Background

Effective management of Mechanical Ventilation (MV) is vital for reducing morbidity, mortality, and cost of healthcare.

Objective

This study aims to synthesize evidence for effective MV management through Intelligent decision support (IDS) with Machine Learning (ML).

Method

Databases that include EBSCO, IEEEXplore, Google Scholar, SCOPUS, and the Web of Science were systematically searched to identify studies on IDS for effective MV management regarding Tidal Volume (TV), asynchrony, weaning, and other outcomes such as the risk of Prolonged Mechanical ventilation (PMV). The quality of the articles identified was assessed with a modified Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sessional research.

Results

A total of 26 articles were identified for the study that has IDS for TV (n = 2, 7.8 %), asynchrony (n = 9, 34.6 %), weaning (n = 12, 46.2 %), and others (n = 3, 11.5 %). It was affirmed that implementing IDS in MV management will enhance seamless ICU patient management following the utilization of various Machine Learning (ML) algorithms in decision support. The studies relied on (n = 14) ML algorithms to predict the TV, asynchrony, weaning, risk of PMV and Positive End-Expiratory Pressure (PEEP) changes of 11–20262 ICU patients records with model inputs ranging from (n = 1) for timeseries analysis of TV to (n = 47) for weaning prediction.

Conclusions

The small data size, poor study design, and result reporting, with the heterogeneity of techniques used in the various studies, hampered the development of a unified approach for managing MV efficiency in TV monitoring, asynchrony, and weaning predictions. Notwithstanding, the ensemble model was able to predict TV, asynchrony, and weaning to a higher accuracy than the other algorithms.

中文翻译：

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机器学习的智能决策支持，可在重症监护室有效管理机械通气–关键概述

背景

机械通气（MV）的有效管理对于降低发病率，死亡率和医疗保健成本至关重要。

客观的

这项研究旨在通过具有机器学习（ML）的智能决策支持（IDS）来为有效的MV管理综合证据。

方法

系统地搜索了包括EBSCO，IEEEXplore，Google Scholar，SCOPUS和Web of Science在内的数据库，以识别关于IDS的研究，以进行有效的MV管理，涉及潮气量（TV），异步，断奶以及其他后果，例如长期机械风险通风（PMV）。通过修改后的乔安娜·布里格斯研究所（JBI）的关键评估清单对跨会期研究进行评估，对所鉴定文章的质量进行了评估。

结果

该研究共鉴定出26篇文章，这些文章具有电视IDS（n = 2，7.8％），异步（n = 9、34.6％），断奶（n = 12、46.2％）和其他文章（n = 3 ，占11.5％）。可以肯定的是，在决策支持中使用各种机器学习（ML）算法之后，在MV管理中实施IDS将增强无缝的ICU患者管理。研究依靠（n = 14）ML算法来预测11-20262 ICU患者记录的电视，异步，断奶，PMV风险和呼气末正压（PEEP）变化，模型输入范围为（n = 1）用于电视的时间序列分析，以（n = 47）进行断奶预测。

结论

数据量小，研究设计差，结果报告差，以及各种研究中使用的技术的异质性，阻碍了统一方法的发展，该方法用于管理电视监视，异步和断奶预测中的MV效率。尽管如此，集成模型仍能够预测电视，异步和断奶，其准确性要高于其他算法。