发表机构
Wellesley College; Harvard University; Harvard Medical School; Massachusetts General Hospital（韦尔斯利学院; 哈佛大学; 哈佛医学院; 麻省总医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自杀风险预测中患者异质性和事件低发率问题，提出潜在相似性高斯过程，通过潜在空间建模相似性并选择性利用同伴信息，实证显示优于现有模型，尤其提升首次事件预测。
AI 中文摘要
由于患者的高度异质性和自杀相关事件（SREs）的低基础发生率，预测自杀风险十分困难。我们提出了潜在相似性高斯过程（LSGPs），该方法将患者嵌入到连续潜在空间中，以联合建模相似性并预测风险。通过有选择地从潜在同伴中提取信息，LSGPs能更好地捕捉个体化的风险轨迹，推广了非特指（合并）、个体（每患者）和层次化框架。我们的贡献包括：（1）一个可识别的双通道相似性核；（2）证明标准模型拟合算法——均值场变分推断——会使LSGPs退化为非特指模型，并提出了修复方法；（3）在密集纵向自杀数据上的实证结果显示，LSGPs在下周风险预测中优于非特指、个体和层次化模型，且在预测首次发生的SREs时提升最大。
英文摘要
Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Latent Similarity Gaussian Processes (LSGPs), which embed patients in a continuous latent space to jointly model similarity and forecast risk. By selectively drawing information from latent peers, LSGPs better capture individualized risk trajectories, generalizing nomothetic (pooled), idiographic (per-patient), and hierarchical frameworks. Our contributions are: (1) an identifiable two-channel Similarity Kernel; (2) proof that the standard model-fitting algorithm, mean-field variational inference, collapses LSGPs to nomothetic models, along with a fix; and (3) empirical results on intensive longitudinal suicide data showing LSGPs outperform nomothetic, idiographic, and hierarchical models for next-week risk forecasting, with the largest gains in forecasting first-occurrence SREs.
