Detecting Alzheimer’s in pre-symptomatic stages can play a crucial role in treating Alzhemier's patients.This model is an approach to predict Alzheimer’s from Electronic Health Records(EHR) data. Model can help in predicting the disease 12 to 24 months in advance and also provide insights on relationship among Diagnosis and Procedures performed on patients. The data used for model building includes diagnosis, lab values, procedures and demographic information. The technique used for modelling is Graph Attention Network (GAT) which is like multiple nodes connected to each other with edges. As different weights are assigned to each variable it is easier to interpret importance on features as well
Input variables : Demographic info, ICD-10 Diagnosis, Lab Values,Procedure codes
Output Variables : Alzheimer’s disease Forecasting
| Statistical | : | Mallow's CP | R Squared | Mean Square Error | Adjusted R Squared | Mean Absolute Error | Huber Loss |
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters | Optimized Hospital Resource Utilization | Decreased Cost of Care | Decreased Patient Visits |
| 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 : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | December, 2019 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Health Risk Management |
| Health Risk Prediction |