Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. We use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges.

自主神经系统失衡可导致直立性不耐受，表现为头晕、头晕和突然失去意识（晕厥）；这些是常见的情况，但要正确诊断它们具有挑战性。触发机制和潜在病理生理学的不确定性导致了其分类的变化。本研究使用机器学习对直立性不耐受患者进行分类。我们使用随机森林分类树来识别血压和心率时间序列数据中的少量标志物在平视倾斜期间测量到（a）区分具有单一病理的患者和（b）检查来自具有单一病理的患者的数据混合病理生理学。接下来，我们使用 Kmeans 对表示时间序列数据的标记进行聚类。我们应用所提出的方法分析了 186 名被确定为对照或患有以下四种情况之一的受试者的临床数据：体位性直立性心动过速 (POTS)、心脏抑制、血管抑制以及混合型心脏抑制和血管抑制。分类结果证实了监督机器学习的使用。我们能够对超过 95% 的单一病症患者进行分类，并且能够将所有患有心脏抑制性和血管减压性混合性晕厥的患者进行亚组。聚类结果确认了疾病组并确定了对照组和混合组中的两个不同的亚组。拟议的研究展示了如何使用机器学习来发现血压和心率时间序列数据中的结构。该方法用于对直立不耐受患者进行分类。直立性不耐受的诊断具有挑战性，病理生理机制的完整表征仍然是正在进行的研究课题。这项研究为利用机器学习帮助临床医生和研究人员应对这些挑战迈出了一步。