Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing.
Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing.
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DOI：
10.1109/jbhi.2022.3163402
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发表时间：
2022-08
影响因子：
7.7
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中科院分区：
文献类型：
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心肺运动测试（CPET）是一种独特的生理医学测试，用于评估人体对渐进最大运动压力的反应。根据偏离正常生理反应的程度和类型，CPET 可以帮助确定患者运动的具体限制，以指导临床护理，而无需其他昂贵的侵入性诊断测试。然而，考虑到从 CPET 获得的数据的数量和复杂性，测试结果的解释和可视化具有挑战性。目前，CPET 数据需要专门的培训和丰富的经验才能对临床医生进行正确的解释。为了让临床医生更容易使用 CPET，我们研究了一种使用机器学习算法的简化数据解释和可视化工具。可视化显示三种类型的限制（心脏、肺部和其他）；值是根据三个独立的随机森林分类器的结果定义的。为了显示模型的分数并使临床医生可以解释它们，开发了一个包含分数和可解释性图的交互式仪表板。该机器学习平台有潜力增强现有的诊断程序，并提供一种工具，使临床医生更容易使用 CPET。
Cardiopulmonary Exercise Testing (CPET) is a unique physiologic medical test used to evaluate human response to progressive maximal exercise stress. Depending on the degree and type of deviation from the normal physiologic response, CPET can help identify a patient’s specific limitations to exercise to guide clinical care without the need for other expensive and invasive diagnostic tests. However, given the amount and complexity of data obtained from CPET, interpretation and visualization of test results is challenging. CPET data currently require dedicated training and significant experience for proper clinician interpretation. To make CPET more accessible to clinicians, we investigated a simplified data interpretation and visualization tool using machine learning algorithms. The visualization shows three types of limitations (cardiac, pulmonary and others); values are defined based on the results of three independent random forest classifiers. To display the models’ scores and make them interpretable to the clinicians, an interactive dashboard with the scores and interpretability plots was developed. This machine learning platform has the potential to augment existing diagnostic procedures and provide a tool to make CPET more accessible to clinicians.