Collaborative learning of common latent representations in routinely
  collected multivariate ICU physiological signals
Hollan Haule Ian Piper Patricia Jones Tsz-Yan Milly Lo Javier Escudero
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.