Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model

SALEH, Nurul, AB GHANI, Hadhrami and JILANI, Zairul (2022). Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model. Artificial Intelligence in Medicine: 102394.

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Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease.

Item Type: Article
Additional Information: ** Article version: AM ** Embargo end date: 31-12-9999 ** From Elsevier via Jisc Publications Router ** Licence for AM version of this article: This article is under embargo with an end date yet to be finalised. **Journal IDs: issn 09333657 **History: issued 05-09-2022; accepted 29-08-2022
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SWORD Depositor: Colin Knott
Depositing User: Colin Knott
Date Deposited: 08 Sep 2022 09:20
Last Modified: 11 Oct 2023 11:45

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