Ecological Momentary Assessment provides real-time data on suicidal thoughts and behaviors, but predicting suicide attempts remains challenging due to their rarity and patient heterogeneity. We show that single models fit to all patients perform poorly, while individualized models improve performance but still overfit to patients with limited data. To address this, we introduce Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity, enabling those with little data to leverage similar patients' trends. Preliminary results show promise: even without kernel-design, we outperform all but one baseline while offering a new understanding of patient similarity.

翻译：生态瞬时评估为自杀意念与行为提供了实时数据，但由于自杀企图的罕见性和患者异质性，其预测仍具挑战性。我们发现，适用于所有患者的单一模型表现不佳，而个体化模型虽能提升性能，却仍对数据有限的患者存在过拟合问题。为此，我们引入潜在相似性高斯过程（LSGPs）以捕捉患者异质性，使数据稀少的患者能够借鉴相似患者的趋势。初步结果显示该方法前景良好：即使未进行核函数设计，我们的方法仍优于除一个基线外的所有对比模型，同时为理解患者相似性提供了新视角。