Age prediction from COVID-19 blood test for ensuring robust artificial intelligence - Nurul Qomariyah, Nunung; Kazako, Dimitar (Institute of Advanced Engineering and Science, 2024)
With the advancement of artificial intelligence (AI) nowadays, the world is experiencing conveniences in automating some complex and tedious tasks, such as analysing large data and predicting the future by mimicking human expertise. AI has also shown promise for mitigating future crisis, such as pandemic. Since the beginning of the COVID-19, several AI models have been published by the researchers to help the healthcare to fight in this situation. However, before deploying the model, one needs to ensure that the model is robust and safe to learn from the real environment, especially in medical domain, where the uncertainty and incomplete information are not unusual. In the effort of providing robust AI, we proposed to use patient age as one of the feasible feature for ensuring vigorous AI models from electronic health record. We conducted several experiment with 28 blood test items and radiologist report from 1,000 COVID-19 patients. Our result shows that with the predicted age as an additional feature in mortality classification task, the model is significantly improved when compared to adding the actual age. We also reported our findings regarding the predicted age in the dataset.