Predicting COVID-19 Patient Shielding: A Comprehensive Study

• URL: http://arxiv.org/abs/2110.00183v1
• Date: Fri, 1 Oct 2021 03:02:58 GMT
• Title: Predicting COVID-19 Patient Shielding: A Comprehensive Study
• Authors: Vithya Yogarajan and Jacob Montiel and Tony Smith and Bernhard Pfahringer
• Abstract summary: This study focuses on predicting COVID-19 patient shielding. We present a novel approach to predicting COVID-19 patient shielding as a multi-label classification problem.
• Score: 3.0625089376654664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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