# Dynamic Prediction of Alternating Recurrent Events via Neural Network

arXiv: https://arxiv.org/abs/2606.30889 · HTML: https://mlsanity.com/paper/2606.30889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [xLSTM: Extended Long Short-Term Memory](https://mlsanity.com/paper/2405.04517/) (2024)
- [DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values](https://mlsanity.com/paper/1908.02337/) (2019)
- [Random Forest for Dynamic Risk Prediction or Recurrent Events: A Pseudo-Observation Approach](https://mlsanity.com/paper/2312.00770/) (2023)
- [A Random Forest Inverse Probability Weighted Pseudo-Observation Framework for Alternating Recurrent Events](https://mlsanity.com/paper/2510.23764/) (2025)
- [Neural Networks as Linear Regression: An Introduction for Statisticians](https://mlsanity.com/paper/2606.23601/) (2026)

## Abstract (arXiv)

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.