Abstract
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased output variance, resulting in notably divergent outputs even when prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively curbs variance for various LLMs, providing a more uniform and dependable solution for summarizing vital medical information.
Abstract (translated)
电子健康记录(EHRs)存储了广泛的患者信息，包括医疗历史、诊断、治疗和测试结果。这些记录对于使医疗保健提供者做出关于护理的知情决策至关重要。总结临床笔记进一步协助医疗保健专业人员指出潜在的健康风险并做出更好的知情决策。这个过程有助于减少错误并增强患者的治疗效果，通过确保提供者访问最相关和最新的患者数据来实现。最近的研究表明，包括大型语言模型(LLMs)的提示极大地提高了摘要任务的有效性。然而，我们表明，这种方法也导致输出变异性增加，即使提示共享相似的含义，仍会导致显著的不同输出。为了应对这个挑战，我们引入了一种模型无关的软提示-基于校准(SPeC)管道，采用软提示以减少变异性，同时保留提示摘要的优势。对多个临床笔记任务和LLM的实验室发现表明，我们的方法不仅增强了性能，而且有效地限制了各种LLM的输出变异性，提供了一种更均匀和可靠的摘要重要医疗信息的解决方案。
URL
https://arxiv.org/abs/2303.13035
