Classification of orthostatic intolerance through data analytics
Classification of orthostatic intolerance through data analytics
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DOI：
10.1007/s11517-021-02314-0
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发表时间：
2021-02-13
影响因子：
3.2
通讯作者：
Olufsen，Mette S.
中科院分区：
文献类型：
--
作者：
Gilmore，Steven;Hart，Joseph;Olufsen，Mette S.
自主神经系统的不平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）;这些都是常见的疾病，但难以正确诊断。对触发机制和潜在的病理生理学的不了解导致了其分类的变化。这项研究使用机器学习对直立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中的少量标记物，以及在头向上倾斜期间测量的心率时间序列数据，以（a）区分具有单一病理的患者和（B）检查具有混合病理生理学的患者的数据。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名受试者的临床数据，这些受试者被确定为对照组或患有以下四种疾病之一：体位性直立性心动过速（POTS），心脏抑制，血管抑制和混合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。我们能够将超过95%的患者归类为单一疾病，并能够将所有患者分为混合性心脏抑制性和血管抑制性晕厥。聚类结果确认了疾病组，并确定了两个不同的亚组内的控制和混合组。这项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的结构。该方法用于直立不耐受患者的分类。诊断立位不耐受是具有挑战性的，充分表征的病理生理机制仍然是一个正在进行的研究课题。这项研究提供了一个利用机器学习来帮助临床医生和研究人员在解决这些challenges.Graphical abstractionMachine学习工具用于分析晕厥和对照患者的心率（HR）和血压（BP）时间序列数据。结果表明，机器学习可以为98%的患者提供准确的疾病组分类，我们在对照患者中确定了两个亚组，并根据血压反应进行了区分。
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.Graphical abstractMachine learning tools utilized to analyze heart rate (HR) and blood pressure (BP) time-series data from syncope and control patients. Results show that machine learning can provide accurate classification of disease groups for 98% of patients and we identified two subgroups within the control patients differentiated by their BP response.