Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Kwak, Heeyoung; Lee, Minwoo; Yoon, Seunghyun; Chang, Jooyoung; Park, Sang Min; Jung, Kyomin

doi:10.1007/978-3-030-47436-2_48

Cited by 18 publications

(17 citation statements)

References 20 publications

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“…Recent advancement in Graph Neural Network (GNN) has led to an increasing interest in using knowledge bases for ADE prediction as GNN has achieved superior performance compared with other machine learning algorithms. In more recent works, the graph structures of knowledge bases were integrated with RWD to enhance the causal interpretability of ADE detection [ 63 ].…”

Section: Data Sources For Pharmacovigilancementioning

confidence: 99%

“…May undermine machine learning model transportability 6. It will take a long time for data collection, thus there may be a delay in detection of ADEs Disproportionality [ 27 , 28 , 31 , 32 ] Network analysis [ 12 ] Clustering [ 11 ] SVM, Bayesian classifier, decision tree and/or Random Forest [ 71 , 72 ] RWD (EHRs and registries) Disproportionality [ 6 , 7 ] Drug–event pair extraction [ 6 , 7 , 73 ] ADE detection [ 36 , 41 – 44 , 64 , 74 ] ADE prediction (post-marketing) [ 63 , 75 ] Advantages: 1. Provides a population denominator who has taken the same medications, which enables adoption of study designs for causal effect estimation 2.…”

Section: Data Sources For Pharmacovigilancementioning

confidence: 99%

“…Thus, EHRs work better in inpatient ADE detection than outpatient. May diminish generalizability of machine learning models Cohort/case-based study [ 41 , 42 , 74 ] Sequence/temporal analysis [ 36 , 43 , 44 ] SVM, Bayesian classifier, decision tree and/or Random Forest [ 75 , 76 ] NLP relation extraction [ 10 , 73 ] Neural network [ 63 ] Social media Association rule mining [ 46 ] Drug–event pair extraction [ 46 , 56 , 69 , 77 , 78 ] ADE detection [ 54 ] Advantages: 1. Huge data size with rapid growth.…”

Section: Data Sources For Pharmacovigilancementioning

confidence: 99%

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Machine Learning in Causal Inference: Application in Pharmacovigilance

Zhao

Wang

et al. 2022

Drug Saf

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Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.

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Section: Data Sources For Pharmacovigilancementioning

confidence: 99%

Section: Data Sources For Pharmacovigilancementioning

confidence: 99%

Section: Data Sources For Pharmacovigilancementioning

confidence: 99%

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Machine Learning in Causal Inference: Application in Pharmacovigilance

Zhao

Wang

et al. 2022

Drug Saf

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“…Little has been studied on using claims data for ADE detection. [24] use ICD codes and GPI drug code in claims data (see Table I for description of the code) as input and design a graph neural network model to construct a drug-disease graph for ADE detection. They first embedded disease codes and drug codes into a graph, respectively, then the merged drug and disease graph is fed into a graph neural network for ADE detection.…”

Section: Related Workmentioning

confidence: 99%

Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

Shi

Gao

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of studies have been performed on detect correlation between a drug and an AE, limited studies have been conducted on personalized ADE risk prediction. Among treatment alternatives, avoiding the drug that has high likelihood of causing severe AE can help physicians to provide safer treatment to patients. Existing work on personalized ADE risk prediction uses the information obtained in the current medical visit. However, on the other hand, medical history reveals each patients unique characteristics and comprehensive medical information. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claims codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that capture characteristics of claim codes and their relationship. The empirical evaluation show that the proposed HTNNR model substantially outperforms the comparison methods, especially for rare drugs.

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“…This learnable and informative dropout model is very useful because usually not all the nodes in a graph have the same set of features, this is especially true in the biology domain where features are difficult to get due to high experiment cost. This framework is not only suitable to the graph convolutional networks, but also can be extended to various of GNNs, such as GraphSage ( Hamilton et al , 2017 ) and GAT ( Veličković et al , 2017 ), which has been more and more applied to biology domain ( Kwak et al , 2020 ; Zitnik et al , 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Disease gene prediction with privileged information and heteroscedastic dropout

et al. 2021

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Motivation Recently, machine learning models have achieved tremendous success in prioritizing candidate genes for genetic diseases. These models are able to accurately quantify the similarity among disease and genes based on the intuition that similar genes are more likely to be associated with similar diseases. However, the genetic features these methods rely on are often hard to collect due to high experimental cost and various other technical limitations. Existing solutions of this problem significantly increase the risk of overfitting and decrease the generalizability of the models. Results In this work, we propose a graph neural network (GNN) version of the Learning under Privileged Information paradigm to predict new disease gene associations. Unlike previous gene prioritization approaches, our model does not require the genetic features to be the same at training and test stages. If a genetic feature is hard to measure and therefore missing at the test stage, our model could still efficiently incorporate its information during the training process. To implement this, we develop a Heteroscedastic Gaussian Dropout algorithm, where the dropout probability of the GNN model is determined by another GNN model with a mirrored GNN architecture. To evaluate our method, we compared our method with four state-of-the-art methods on the Online Mendelian Inheritance in Man dataset to prioritize candidate disease genes. Extensive evaluations show that our model could improve the prediction accuracy when all the features are available compared to other methods. More importantly, our model could make very accurate predictions when >90% of the features are missing at the test stage. Availability and implementation Our method is realized with Python 3.7 and Pytorch 1.5.0 and method and data are freely available at: https://github.com/juanshu30/Disease-Gene-Prioritization-with-Privileged-Information-and-Heteroscedastic-Dropout.

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Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Cited by 18 publications

References 20 publications

Machine Learning in Causal Inference: Application in Pharmacovigilance

Machine Learning in Causal Inference: Application in Pharmacovigilance

Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

Disease gene prediction with privileged information and heteroscedastic dropout

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