# StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation

arXiv: https://arxiv.org/abs/2308.16781 · HTML: https://mlsanity.com/paper/2308.16781/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [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)
- [Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax](https://mlsanity.com/paper/2006.10408/) (2020)
- [Hierarchical Gating Networks for Sequential Recommendation](https://mlsanity.com/paper/1906.09217/) (2019)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination](https://mlsanity.com/paper/1809.01852/) (2018)
- [A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation](https://mlsanity.com/paper/2010.15982/) (2020)
- [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)
- [SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation](https://mlsanity.com/paper/1710.05980/) (2017)
- [Drug Package Recommendation via Interaction-aware Graph Induction](https://mlsanity.com/paper/2102.03577/) (2021)
- [Multi-Scale Convolutional Neural Network for Automated AMD Classification using Retinal OCT Images](https://mlsanity.com/paper/2110.03002/) (2021)
- [Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation](https://mlsanity.com/paper/2204.06517/) (2022)
- [MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation](https://mlsanity.com/paper/2104.11026/) (2021)
- [Alleviating the Long-Tail Problem in Conversational Recommender Systems](https://mlsanity.com/paper/2307.11650/) (2023)
- [Drug-drug interaction prediction based on co-medication patterns and graph matching](https://mlsanity.com/paper/1902.08675/) (2019)

## Built on by (most cited first, 6 of 6)

- [CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state](https://mlsanity.com/paper/2404.12228/) (2024)
- [Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases](https://mlsanity.com/paper/2501.16373/) (2025)
- [CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning](https://mlsanity.com/paper/2403.00880/) (2024)
- [Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation](https://mlsanity.com/paper/2504.18096/) (2025)
- [CafeMed: Causal Attention Fusion Enhanced Medication Recommendation](https://mlsanity.com/paper/2511.14064/) (2025)
- [HADRec: A Hierarchy-Aware Drug Recommendation Framework by Fusing Molecular Knowledge and Electronic Health Record](https://mlsanity.com/paper/2610.00984/) (2026)

## Abstract (arXiv)

With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. As a sub-domain, medication recommendation aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing works ignore the inherent long-tailed distribution of medical data, have uneven learning strengths for hot and sparse data, and fail to balance safety and accuracy. To address the above limitations, we propose StratMed, which introduces a stratification strategy that overcomes the long-tailed problem and achieves fuller learning of sparse data. It also utilizes a dual-property network to address the issue of mutual constraints on the safety and accuracy of medication combinations, synergistically enhancing these two properties. Specifically, we construct a pre-training method using deep learning networks to obtain medication and disease representations. After that, we design a pyramid-like stratification method based on relevance to strengthen the expressiveness of sparse data. Based on this relevance, we design two graph structures to express medication safety and precision at the same level to obtain patient representations. Finally, the patient's historical clinical information is fitted to generate medication combinations for the current health condition. We employed the MIMIC-III dataset to evaluate our model against state-of-the-art methods in three aspects comprehensively. Compared to the sub-optimal baseline model, our model reduces safety risk by 15.08%, improves accuracy by 0.36%, and reduces training time consumption by 81.66%.