# BernGraph: Probabilistic Graph Neural Networks for EHR-based Medication Recommendations

arXiv: https://arxiv.org/abs/2408.09410 · HTML: https://mlsanity.com/paper/2408.09410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination](https://mlsanity.com/paper/1809.01852/) (2018)
- [Deepr: A Convolutional Net for Medical Records](https://mlsanity.com/paper/1607.07519/) (2016)
- [E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT](https://mlsanity.com/paper/2103.16329/) (2021)
- [Beyond One-hot Encoding: lower dimensional target embedding](https://mlsanity.com/paper/1806.10805/) (2018)
- [Change Matters: Medication Change Prediction with Recurrent Residual Networks](https://mlsanity.com/paper/2105.01876/) (2021)
- [Conditional Generation Net for Medication Recommendation](https://mlsanity.com/paper/2202.06588/) (2022)
- [Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning](https://mlsanity.com/paper/1802.00662/) (2018)
- [Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation](https://mlsanity.com/paper/2302.11231/) (2023)

## Abstract (arXiv)

The medical community believes binary medical event outcomes in EHR data contain sufficient information for making a sensible recommendation. However, there are two challenges to effectively utilizing such data: (1) modeling the relationship between massive 0,1 event outcomes is difficult, even with expert knowledge; (2) in practice, learning can be stalled by the binary values since the equally important 0 entries propagate no learning signals. Currently, there is a large gap between the assumed sufficient information and the reality that no promising results have been shown by utilizing solely the binary data: visiting or secondary information is often necessary to reach acceptable performance. In this paper, we attempt to build the first successful binary EHR data-oriented drug recommendation system by tackling the two difficulties, making sensible drug recommendations solely using the binary EHR medical records. To this end, we take a statistical perspective to view the EHR data as a sample from its cohorts and transform them into continuous Bernoulli probabilities. The transformed entries not only model a deterministic binary event with a distribution but also allow reflecting event-event relationship by conditional probability. A graph neural network is learned on top of the transformation. It captures event-event correlations while emphasizing event-to-patient features. Extensive results demonstrate that the proposed method achieves state-of-the-art performance on large-scale databases, outperforming baseline methods that use secondary information by a large margin. The source code is available at urlhttps://github.com/chenzRG/BEHRMecom