机器学习对重症监护结果进行基准测试。

IF 2.3 Q3 MEDICAL INFORMATICS
Healthcare Informatics Research Pub Date : 2023-10-01 Epub Date: 2023-10-31 DOI:10.4258/hir.2023.29.4.301
Louis Atallah, Mohsen Nabian, Ludmila Brochini, Pamela J Amelung
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引用次数: 0

摘要

目的:提高重症监护疗效包括评估和改进系统功能。基准测试是将结果与标准进行回顾性比较,有助于进行风险调整评估,并帮助医疗保健提供者根据观察到的和预测的结果确定需要改进的领域。在过去的二十年中,使用机器学习(ML)进行临床结果预测的几个模型得到了发展。ML是人工智能的一个领域,专注于创建算法,使计算机能够从数据中学习并根据数据做出预测或决策。本综述以关键发现和结果为中心,以帮助临床医生和研究人员选择使用ML进行重症监护基准测试的最佳方法。方法:我们使用PubMed检索2003年至2023年关于使用ML预测死亡率(592篇文章)、住院时间(143篇文章)或机械通气(195篇文章)的文献。我们用b谷歌Scholar作为PubMed搜索的补充,确保包含相关文章。考虑到叙事风格,队列中的论文是手动整理的,以便全面的读者视角。结果:我们的报告展示了基准结果的比较结果,并强调了特征类型、预处理、模型选择和验证方面的进展。它展示了ML有效解决重症监护结果预测挑战的实例,包括非线性关系、类别不平衡、数据缺失和文档可变性,从而提高了结果。结论:尽管机器学习提供了新的工具来改善重症监护结果的基准,但需要进一步研究的领域包括类别不平衡、公平性、改进的校准、可推广性和已发表模型的长期验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning for Benchmarking Critical Care Outcomes.

Objectives: Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative review centers on key discoveries and outcomes to aid clinicians and researchers in selecting the optimal methodology for critical care benchmarking using ML.

Methods: We used PubMed to search the literature from 2003 to 2023 regarding predictive models utilizing ML for mortality (592 articles), length of stay (143 articles), or mechanical ventilation (195 articles). We supplemented the PubMed search with Google Scholar, making sure relevant articles were included. Given the narrative style, papers in the cohort were manually curated for a comprehensive reader perspective.

Results: Our report presents comparative results for benchmarked outcomes and emphasizes advancements in feature types, preprocessing, model selection, and validation. It showcases instances where ML effectively tackled critical care outcome-prediction challenges, including nonlinear relationships, class imbalances, missing data, and documentation variability, leading to enhanced results.

Conclusions: Although ML has provided novel tools to improve the benchmarking of critical care outcomes, areas that require further research include class imbalance, fairness, improved calibration, generalizability, and long-term validation of published models.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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