et al. "Lora: Low-rank adaptation of large language models." arXiv preprint arXiv:2106.09685 (2021).
Sensoy, Murat, Lance Kaplan, and Melih Kandemir. "Evidential deep learning to quantify classification uncertainty." Advances in neural information processing systems 31 (2018). information processing systems 31 (2018).
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| StudentProjectStatus
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Open +
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| Supervisors
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Awais Ashfaq +
, Prayag Tiwari +
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| TimeFrame
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2023-2024 +
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| Title
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Leveraging LLMs for Clinical Note Annotation and Uncertainty Estimation +
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| Categories |
StudentProject +
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| Modification dateThis property is a special property in this wiki.
|
27 October 2023 07:33:38 +
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