No More False Alert: Contrastive Learning for Predicting Health Deterioration from Imbalanced Care Records

In this paper, we propose an outcome-based contrastive loss for imbalanced binary classification to alert to next-day health deterioration using care records and meteorological data. Long-term care facilities maintain daily care and observation records to monitor the health of older adults. Such objective records are particularly valuable when sudden deterioration occurs, enabling timely coordination with medical institutions. Predicting deterioration one day in advance could provide care staff with an actionable window to intensify observation and adjust care plans (e.g., scheduling additional vital checks or increasing fluid intake monitoring). This could potentially reduce emergency transports and ease the burden on already understaffed care facilities. However, for such predictions to be useful in practice, false positives must be suppressed. Because deterioration events are rare, class imbalance generates an excess of false positives, causing alert fatigue and increasing the risk that actual events go unnoticed. To address these challenges, we propose an outcome-based contrastive loss that contrasts actual deteriorating samples against false alarms conditioned on mini-batch prediction outcomes. The proposed loss contracts same-label pairs to shape local structure within each ground-truth label. The loss also separates actual deteriorating samples from false alarms among samples predicted as deteriorating, thereby directly reducing unnecessary alerts. As a result, compared with random oversampling with standard cross-entropy, the proposed model improved precision from 3.97% to 12.94% (+8.97 percentage points), while limiting the F1-score decrease to 0.71 percentage points (from 7.28% to 6.57%). Pair-design ablations and UMAP projections supported this mechanism by indicating clearer separation between actually deteriorating and false-alarm samples in the learned representation space. These results suggest a viable direction for alert systems that produce fewer unnecessary alerts, reducing alert fatigue and supporting more reliable deterioration detection in care settings.