用神经网络动态预测交替复发事件，提升医学心理状态预测精度
Dynamic Prediction of Alternating Recurrent Events via Neural Network
- 基于逆概率加权伪观测构建神经网络模型
- 在模拟与医学生情绪低谷预测中表现优异
- 适合需连续监测复发事件的临床研究者
交替复发事件——特定类型事件发生后引发次生抑制期——广泛存在于行为科学、刑事司法和生物统计学等领域。此类事件分析需关注数据相关性、重复结果及潜在删失问题。本文提出一种适用于在线动态预测的框架，结合神经网络理论与逆概率加权伪观测方法，用于预测后续交替复发事件的无事件时间。所提模型在仿真中表现良好，在预测一年级医学生低落情绪周期方面展现出卓越能力。最后进行讨论。
原文摘要 · Abstract (English)
Alternating recurrent events -- event-times of a specific nature that trigger a secondary refractory period -- occur in a wide-range of fields, including behavioral science, criminal justice, and biostatistics. Analysis of these events requires careful attention to the statistical nuance, including correlated observations and repeated outcomes subject to potential censoring. We develop an online dynamic prediction framework appropriate for predicting subsequent alternating recurrent events, by developing neural network theory for a statistical audiences and applying inverse probability weighted pseudo-observations. The proposed model is applied to dynamically predict alternating recurrent event-free time, showing good performance in simulation, and outstanding capability in application to predicting periods of low mood for first-year medical residents. We close with a discussion.
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