Predicting COVID-19 Patient Shielding: A Comprehensive Study
34th Australasian Joint Conference on Artificial Intelligence, AI 2021
; 13151 LNAI:332-343, 2022.
Article
in English
| Scopus | ID: covidwho-1782718
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. © 2022, Springer Nature Switzerland AG.
COVID-19; Medical text; Multi-label; Neural networks; Transformers; Coronavirus; Data Analytics; Diagnosis; Forecasting; Risk management; Shielding; Text processing; Coronaviruses; Multi-labels; Neural-networks; Prediction risks; Risk prevention; Risks management; Transformer; United kingdom; Classification (of information)
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
34th Australasian Joint Conference on Artificial Intelligence, AI 2021
Year:
2022
Document Type:
Article
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