In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health records (EHR), here we propose an ML approach for phenotyping using routinely collected physiological time series data. Our new algorithm integrates Long Short-Term Memory (LSTM) networks with collaborative filtering concepts to identify common physiological states across patients. Tested on real-world ICU clinical data for intracranial hypertension (IH) detection in patients with brain injury, our method achieved an area under the curve (AUC) of 0.889 and average precision (AP) of 0.725. Moreover, our algorithm outperforms autoencoders in learning more structured latent representations of the physiological signals. These findings highlight the promise of our methodology for patient phenotyping, leveraging routinely collected multivariate time series to improve clinical care practices.

在重症监护室（ICU）中，多元时间序列的丰富性为机器学习提供了改进患者表型刻画的机会。与以往主要关注电子健康记录（EHR）的研究不同，本文提出了一种使用常规收集的生理时间序列数据进行表型刻画的机器学习方法。我们的新算法将长短期记忆（LSTM）网络与协同过滤的概念相结合，识别出患者之间共同的生理状态。在实际世界的ICU临床数据中测试，我们的方法在脑损伤患者颅内高压（IH）检测方面取得了0.889的曲线下面积（AUC）和0.725的平均精度（AP）。此外，我们的算法在学习生理信号的结构化潜在表示方面优于自编码器。这些发现突显了我们方法在利用常规多元时间序列改善临床护理实践的患者表型刻画方面的巨大潜力。

常规收集的多变量ICU生理信号中的共同潜在表示的协作学习