Acute Kidney Injury (AKI) Prediction System using Machine Learning Models
Many serious medical conditions can result in kidney injury, making kidney function a good, if lagging, indicator of general health. Acute kidney injury is generally detected through the presence of creatine, a waste product, in the blood. If a patient’s kidneys are damaged, they will not be able to remove creatinine effectively, leading to an increased amount being found in blood tests.
ML Model
This is a proposed Machine Learning model for the prediction of Acute Kidney Injury (AKI) from clinical temporal changes of creatinine levels. Trained on 7000+ clinical samples, this ML model can correctly predict the presence of AKI with >98% accuracy.
Full System
A real-time prediction system to detect Acute Kidney Injury (AKI) from clinical temporal changes of creatinine levels using a Machine Learning model.
The System processes real-time Health Level 7 (HL7) messages sent from the Hospital’s Clinical Systems: Patient Administration System (PAS) and Laboratory Information Management System (LISM). For every new blood test result, the system will run the prediction using the pre-trained model and, if AKI is detected, the system will page the Hospital’s Clinical Response team in less than 3 seconds from the test result receipt. Our system achieved perfect paging score during a 2-week live testing simulation.
All patient data from the Hospital is stored in a database, with separate tables for Patient Data and Blood Test Results data.
