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Tariq MU, Ismail SB. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLoS One 2024; 19:e0294289. [PMID: 38483948 PMCID: PMC10939212 DOI: 10.1371/journal.pone.0294289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 10/28/2023] [Indexed: 03/17/2024] Open
Abstract
The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.
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Affiliation(s)
- Muhammad Usman Tariq
- Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia
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Boateng A, Maposa D, Mokobane R. Retrospective Post-Hospitalisation COVID-19 Mortality Risk Assessment of Patients in South Africa. Eur J Investig Health Psychol Educ 2023; 13:1655-1675. [PMID: 37754459 PMCID: PMC10528257 DOI: 10.3390/ejihpe13090120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/28/2023] Open
Abstract
Background: This study explores the determinants impacting the mortality risk of COVID-19 patients following hospitalisation within South Africa's Limpopo province. Methods: Utilising a dataset comprising 388 patients, the investigation employs a frailty regression model to evaluate the influence of diverse characteristics on mortality outcomes, contrasting its performance against other parametric models based on loglikelihood measures. Results: The findings underscore diabetes and hypertension as notable contributors to heightened mortality rates, underscoring the urgency of effectively managing these comorbidities to optimise patient well-being. Additionally, regional discrepancies come to the fore, with the Capricorn district demonstrating elevated mortality risks, thereby accentuating the necessity for precisely targeted interventions. Medical interventions, particularly ventilation, emerge as pivotal factors in mitigating mortality risk. Gender-based distinctions in mortality patterns also underscore the need for bespoke patient care strategies. Conclusions: Collectively, these outcomes supply practical insights with implications for healthcare interventions, policy formulation, and clinical strategies aimed at ameliorating COVID-19 mortality risk among individuals discharged from hospitals within South Africa's Limpopo province.
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Affiliation(s)
- Alexander Boateng
- Department of Biostatistics, University of the Free State, Bloemfontein 9300, South Africa;
| | - Daniel Maposa
- Department of Statistics and Operations Research, University of Limpopo, Polokwane 0727, South Africa;
| | - Reshoketswe Mokobane
- Department of Statistics and Operations Research, University of Limpopo, Polokwane 0727, South Africa;
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Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. JOURNAL OF BUSINESS RESEARCH 2023; 160:113806. [PMID: 36895308 PMCID: PMC9981538 DOI: 10.1016/j.jbusres.2023.113806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.
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Affiliation(s)
- Miguel Ortiz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Sebastián Arias-Fonseca
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Alessio Ishizaka
- NEOMA Business School, 1 rue du Maréchal Juin, Mont-Saint-Aignan 76130, France
| | - Maria Barbati
- Department of Economics, University Ca' Foscari, Cannaregio 873, Fondamenta San Giobbe, 30121 Venice, Italy
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Muto R, Fukuta S, Watanabe T, Shindo Y, Kanemitsu Y, Kajikawa S, Yonezawa T, Inoue T, Ichihashi T, Shiratori Y, Maruyama S. Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data. Front Med (Lausanne) 2022; 9:1042067. [PMID: 36530899 PMCID: PMC9748157 DOI: 10.3389/fmed.2022.1042067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND When facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19. MATERIALS AND METHODS We enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission. RESULTS The model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care. CONCLUSION In clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.
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Affiliation(s)
- Reiko Muto
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Molecular Medicine and Metabolism, Research Institute of Environmental Medicine, Nagoya University, Nagoya, Japan
| | - Shigeki Fukuta
- Artificial Intelligence Laboratory, Fujitsu Limited, Kawasaki, Japan
| | | | - Yuichiro Shindo
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshihiro Kanemitsu
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Shigehisa Kajikawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Toshiyuki Yonezawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Takahiro Inoue
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takuji Ichihashi
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
| | - Yoshimune Shiratori
- Center for Healthcare Information Technology (C-HiT), Nagoya University, Nagoya, Japan
- Medical IT Center, Nagoya University Hospital, Nagoya, Japan
| | - Shoichi Maruyama
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
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Khadem H, Nemat H, Elliott J, Benaissa M. Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228757. [PMID: 36433354 PMCID: PMC9692305 DOI: 10.3390/s22228757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/07/2022] [Accepted: 11/11/2022] [Indexed: 05/13/2023]
Abstract
People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors' global and local influence on the model's outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.
