{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/prompting-large-language-models-for-zero-shot-1","title":"Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data","arxiv_id":"2402.01713","date":"2024-01-25","proceeding":null,"authors":["Yinghao Zhu","Zixiang Wang","Junyi Gao","Yuning Tong","Jingkun An","Weibin Liao","Ewen M. Harrison","Liantao Ma","Chengwei Pan"],"abstract":"The inherent complexity of structured longitudinal Electronic Health Records (EHR) data poses a significant challenge when integrated with Large Language Models (LLMs), which are traditionally tailored for natural language processing. Motivated by the urgent need for swift decision-making during new disease outbreaks, where traditional predictive models often fail due to a lack of historical data, this research investigates the adaptability of LLMs, like GPT-4, to EHR data. We particularly focus on their zero-shot capabilities, which enable them to make predictions in scenarios in which they haven't been explicitly trained. In response to the longitudinal, sparse, and knowledge-infused nature of EHR data, our prompting approach involves taking into account specific EHR characteristics such as units and reference ranges, and employing an in-context learning strategy that aligns with clinical contexts. Our comprehensive experiments on the MIMIC-IV and TJH datasets demonstrate that with our elaborately designed prompting framework, LLMs can improve prediction performance in key tasks such as mortality, length-of-stay, and 30-day readmission by about 35\\%, surpassing ML models in few-shot settings. Our research underscores the potential of LLMs in enhancing clinical decision-making, especially in urgent healthcare situations like the outbreak of emerging diseases with no labeled data. The code is publicly available at https://github.com/yhzhu99/llm4healthcare for reproducibility.","url_abs":"https://arxiv.org/abs/2402.01713v2","url_pdf":"https://arxiv.org/pdf/2402.01713v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"prompting-large-language-models-for-zero-shot-1","repo_url":"https://github.com/yhzhu99/llm4healthcare","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"in-context-learning","task_name":"In-Context Learning"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"focus","method_name":"Focus"},{"method_slug":"gpt-4","method_name":"GPT-4"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.01713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01713"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yhzhu99/llm4healthcare","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":1,"ran":3,"ran_violates":1},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"20bb0ff4a2d77a03","entry":"attention","repo":"yhzhu99/llm4healthcare","repo_kind":"official","path":"AICare-baselines/models/aicare.py","file_url":"https://github.com/yhzhu99/llm4healthcare/blob/HEAD/AICare-baselines/models/aicare.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"20bb0ff4a2d77a03"}},{"code_sha256_prefix":"4923f2260b53bf04","entry":"clones","repo":"yhzhu99/llm4healthcare","repo_kind":"official","path":"AICare-baselines/models/aicare.py","file_url":"https://github.com/yhzhu99/llm4healthcare/blob/HEAD/AICare-baselines/models/aicare.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4923f2260b53bf04"}},{"code_sha256_prefix":"7b30edc2eaf0eca3","entry":"get_multitask_loss","repo":"yhzhu99/llm4healthcare","repo_kind":"official","path":"AICare-baselines/losses/multitask_loss.py","file_url":"https://github.com/yhzhu99/llm4healthcare/blob/HEAD/AICare-baselines/losses/multitask_loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7b30edc2eaf0eca3"}},{"code_sha256_prefix":"4e87d83c491e7a1a","entry":"get_time_aware_loss","repo":"yhzhu99/llm4healthcare","repo_kind":"official","path":"AICare-baselines/losses/time_aware_loss.py","file_url":"https://github.com/yhzhu99/llm4healthcare/blob/HEAD/AICare-baselines/losses/time_aware_loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4e87d83c491e7a1a"}},{"code_sha256_prefix":"dd03765c7da9ca51","entry":"subsequent_mask","repo":"yhzhu99/llm4healthcare","repo_kind":"official","path":"AICare-baselines/models/aicare.py","file_url":"https://github.com/yhzhu99/llm4healthcare/blob/HEAD/AICare-baselines/models/aicare.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"dd03765c7da9ca51"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}