TEMPORAL LABEL SMOOTHING FOR EARLY PREDICTION OF ADVERSE EVENTS Anonymous authors Paper under double-blind review

Abstract

Models that can predict adverse events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging machine learning task remains typically treated as simple binary classification, with few bespoke methods proposed to leverage temporal dependency across samples. We propose Temporal Label Smoothing (TLS), a novel learning strategy that modulates smoothing strength as a function of proximity to the event of interest. This regularization technique reduces model confidence at the class boundary, where the signal is often noisy or uninformative, thus allowing training to focus on clinically informative data points away from this boundary region. From a theoretical perspective, we also show that our method can be framed as an extension of multi-horizon prediction, a learning heuristic proposed in other early prediction work. TLS empirically matches or outperforms all competitor methods on various early prediction benchmark tasks. In particular, our approach significantly improves performance on clinically-relevant metrics such as event recall at low false-alarm rates.

1. INTRODUCTION

Early prediction of adverse events is key to safetycritical operations such as clinical care [1] or environmental monitoring [2] . In particular, adverse event prediction is highly relevant to clinical decisionmaking, as the deployment of in-patient risk stratification models can significantly improve patient outcomes and facilitate resource planning [1] . For instance, the National Early Warning Score (NEWS), a simple rule-based model predicting acute deterioration in critical care units, has been demonstrated to reduce in-patient mortality [3; 4] . 2b , our method is designed to reduce model confidence with stronger smoothing at the class boundary, allowing training to focus on more clinically informative data points away from this noisily labeled region. Contributions. The contributions of our work are threefold: (i) In Section 3.2, we introduce a novel label smoothing methodfoot_0 , which leverages the temporal structure of early prediction tasks to focus training and model confidence on areas with a stronger predictive signal. (ii) In Section 5, we show that our approach improves prediction performance over previously proposed objectives, particularly for clinically relevant criteria. (iii) In Section 3.3, we bridge the gap between prior work on multi-horizon prediction (MHP) [8] and label smoothing [14] by showing the former is equivalent to a special case of TLS under reasonable assumptions that we verify empirically. Figure 2 : Illustration of temporal label smoothing for early prediction of adverse events. Predictions are carried out over a horizon h and t e is the time of the next event, shaded in grey. True labels in black. (a) Model confusion is highest near the label boundary t e -h (maximum false positive, FPR, and minimum true positive rates, TPR), while performance is best close to event occurrence (t e ) and away from it (t e -2h). This motivates greater smoothing near t e -h. Metrics are computed over four-hour bins based on a 50% precision threshold. (b) γ controls the smoothing strength of surrogate labels q T LS .

2. RELATED WORK

Recent years have seen the development of custom machine learning methods to predict expected patient evolution and support clinical decision-making [15; 16; 17; 7] . Amongst these, early prediction of adverse clinical events is a particularly complex task due to their typically rare occurrence and noisy label definition, which induces challenging, highly imbalanced datasets for model training [8] . As a result, prediction systems often suffer from high false-alarm rates with limited usefulness in the clinical context [1] . Prior works on early event prediction have adopted various approaches to tackle this issue, which we compare in Table 1 and formalize in Appendix A.4. We also discuss similarities and distinctions between our task and the frameworks of early time-series classification and survival analysis [18] 



All code is made publicly available at https://anonymous.4open.science/r/tls/.



Figure 1: Early prediction task.

Under review a conference paper at ICLR 2023 boundary, corresponding to the prediction horizon before the event. Data in this boundary region dominates the loss but may not be clinically discriminative of patient deterioration patterns. Motivated by this observation, we propose Temporal Label Smoothing (TLS), a novel regularization strategy making label smoothing [14] time-dependent to better match prediction uncertainty patterns over time. As visualized in Figure

in Appendix A.3.Learning objectives for imbalanced datasets. Class imbalance is often addressed through loss reweighting techniques. Static class reweighting was used for sepsis or circulatory failure prediction [17; 7] through a balanced cross-entropy, which assigns a higher weight to samples from the minority class[19]. Still, performance improvements with this objective remain limited on highly imbalanced prediction tasks[20]. In contrast, dynamic reweighting methods such as focal loss and extensions[21; 22]  induce a learning bias towards samples with high model uncertainty, typically harder to classify. This approach can improve the prediction of disease progression from imbalanced datasets[23]  but does not consider patterns of sample informativeness over time.

