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

Saleh, Nurul; Ghani, Hadhrami Ab.; Jilani, Zairul

doi:10.1016/j.artmed.2022.102394

Cited by 4 publications

(6 citation statements)

References 30 publications

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“…using formal concept analysis (FCA) to create a set of association rules with different confidence intervals [120]; -applying a Bayesian network to visualize the effect of the potential influencers on decision making [24]; -proposing a single associated decision tree (DT) to represent a random forest (RF) model [68]; -applying the anchors method to help explain predictions by decision rules [23]; -utilizing a probabilistic graphical model (PGM-Explainer) as a simpler interpretable Bayesian network in order to interpret GNNs [121]; -applying the symbolic meta modeling approach, which integrates various simple parameterized functions to obtain a closed-form and interpretable expression for the meta model [122].…”

Section: Non-intrinsically Interpretable Modelsmentioning

confidence: 99%

“…The analyzed studies focus on different research objectives. Depending on the focus, they generate different explanations on the significance of different influential factors on the spread of the pandemic, such as compliance with interventions [133], population density [134], population movement and gathering [76,78,92], lock down effects [18,120], labor and unemployment effects [72,81], closure and regulation of schools [129,135], vaccination [130][131][132], spatial effects [74,136], weather conditions [127,137,138], country dietary and cultural effects [51,61], virus variants [98], and health infrastructural impacts [139].…”

Section: Literature Analysis Of Epidemiological Ai Researchmentioning

confidence: 99%

“…Graphical causal structure of the data is used in the studies presented in Table 3, not only for the sake of intrinsically interpretable model training [136,142], but also for incorporating prior knowledge into the resulting SHAP values [143]. In addition, creating associative rules as interpretable model-agnostic models is applied in [51,120].…”

Section: Literature Analysis Of Epidemiological Ai Researchmentioning

confidence: 99%

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Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili,

Wimmer

2024

Life

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By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

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Section: Non-intrinsically Interpretable Modelsmentioning

confidence: 99%

Section: Literature Analysis Of Epidemiological Ai Researchmentioning

confidence: 99%

Section: Literature Analysis Of Epidemiological Ai Researchmentioning

confidence: 99%

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Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili,

Wimmer

2024

Life

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“…In computer science, some studies were successful to apply formal concept analysis for solving some problems in many sub-domains, e.g ., datamining ( Aragón, Medina & Ramírez-Poussa, 2022 ; Hao et al, 2023 ), machine learning ( Janostik, Konecny & Krajča, 2022 ), data science ( Bazin et al, 2022 ), intelligent system ( Shao et al, 2023 ), information retrieval ( Ojeda-Hernández, López-Rodríguez & Mora, 2023 ; Khattak et al, 2021 ), natural language processing ( Marín et al, 2021 ; Jain, Seeja & Jindal, 2020 ), decision support system ( Wei et al, 2020 ), recommendation system ( Liu et al, 2022 ), semantic web ( Jindal, Seeja & Jain, 2020 ), cloud computing ( Khemili, Hajlaoui & Omri, 2022 ), data structure ( Ferré & Cellier, 2020 ), mobile application ( Kwon et al, 2021 ), software engineering ( Carbonnel et al, 2020 ), and robotic ( Zhang et al, 2023 ). In addition, some successful studies to apply formal concept analysis were in other domains, e.g ., engineering ( Rocco, Hernandez-Perdomo & Mun, 2020 ), mathematics ( Jäkel & Schmidt, 2022 ; Rocco, Hernandez-Perdomo & Mun, 2020 ), biology ( Gély et al, 2022 ), psychology ( Belohlavek & Mikula, 2022 ), medicine ( Md Saleh, Ab Ghani & Jilani, 2022 ), business ( Wajnberg et al, 2018 ; Ravi, Ravi & Prasad, 2017 ; Acharjya & Das, 2017 ), and social science ( Lang & Yao, 2023 ; Hao et al, 2021 ; Gao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Non-redundant implicational base of formal context with constraints using SAT

Hidayat,

Ahmad,

Ngo

2024

PeerJ Computer Science

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An implicational base is knowledge extracted from a formal context. The implicational base of a formal context consists of attribute implications which are sound, complete, and non-redundant regarding to the formal context. Non-redundant means that each attribute implication in the implication base cannot be inferred from the others. However, sometimes some attribute implications in the implication base can be inferred from the others together with a prior knowledge. Regarding knowledge discovery, such attribute implications should be not considered as new knowledge and ignored from the implicational base. In other words, such attribute implications are redundant based on prior knowledge. One sort of prior knowledge is a set of constraints that restricts some attributes in data. In formal context, constraints restrict some attributes of objects in the formal context. This article proposes a method to generate non-redundant implication base of a formal context with some constraints which restricting the formal context. In this case, non-redundant implicational base means that the implicational base does not contain all attribute implications which can be inferred from the others together with information of the constraints. This article also proposes a formulation to check the redundant attribute implications and encoding the problem into satisfiability (SAT) problem such that the problem can be solved by SAT Solver, a software which can solve a SAT problem. After implementation, an experiment shows that the proposed method is able to check the redundant attribute implication and generates a non-redundant implicational base of formal context with constraints.

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“…The RNN, Stacking, Bi-LSTM, and Gyrus LSTM models offered accurate estimates for the quantity of COVID-19 incidences and fatalities anticipated in the subsequent month. The investigation by Md Saleh et al ( 2022 ) employed the LSTM model to scrutinize regularly publicized cases at national and regional levels in Spain. Dairi et al ( 2021 ) presented the integration of Bayesian optimization, long short-term memory (LSTM), and convolutional neural network (CNN) that demonstrated commendable efficiency in predicting epidemics.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Alhhazmi,

Alferidi,

Almutawif

et al. 2024

Front. Artif. Intell.

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Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.

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

Cited by 4 publications

References 30 publications

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Non-redundant implicational base of formal context with constraints using SAT

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

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