For complicated diseases such as Parkinson’s and Alzheimer’s, both EHR and neuroimaging information are important for understanding and diagnosis purpose. MemoryBased Graph Convolution Network (MemGCN) is a framework to perform integrative analysis with such multi-modal data and aims at the task of classification of Parkinson’s Disease (PD) cases versus controls. During analysis, GCN module extracts features from the human brain networks constructed from the brain images. The longitudinal patient EHRs are stored in the memory network to encode the historical clinical information about the patient before the acquisition of the image. The information contained in each brain image is combined with the information read out from the memory network to infer the disease state at the image acquisition timestamp. Model is then trained in an end-to-end fashion with stochastic optimization.
Input variables : EHR, Neuroimages
Output Variables : Disease Classification
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : arxiv.org
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | September, 2018 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Health Risk Management |
| Disease Detection |