Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
Abstract
:1. Introduction
1.1. Comparisons with the Existing Literature Reviews
1.2. Motivations and Contributions
 We review and classify different levels of graph machinelearning approaches.
 The applications of disease prediction in different graph ML approaches are summarised.
 We highlight the shortcomings in the present research, pointing to future research directions and opportunities.
2. Overview and Search Strategy
3. Graph MachineLearning Approaches
3.1. Shallow Embedding
3.1.1. HandCrafted Features
3.1.2. Random WalkBased Methods
3.2. Graph Neural NetworkBased Methods
3.2.1. Graph Convolutional Networks
3.2.2. Graph Attention Networks
3.2.3. Graph AutoEncoders
4. Applications in Disease Prediction
4.1. Node Classification
4.2. Link Prediction
5. Findings
Reference  Disease Predicted  Type of Data  Data Size  Task  Methods  Prediction Performance  Source Code 

Liu et al. (2015) [53]  Oneyear hospitalisation prediction and congestive heart failure (CHF)  Realworld electronic health records over four years  319,650  Node classification  Shallow embedding (handcrafted)  Accuracy: 76% (CHF), 65% (hospitalisation)   
Khan et al. (2019) [8]  Type 2 diabetes  Administrative claim data from an Australian insurance company  2300  Node classification  Shallow embedding (handcrafted)  Accuracy: 82–87% (for different machinelearning methods)   
Hossain et al. (2020) [54]  Cardiovascular disease in patients with type 2 diabetes  Administrative claim data from an Australian insurance company  172  Node classification  Shallow embedding (handcrafted)  Accuracy: 79–88% (for different machinelearning methods)   
Lu et al. (2021) [12]  Type 2 diabetes  Administrative claim data from an Australian insurance company  2056  Node classification  Shallow embedding (handcrafted)  Area under curve (AUC): 0.79–0.91 (for different machinelearning methods)   
Choi et al. (2017) [55]  Heart failure  Three different datasets (Sutter PAMF, Medical Information Mart for Intensive Care (MIMIC)III, and Sutter Heart failure cohort)  258,555, 7499, and 30,737, respectively  Node classification  Shallow embedding (handcrafted and random walk)  AUC: 0.7970–0.8448 (using different training ratios)  https://github.com/mp2893/gram (accessed on 3 March 2023) 
Zhang et al. (2017) [56]  Chronic disease comorbidity in patients  Anonymised electronic healthcare records data from a major medical centre  381,169  Node classification  Shallow embedding (handcrafted)  F1 score: 0.26–0.48 (for different comorbidities)   
Xu et al. (2020) [57]  Postdischarge selfharm incidents  Electronic healthcare records collected from Hong Kong residents  2323 selfharm samples and 46,460 counterparts  Node classification  Shallow embedding (tandom walk)  Cstatistic: 0.89   
Yang et al. (2022) [70]  Ischemic heart disease  Hospital discharge records from China  72,668  Node classification  Shallow embedding (handcrafted)  AUC: 0.864–0.900  
Sun et al. (2020) [58]  Multiple diseases  Realworld electronic healthcare records: private patient clinical record dataset collected from local hospitals  806  Node classification  GNN based (GAT and graph autoencoder)  F1score: 0.457 (all diseases), 0.442 (rare diseases)  https://github.com/zhchs/DiseasePredictionviaGCN (accessed on 3 March 2023) 
Wang et al. (2020) [59]  Cancer  Electronic healthcare records collected from the US  159 for breast cancer and 160 for the lung squamous cell cancer  Node classification  GNN based (GCN)  Accuracy: 92.80% (for invasive breast carcinoma), 80.50% (lung squamous cell carcinoma)   
Gao et al. (2020) [60]  Breast cancer  Electronic health records from Memorial Sloan Kettering Cancer Center  1903  Node classification  GNN based (graph autoencoder)  Accuracy: 94%   
Lu and Uddin (2021) [7]  Cardiovascular and chronic pulmonary  Administrative claim data from an Australian insurance company  2610 for the cardiovascular and 1056 for the chronic pulmonary  Node classification  GNN based (GCN and GAT)  Accuracy: 93.49% (cardiovascular disease), 89.15% (chronic pulmonary disease)   
Li et al. (2020) [61]  Multiple diseases  A realworld longitudinal electronic health records database  7499  Node classification  GNN based (GCN)  Accuracy: 81.76%   
Zhu and Razavian (2021) [62]  Alzheimer’s disease and multiple predictive tasks  Electronic health records, MIMICIII, and eICU  6028, 6778, and 3250, respectively  Node classification  GNN based (graph autoencoder)  The area under the precisionrecall curve (AUPRC): 0.4580 (ADHER), 0.7102 (MIMICII), and 0.3986 (eICU readmission)  https://github.com/NYUMedML/GNN_for_EHR (accessed on 3 March 2023) 
Wang et al. (2020) [66]  Multiple diseases  General hospital data from two hospitals in Beijing and Shenzhen, China  7989 and 4131, respectively  Link prediction  Shallow embedding (handcrafted)  Mean accuracy: 85.75–89.87 (for the different schemes and datasets)   
del Valle et al. (2021) [67]  Multiple diseases  Electronic health records: DISNET  5147  Link prediction  Shallow embedding (tandom walk)  AUC: 0.74   
Wang et al., (2020) [69]  Multiple diseases  Electronic health records from New York State Medicaid  596,574  Link prediction  GNN based (GCN)  RMSE: 0.8622   
Lu and Uddin (2022) [71]  Multiple diseases  Administrative claim data from an Australian insurance company  19,828  Link prediction  Shallow embedding (handcrafted and random walk) and GNN based (GCN)  AUC: 0.7964 to 0.8969.   
6. Discussions and Future Directions
6.1. Benefits and Drawbacks
6.2. Data Processing
6.3. Challenges and Trends
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Abbreviation  Definition 
AUC  Area under curve 
AUPRC  The area under the precisionrecall curve 
CHF  Congestive heart failure 
CNN  Convolutional neural network 
DL  Deep learning 
GAE  Graph autoencoders 
GAT  Graph attention network 
GCN  Graph convolutional network 
GNN  Graph neural networks 
HCNN  Heterogeneous convolution neural network 
MIMIC  Medical Information Mart for Intensive Care 
ML  Machine learning 
LSTM  Long shortterm memory 
T2D  Type 2 diabetes 
VGAE  Variation graph autoencoder 
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Graph MachineLearning Model  Advantage  Disadvantage 

Shallow embedding (handcrafted features)  
Shallow embedding (deep walk based)  
GCNs 


GATs 
 
Graph autoencoder 

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Lu, H.; Uddin, S. Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends. Healthcare 2023, 11, 1031. https://doi.org/10.3390/healthcare11071031
Lu H, Uddin S. Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends. Healthcare. 2023; 11(7):1031. https://doi.org/10.3390/healthcare11071031
Chicago/Turabian StyleLu, Haohui, and Shahadat Uddin. 2023. "Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends" Healthcare 11, no. 7: 1031. https://doi.org/10.3390/healthcare11071031