Attention-Guided Autoencoder for Retrospective Physiological Anomaly Detection in ICU Vital Signs Using MIMIC-IV Demo Data
Year: 2026
Type: article
Abstract: This study evaluates threshold-defined physiological anomaly screening in retrospective vital-sign data from the MIMIC-IV Clinical Database Demo v2.2. Six vital signs were temporally aligned; Fahrenheit temperature values were converted to Celsius, physiologically implausible measurements were converted to missing values before within-stay forward/backward filling, and remaining incomplete rows were removed. After cleaning, the ICU stays were separated at stay level into 82 training, 28 validation, and 28 test stays. These partitions produced 9,210, 2,784, and 5,183 windows, respectively. Only normal windows from the training partition were used to fit the neural models, while labeled validation windows were used to choose the operating thresholds. Across five seeds, AGAE achieved accuracy 0.7838, precision 0.3417, recall 0.4954, specificity 0.8335, F1 0.4025, ROC-AUC 0.7709, and PR-AUC 0.3519. The threshold-independent results for AGAE and LSTM-AE were almost the same (ROC-AUC 0.7709 versus 0.7722; PR-AUC 0.3519 versus 0.3530). At the selected operating points, AGAE produced higher accuracy, precision, and specificity and fewer false positives; LSTM-AE produced higher recall and fewer false negatives. Taken together, these results do not show a consistent advantage for AGAE over LSTM-AE, and AGAE recall remained moderate. Prospective streaming and clinical validation are required before any real-time use.
Institutions: Diponegoro University, Universitas Dian Nuswantoro
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W7223446713