用患者相似性建模提升数据少者的自杀行为预测精度。
Improving Forecasts of Suicide Attempts for Patients with Little Data
- 基于潜在相似性构建高斯过程，捕捉患者个体差异。
- 数据少的患者预测准确率显著提升，超越多数基线方法。
- 适合临床风险评估、精神健康监测等场景使用。
生态瞬间评估（Ecological Momentary Assessment）可提供自杀意念与行为的实时数据，但因事件罕见且患者差异大，预测仍具挑战。我们发现，统一模型在所有患者上表现不佳，而个性化模型虽有改进，却仍对数据少的患者过拟合。为此，提出潜在相似性高斯过程（Latent Similarity Gaussian Processes, LSGPs），通过挖掘相似患者的趋势，帮助数据少的个体提升预测能力。初步结果表明，无需手动设计核函数，其性能优于除一种基线外的所有方法，同时提供了对患者相似性的新理解。
原文摘要 · Abstract (English)
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.
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