Automated remote decision-making algorithm as a primary triage system using machine learning techniques.

Objective: An objective and convenient primary triage procedure is needed for prioritizing patients who need help in mass casualty incident (MCI) situations, where there is a lack of medical staff and available resources. This study aimed to develop an automated remote decision-making algorithm that remotely categorize a patient's emergency level using clinical parameters that can be measured with a wearable device.

Approach: The algorithm was developed according to the following procedures.

Prediction of mortality in patients undergoing maintenance hemodialysis by Charlson Comorbidity Index using ICD-10 database.

Background/aims: Many patients with end-stage renal disease have additional comorbidities that are important to clinical study and impact the patient's outcome. The Charlson Comorbidity Index (CCI) is a popular tool and a strong predictor of outcome in end-stage renal disease patients. We obtained comorbidity data from the hospital discharge database using the International Classification of Disease, 10th revision (ICD-10) and analyzed the mortality rate in incident patients undergoing maintenance hemodialysis (HD).