Exploring COVID-19 Vital Signs using Bayesian Network Causal Graph
Nunung Nurul Qomariyah, Teny Handhayani, Sri Dhuny Atas Asri, Dimitar Kazakov · 2024
COVID-19 (Coronavirus 2019) has now shown a flattening curve as the impact of increased immunity system through vaccination or natural infection. However, experience and data suggest new variants can emerge at any time. Therefore, research in this topic is still rising. The Artificial Intelligence (AI) can help the health practitioners and medical personnel to understand the problems caused by this disease based on the historical data. Usually, the progress of the patients in a hospital are monitored by the nurse or the doctors in daily basis. Especially when they are in the critical stage of the illness. They take the reading from the devices to monitor the vital signs. In this study, we aim to explain the vital sign causal relationship by using one of the Bayesian Network (BN) method, namely PC algorithm. The result shows that causal relationship from died patients vital signs is different and unexplainable due to many failures of the body to regulate the flow of oxygen supply.