Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) readings. This model is a novel decentralised learning framework for the generation of continuous ABP data and MAP estimates using a single optical sensor alone. The traditional methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. This machine learning approach is capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone.
Input variables : Data from PPG
Output Variables : Arterial Blood Pressure
| 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 | : | February, 2021 |
| Healthcare Domain | : | Medical Technology |
| Code | : | github.com |
| Health Risk Management |
| Health Risk Prediction |