# Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning

arXiv: https://arxiv.org/abs/2407.16715 · HTML: https://mlsanity.com/paper/2407.16715/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm](https://mlsanity.com/paper/2405.06652/) (2024)
- [Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.04148/) (2023)
- [Research on Image Recognition Technology Based on Multimodal Deep Learning](https://mlsanity.com/paper/2405.03091/) (2024)
- [Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures](https://mlsanity.com/paper/1903.04571/) (2019)

## Built on by (most cited first, 17 of 17)

- [Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning](https://mlsanity.com/paper/2411.06326/) (2024)
- [Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues](https://mlsanity.com/paper/2411.06374/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study](https://mlsanity.com/paper/2410.20792/) (2024)
- [Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis](https://mlsanity.com/paper/2410.24046/) (2024)
- [Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data](https://mlsanity.com/paper/2411.18622/) (2024)
- [Accurate Medical Named Entity Recognition Through Specialized NLP Models](https://mlsanity.com/paper/2412.08255/) (2024)
- [Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation](https://mlsanity.com/paper/2409.13868/) (2024)
- [Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature](https://mlsanity.com/paper/2409.14000/) (2024)
- [Optimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction](https://mlsanity.com/paper/2410.12642/) (2024)
- [Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems](https://mlsanity.com/paper/2410.14167/) (2024)
- [A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization](https://mlsanity.com/paper/2503.06888/) (2025)
- [Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks](https://mlsanity.com/paper/2412.18729/) (2024)
- [Intelligent logistics management robot path planning algorithm integrating transformer and GCN network](https://mlsanity.com/paper/2501.02749/) (2025)
- [Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling](https://mlsanity.com/paper/2508.00920/) (2025)
- [Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion](https://mlsanity.com/paper/2609.09801/) (2026)

## Abstract (arXiv)

In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results. Unified prediction of adverse drug reactions covered. Knowledge graph embedding technology can fuse the associated information between drugs and alleviate the shortcomings of high-dimensional sparsity in feature matrices, and the efficient training capabilities of deep learning can improve the prediction accuracy of the model. This article builds an adverse drug reaction knowledge graph based on drug feature data; by analyzing the embedding effect of the knowledge graph under different embedding strategies, the best embedding strategy is selected to obtain sample vectors; and then a convolutional neural network model is constructed to predict adverse reactions. The results show that under the DistMult embedding model and 400-dimensional embedding strategy, the convolutional neural network model has the best prediction effect; the average accuracy, F_1 score, recall rate and area under the curve of repeated experiments are better than the methods reported in the literature. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.