Sequential Anomaly Detection for Continuous Prediction of Unexpected ICU Admission From Emergency Department

Abstract

In the emergency department, accurately identifying patients who will require intensive care unit admission is crucial for effective care and resource allocation. Existing methods, such as triage judgments based on Emergency Severity Index, may not fully capture the dynamic nature of patient conditions, potentially overlooking critical risks. Therefore, it is essential to detect sequential deterioration in patients with initially underestimated triage scores. This study addresses the challenge of identifying patients who, despite being initially triaged as low-acuity, later require intensive care unit admission due to clinical deterioration. We propose an anomaly detection approach to continuously monitor patients for signs of deterioration. To achieve this, we employed a sequential anomaly detection model integrated with Long Short-Term Memory to capture temporal relationships within the data. Given the limitations of existing metrics for assessing clinical relevance, we validated the performance of our model by measuring its ability to correctly identify cases and its effectiveness in providing timely and actionable alerts to healthcare providers. Our model achieved a recall of 0.8299 and a precision of 0.8206, continuously alerting healthcare providers 36% of the time throughout the patient’s stay, with an average lead time of 3.13 hours.

Keywords

• Anomaly detection
• emergency department
• triage
• intensive care unit
• machine learning
• MIMIC-IV