A Retrieval-Based Approach for Automatic Interpretation of Multi-Analyte Laboratory Profiles.

IF 2.3 Q3 MEDICAL LABORATORY TECHNOLOGY
Thomas E Tavolara, Sarthak Khandelwal, Rachel Leger, Serena Navitskas, Rajiv Pruthi, Dong Chen, Jansen N Seheult
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引用次数: 0

Abstract

Background: Interpretation of multi-analyte laboratory profiles, such as lupus anticoagulant (LAC) testing, requires integration of numerous test results and clinical information. However, this process is time-consuming, requires domain knowledge, and has limited scalability. Given that rule-based systems are rigid and difficult to maintain and large language models pose risks of "hallucinations," we developed and evaluated a novel retrieval-based approach to automate LAC profile interpretation to improve efficiency while maintaining fidelity and reliability for clinical use.

Methods: Our method processes laboratory values through ensemble random forest (RF) models to retrieve the most similar expert interpretation from a curated database of 347 previously interpreted LAC profiles. The system's performance was assessed through technical accuracy metrics, a work flow efficiency timing study, and expert review of interpretation accuracy for held-out cases.

Results: Technical validation demonstrated high accuracy in retrieving correct interpretative elements (median: 93% accuracy, interquartile range [IQR]: 67% to 100%). Retrieval accuracy was highly correlated with the prevalence of a given element (ρ = 0.8307). In a timing study of 93 consecutive LAC profiles, ensemble-assisted interpretation achieved a median time of 22 s per report (IQR: 16 s to 31 s), representing a 78.6% total reduction compared to manual interpretation (median: 118 s, IQR: 96 s to 172 s; t = 18.61, P < 0.001). Furthermore, 92.5% of AI-assisted interpretations were completed within 60 s, exceeding the target of 85%. Expert review confirmed 100% concordance for LAC presence/absence.

Conclusions: The proposed approach significantly improves efficiency of complex laboratory interpretation for LAC profiles, while maintaining consistency and high fidelity. Future studies will explore the method's inherent extensibility to the interpretation of other multi-analyte laboratory profiles.

基于检索的多分析物实验室剖面自动解释方法。
背景:解释多分析物的实验室资料,如狼疮抗凝血剂(LAC)测试,需要整合大量的测试结果和临床信息。然而,这个过程耗时,需要领域知识,并且具有有限的可扩展性。鉴于基于规则的系统僵化且难以维护,并且大型语言模型会带来“幻觉”的风险,我们开发并评估了一种新的基于检索的方法来自动化LAC配置文件解释,以提高效率,同时保持临床使用的保真度和可靠性。方法:我们的方法通过集成随机森林(RF)模型处理实验室值,从347个先前解释的LAC概况的精心整理的数据库中检索最相似的专家解释。系统的性能通过技术精度指标、工作流程效率定时研究和专家对滞留案例解释精度的审查进行了评估。结果:技术验证表明,检索正确的解释元素具有很高的准确性(中位数:93%准确率,四分位数间距[IQR]: 67%至100%)。检索准确度与给定元素的流行率高度相关(ρ = 0.8307)。在一项对93个连续LAC剖面的时间研究中,集成辅助解释的平均时间为22秒(IQR: 16秒至31秒),与人工解释相比减少了78.6%(中位数:118秒,IQR: 96秒至172秒;t = 18.61, P < 0.001)。此外,92.5%的人工智能辅助口译在60秒内完成,超过了85%的目标。专家审查确认LAC存在/不存在100%一致。结论:该方法显著提高了LAC图谱复杂实验室解释的效率,同时保持了一致性和高保真度。未来的研究将探索该方法内在的可扩展性,以解释其他多分析物实验室概况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Applied Laboratory Medicine
Journal of Applied Laboratory Medicine MEDICAL LABORATORY TECHNOLOGY-
CiteScore
3.70
自引率
5.00%
发文量
137
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