# Change Matters: Medication Change Prediction with Recurrent Residual Networks

arXiv: https://arxiv.org/abs/2105.01876 · HTML: https://mlsanity.com/paper/2105.01876/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 9 of 9)

- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Pre-training of Graph Augmented Transformers for Medication Recommendation](https://mlsanity.com/paper/1906.00346/) (2019)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination](https://mlsanity.com/paper/1809.01852/) (2018)
- [Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning](https://mlsanity.com/paper/1802.00662/) (2018)
- [Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data](https://mlsanity.com/paper/1902.01506/) (2019)
- [SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation](https://mlsanity.com/paper/1710.05980/) (2017)
- [A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns](https://mlsanity.com/paper/1610.08115/) (2016)
- [PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records](https://mlsanity.com/paper/2008.13569/) (2020)

## Built on by (most cited first, 30 of 38)

- [Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time](https://mlsanity.com/paper/2211.14238/) (2022)
- [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://mlsanity.com/paper/2305.12788/) (2023)
- [Conditional Generation Net for Medication Recommendation](https://mlsanity.com/paper/2202.06588/) (2022)
- [Large Language Model Distilling Medication Recommendation Model](https://mlsanity.com/paper/2402.02803/) (2024)
- [ManyDG: Many-domain Generalization for Healthcare Applications](https://mlsanity.com/paper/2301.08834/) (2023)
- [Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients](https://mlsanity.com/paper/2403.17745/) (2024)
- [VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation](https://mlsanity.com/paper/2312.12100/) (2023)
- [SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation](https://mlsanity.com/paper/2309.05675/) (2023)
- [CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state](https://mlsanity.com/paper/2404.12228/) (2024)
- [Contrastive Learning on Medical Intents for Sequential Prescription Recommendation](https://mlsanity.com/paper/2408.10259/) (2024)
- [StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation](https://mlsanity.com/paper/2308.16781/) (2023)
- [RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment](https://mlsanity.com/paper/2412.12475/) (2024)
- [Multi-task Heterogeneous Graph Learning on Electronic Health Records](https://mlsanity.com/paper/2408.07569/) (2024)
- [Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases](https://mlsanity.com/paper/2501.16373/) (2025)
- [Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs](https://mlsanity.com/paper/2502.13555/) (2025)
- [Fine-grained List-wise Alignment for Generative Medication Recommendation](https://mlsanity.com/paper/2505.20218/) (2025)
- [Natural Language-Assisted Multi-modal Medication Recommendation](https://mlsanity.com/paper/2501.07166/) (2025)
- [CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning](https://mlsanity.com/paper/2403.00880/) (2024)
- [HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships](https://mlsanity.com/paper/2603.18459/) (2026)
- [DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning](https://mlsanity.com/paper/2305.19604/) (2023)
- [ACDNet: Attention-guided Collaborative Decision Network for Effective Medication Recommendation](https://mlsanity.com/paper/2307.03332/) (2023)
- [OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation](https://mlsanity.com/paper/2401.15814/) (2024)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [ARMR: Adaptively Responsive Network for Medication Recommendation](https://mlsanity.com/paper/2507.04428/) (2025)
- [MediRec: Enhancing Chinese Medication Recommendation with Explainable Clinical Reasoning](https://mlsanity.com/paper/2510.21084/) (2025)
- [User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filtering](https://mlsanity.com/paper/2601.22820/) (2026)
- [Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care](https://mlsanity.com/paper/2606.31036/) (2026)
- [RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation](https://mlsanity.com/paper/2610.14474/) (2026)
- [ALGNet: Attention Light Graph Memory Network for Medical Recommendation System](https://mlsanity.com/paper/2312.08377/) (2023)
- [MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation](https://mlsanity.com/paper/2408.01445/) (2024)

## Abstract (arXiv)

Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all medications for the current visit, which often overlaps with medications from previous visits. A more clinically relevant task is to identify medication changes. In this paper, we propose a new recurrent residual network, named MICRON, for medication change prediction. MICRON takes the changes in patient health records as input and learns to update a hidden medication vector and the medication set recurrently with a reconstruction design. The medication vector is like the memory cell that encodes longitudinal information of medications. Unlike traditional methods that require the entire patient history for prediction, MICRON has a residual-based inference that allows for sequential updating based only on new patient features (e.g., new diagnoses in the recent visit) more efficiently. We evaluated MICRON on real inpatient and outpatient datasets. MICRON achieves 3.5% and 7.8% relative improvements over the best baseline in F1 score, respectively. MICRON also requires fewer parameters, which significantly reduces the training time to 38.3s per epoch with 1.5x speed-up.