(untitled) (Comprehensive Journal of Science, 2026)
Using the MOVER dataset, a novel Temporal Context-Aware LSTM (TC-LSTM) for multivariate time-series classification of crucial intraoperative events is presented in this study. TC-LSTM clearly captures inter-observation intervals, uses context-aware imputation for missing values, and applies temporal attention to emphasize clinically significant windows, in contrast to traditional recurrent models that assume regular sampling or neglect temporal gaps. This research study model outperforms LSTM (82.1%), GRU (83.4%), T-LSTM (85.5%), Neural ODEs (84.3%), and Transformers (85.0%) under identical patient-disjoint splits, achieving a macro-F1 score of 89.7% and AUC of 92.3% on 1,247 surgical cases with five expert-annotated event types. TC-LSTM’s ability to learn from sparse, irregular data without interpolation artifacts is demonstrated via the improvements, which are particularly noticeable for hemorrhage, a rare but high-mortality event, where it increases F1 via over 7 points relative to baselines. Each component contributes significantly, according to ablation experiments; performance is reduced via 2.4–4.7% when time embedding or attention are removed. Importantly, attention weights are in line with recognized hemodynamic antecedents, yet the architecture is nevertheless lightweight and comprehensible. This work fills a gap that is frequently overlooked in favor of architectural innovation via proposing a methodical, physiology-informed adaptation of current technologies to a real clinical situation rather than a new deep learning paradigm. In addition, The findings highlight the need of characterizing temporal irregularity as signal rather than noise for effective medical AI and set a new standard for time-series classification in operating room monitoring.