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Affiliation(s)
- Heydar Khadem
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
- Correspondence:
| | - Hoda Nemat
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
| | - Jackie Elliott
- Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
- Teaching Hospitals, Diabetes and Endocrine Centre, Northern General Hospital, Sheffield S5 7AU, UK
| | - Mohammed Benaissa
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
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Khadem H, Nemat H, Elliott J, Benaissa M. Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy. Talanta 2022; 243:123379. [DOI: 10.1016/j.talanta.2022.123379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 11/30/2022]
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Peng H, Hu C, Deng W, Huang L, Zhang Y, Luo B, Wang X, Long X, Huang X. Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model. BMC Pulm Med 2022; 22:188. [PMID: 35549897 PMCID: PMC9095818 DOI: 10.1186/s12890-022-01986-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission. Method We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization. Results In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53–15.80], incubation period (OR 0.83; 95% CI 0.68–0.99), number of comorbidities (OR 1.76; 95% CI 1.03–3.05), D-dimer (OR 7.05; 95% CI, 1.35–45.7), C-reactive protein (OR 1.06; 95% CI 1.02–1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27–1.82). The model showed good fitting (Hosmer–Lemeshow goodness, X2(8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949–0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUCPR = 0.934). We prepared a nomogram and a freely available online prediction platform (https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/). Conclusion We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-022-01986-0.
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Affiliation(s)
- Hongbing Peng
- Department of Pulmonary and Critical Care Medicine, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China. .,Department of Pulmonary and Critical Care Medicine, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China.
| | - Chao Hu
- Department of Pulmonary and Critical Care Medicine, Xiangtan Central Hospital, No. 120, Road Heping, Distract Yuhu, Xiangtan, 411100, People's Republic of China
| | - Wusheng Deng
- Department of Pulmonary and Critical Care Medicine, Shaoyang Central Hospital, No. 36 Hongqi Road, Shaoyang, 422000, People's Republic of China
| | - Lingmei Huang
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of YueYang, No. 39 Dongmaoling Road, Yueyang, 414000, People's Republic of China
| | - Yushan Zhang
- Department of Pulmonary and Critical Care Medicine, Huaihua First People's Hospital, No. 144, Jinxi South Road, Huaihua, 418000, People's Republic of China
| | - Baowei Luo
- Department of Respiratory Medicine, Shuangfeng County People's Hospital, 238 Shuyuan Road, Shuangfeng County, 417007, People's Republic of China
| | - Xingxing Wang
- Department of Infection, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China
| | - Xiaodan Long
- Department of Urology Surgery, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China
| | - Xiaoying Huang
- Department of Pulmonary and Critical Care Medicine, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China
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Xu H, Buckeridge DL, Wang F, Tarczy-Hornoch P. Novel informatics approaches to COVID-19 Research: From methods to applications. J Biomed Inform 2022; 129:104028. [PMID: 35181495 PMCID: PMC8847074 DOI: 10.1016/j.jbi.2022.104028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/10/2022] [Indexed: 10/30/2022]
Affiliation(s)
- Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - David L Buckeridge
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, NY, USA
| | - Peter Tarczy-Hornoch
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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Derivation and Validation of a Predictive Score for Respiratory Failure Worsening Leading to Secondary Intubation in COVID-19: The CERES Score. J Clin Med 2022; 11:jcm11082172. [PMID: 35456266 PMCID: PMC9028352 DOI: 10.3390/jcm11082172] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 04/09/2022] [Accepted: 04/11/2022] [Indexed: 12/27/2022] Open
Abstract
Predictive scores assessing the risk of respiratory failure in COVID-19 mostly focused on the prediction of early intubation. A combined assessment of clinical parameters and biomarkers of endotheliopathy could allow to predict late worsening of acute respiratory failure (ARF), subsequently warranting intubation in COVID-19. Retrospective single-center derivation (n = 92 subjects) and validation cohorts (n = 59 subjects), including severe COVID-19 patients with non-invasive respiratory support, were assessed for at least 48 h following intensive care unit (ICU) admission. We used stepwise regression to construct the COVID endothelial and respiratory failure (CERES) score in a derivation cohort, and secondly assessed its accuracy for the prediction of late ARF worsening, requiring intubation within 15 days following ICU admission in an independent validation cohort. Platelet count, fraction of inspired oxygen, and endocan measured on ICU admission were identified as the top three predictive variables for late ARF worsening and subsequently included in the CERES score. The area under the ROC curve of the CERES score to predict late ARF worsening was calculated in the derivation and validation cohorts at 0.834 and 0.780, respectively. The CERES score is a simple tool with good performances to predict respiratory failure worsening, leading to secondary intubation, in COVID-19 patients.
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