# Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning

arXiv: https://arxiv.org/abs/2104.01787 · HTML: https://mlsanity.com/paper/2104.01787/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Multi-layer Representation Learning for Medical Concepts](https://mlsanity.com/paper/1602.05568/) (2016)
- [Deepr: A Convolutional Net for Medical Records](https://mlsanity.com/paper/1607.07519/) (2016)
- [Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record](https://mlsanity.com/paper/1810.04793/) (2018)

## Built on by (most cited first, 3 of 3)

- [Personalized Event Prediction for Electronic Health Records](https://mlsanity.com/paper/2308.11013/) (2023)
- [Learning to Adapt Clinical Sequences with Residual Mixture of Experts](https://mlsanity.com/paper/2204.02687/) (2022)
- [Interaction Order Prediction for Temporal Graphs](https://mlsanity.com/paper/2302.02128/) (2023)

## Abstract (arXiv)

Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient's sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.