Author

Julian C. Hong

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Open access Sep 2026

Leveraging dynamic prompting for outcome prediction of cancer patients using large language models and electronic health record notes

Outcome prediction from unstructured EHR notes remains challenging, especially for rare cancers with limited representation in large language model (LLM) pre-training data. We present a dynamic prompting framework that retrieves semantically similar patient examples, constructs tailored few-shot prompts and integrate...

Shreya Anil, B. Srinivas, Bo Liu et al. · npj Digital Medicine · 0 citations

#human-computer interacti... Preprint Sep 2026

"I Know Where to Look,"But Does the LLM? Charting the Gaps Between Clinical Expert Needs and Unstructured Data Abstraction Tools

While clinicians knew where and how to annotate complex concepts in patient notes, in twelve of fourteen tasks they faced barriers to replicating those intuitions with LLMs, which highlighted open problems for HCI research to bridge the gaps between AI data work tools and clinical users' needs.

Venkatesh Sivaraman, Rigney E. Turnham, G. Bonano et al. · 0 citations

Review 2026

From Zero-Shot to Bedside: A Practical Playbook for Adapting Open-Source Large Language Models to Clinical Symptom Extraction

A playbook for fine-tuning LLMs on de-identified clinical notes from patients with pancreatic cancer, spanning both pre-diagnosis and on-treatment settings is presented, and the use of machine-generated annotations to augment limited expert labels is examined, showing that balanced mixtures of synthetic and human data...

Li-Ching Chen, T. Zack, Divneet Mandair et al. · Proceedings of machine learn... · 0 citations