Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, Switzerland.
Neurology Department, Hephata Klinik, Schwalmstadt, Germany.
Sci Rep. 2018 Jul 13;8(1):10628. doi: 10.1038/s41598-018-28840-w.
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.
发作性睡病是一种罕见的终身疾病，存在两种形式，即 1 型（NT1）或 2 型（NT2），但只有 NT1 被认为是明确界定的实体。两种类型的发作性睡病都属于中枢性嗜睡症（CH）的范畴，这是一组定义不明确的疾病，以日间过度嗜睡为核心特征。由于症状的明显重叠和疾病的罕见性，很难确定 CH 的明显表型。机器学习（ML）可以帮助识别表型，因为它学会识别对人类来说不可见的临床特征。在这里，我们将 ML 应用于来自庞大的欧洲发作性睡病网络（EU-NN）的数据，其中包含数百种混合发作性睡病特征，难以用经典统计学进行分析。具有内置特征选择的监督学习模型随机梯度提升在测试集中取得了很高的性能。虽然猝倒特征被认为是最有影响力的预测因子，但机器还发现了其他特征，例如多次睡眠潜伏期试验的快速眼动睡眠潜伏期平均值有助于通过经典统计学分析来区分 NT1 和 NT2。我们的研究结果表明，ML 可以从复杂的数据库中识别出 CH 的机器特征，从而为未来的诊断分类提供“思路”和有希望的候选者。
