背景:了解健康结果的多面性需要对社会,经济,以及塑造个人福祉的环境决定因素。在这些决定因素中,行为因素起着至关重要的作用,特别是精神活性物质的消费模式,这对公众健康有重要影响。全球疾病负担研究显示,由于物质使用,残疾调整寿命年的影响越来越大。成功识别患者的物质使用信息使临床护理团队能够更有效地解决与物质相关的问题,实现有针对性的支持并最终改善患者预后。
目的:传统的自然语言处理(NLP)方法在准确解析与药物使用相关的多种临床语言方面存在局限性。大型语言模型(LLM)通过适应不同的语言模式来克服这些挑战。本研究调查了生成性预训练变压器(GPT)模型的应用,在特定的GPT-3.5-用于提取烟草,酒精,在零射和少射学习环境中,来自患者出院总结的物质使用信息。这项研究通过展示高级语言模型在提取对增强患者护理至关重要的细微差别信息方面的潜力,为医疗保健信息学的发展做出了贡献。
方法:本文分析的主要数据源是重症监护医学信息集市III(MIMIC-III)数据集。在这个数据集中的所有笔记中,我们专注于出院总结。进行了及时的工程,涉及对不同提示的迭代探索。利用精心策划的例子和精致的提示,我们通过零射和少射提示策略来考察模型的熟练程度。
结果:所呈现的结果突出了GPT在提取提及烟草的文本跨度方面的对比性能,酒精,以及在零射和少射学习场景中的物质使用。在零射设置中,烟草提取的准确性,酒精,物质使用信息非常高。然而,在少数镜头设置中,精度大大降低。相反,与零射学习相比,少射学习导致物质使用状态的设计显著增加,召回率和F1得分显著增加.然而,这种改进是以降低精度为代价的,不仅是提到使用的文本跨度,而且是使用的状态。
结论:在精确提取提及物质使用的文本跨度方面的零射学习的卓越表现证明了其在全面回忆很重要的情况下的有效性。相反,当准确确定物质使用状态是主要焦点时,少量学习具有优势,即使它涉及精度的权衡。结果有助于加强早期发现和干预策略,更精确地定制治疗计划,最终,有助于全面了解患者的健康状况。通过将这些人工智能驱动的方法集成到电子健康记录系统中,临床医生可以立即获益,对物质使用的全面见解,从而形成不仅及时而且更加个性化和有效的干预措施。
背景:
BACKGROUND: Understanding the multifaceted nature of health outcomes requires a comprehensive examination of the social, economic, and environmental determinants that shape individual well-being. Among these determinants, behavioral factors play a crucial role, particularly the consumption patterns of psychoactive substances, which have important implications on public health. The Global Burden of Disease Study shows a growing impact in disability-adjusted life years due to substance use. The successful identification of patients\' substance use information equips clinical care teams to address substance-related issues more effectively, enabling targeted support and ultimately improving patient outcomes.
OBJECTIVE: Traditional natural language processing methods face limitations in accurately parsing diverse clinical language associated with substance use. Large language models offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of the generative pretrained transformer (GPT) model in specific GPT-3.5 for extracting tobacco, alcohol, and substance use information from patient discharge summaries in zero-shot and few-shot learning settings. This study contributes to the evolving landscape of health care informatics by showcasing the potential of advanced language models in extracting nuanced information critical for enhancing patient care.
METHODS: The main data source for analysis in this paper is Medical Information Mart for Intensive Care III data set. Among all notes in this data set, we focused on discharge summaries. Prompt engineering was undertaken, involving an iterative exploration of diverse prompts. Leveraging carefully curated examples and refined prompts, we investigate the model\'s proficiency through zero-shot as well as few-shot prompting strategies.
RESULTS: Results show GPT\'s varying effectiveness in identifying mentions of tobacco, alcohol, and substance use across learning scenarios. Zero-shot learning showed high accuracy in identifying substance use, whereas few-shot learning reduced accuracy but improved in identifying substance use status, enhancing recall and F1-score at the expense of lower precision.
CONCLUSIONS: Excellence of zero-shot learning in precisely extracting text span mentioning substance use demonstrates its effectiveness in situations in which comprehensive recall is important. Conversely, few-shot learning offers advantages when accurately determining the status of substance use is the primary focus, even if it involves a trade-off in precision. The results contribute to enhancement of early detection and intervention strategies, tailor treatment plans with greater precision, and ultimately, contribute to a holistic understanding of patient health profiles. By integrating these artificial intelligence-driven methods into electronic health record systems, clinicians can gain immediate, comprehensive insights into substance use that results in shaping interventions that are not only timely but also more personalized and effective.