Predicting COVID-19 Patient Shielding: A Comprehensive Study
This work addresses the need to protect vulnerable patients during the COVID-19 pandemic, but it is incremental as it applies existing methods to a new dataset without major methodological breakthroughs.
This study tackled the problem of predicting COVID-19 patient shielding by identifying clinically vulnerable patients using multi-label classification of medical text, achieving results through an extensive comparison of 12 classifiers including neural networks and transformers.
There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID-19 patient shielding -- identifying and protecting patients who are clinically extremely vulnerable from coronavirus. This study focuses on techniques used for the multi-label classification of medical text. Using the information published by the United Kingdom NHS and the World Health Organisation, we present a novel approach to predicting COVID-19 patient shielding as a multi-label classification problem. We use publicly available, de-identified ICU medical text data for our experiments. The labels are derived from the published COVID-19 patient shielding data. We present an extensive comparison across 12 multi-label classifiers from the simple binary relevance to neural networks and the most recent transformers. To the best of our knowledge this is the first comprehensive study, where such a range of multi-label classifiers for medical text are considered. We highlight the benefits of various approaches, and argue that, for the task at hand, both predictive accuracy and processing time are essential.