CLEAR-Shock: Contrastive LEARning for Shock

Abstract

Shock is a life-threatening condition characterized by generalized circulatory failure, which can have devastating consequences if not promptly treated. Thus, early prediction and continuous monitoring of physiological signs are essential for timely intervention. While previous machine learning research in clinical settings has primarily focused on predicting the onset of deteriorating events, the importance of monitoring the ongoing state of a patient's condition post-onset has often been overlooked. In this study, we introduce a novel analytical framework for a prognostic monitoring system that offers hourly predictions of shock occurrence within the next 8 hours preceding its onset, along with forecasts regarding the likelihood of shock continuation within the subsequent hour post-shock occurrence. We categorize the patient's physiological states into four cases: pre-shock (non-shock or shock within the next 8 hours) and post-shock onset (continuation or improvement of shock within the next hour). To effectively predict these cases, we adopt supervised contrastive learning, enabling differential representation in latent space for training a predictive model. Additionally, to extract effective contrastive embeddings, we incorporate a feature tokenizer transformer into our approach. Our framework demonstrates improved predictive performance compared to baseline models when utilizing contrastive embeddings, validated through both internal and external datasets. Clinically, our system significantly improved early detection by identifying shock on average 6 hours before its onset. This framework not only provides early predictions of shock likelihood but also offers real-time assessments of shock persistence risk, thereby facilitating early prevention and evaluation of treatment effectiveness.