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Huang Y, Wang M, Zheng Z, Ma M, Fei X, Wei L, Chen H. Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients. J Biomed Inform 2023; 143:104427. [PMID: 37339714 DOI: 10.1016/j.jbi.2023.104427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 04/18/2023] [Accepted: 06/15/2023] [Indexed: 06/22/2023]
Abstract
OBJECTIVE To represent a patient record with both time-invariant and time-varying features as a single vector using an end-to-end deep learning model, and further to predict the kidney failure (KF) status and mortality of heart failure (HF) patients. MATERIALS AND METHODS The time-invariant EMR data included demographic information and comorbidities, and the time-varying EMR data were lab tests. We used a Transformer encoder module to represent the time-invariant data, and refined a long short-term memory (LSTM) with a Transformer encoder attached to the top to represent the time-varying data, taking the original measured values and their corresponding embedding vectors, masking vectors, and two types of time intervals as inputs. The proposed representations of patients with time-invariant and time-varying data were used to predict KF status (949 out of 5268 HF patients diagnosed with KF) and mortality (463 in-hospital deaths) for HF patients. Comparative experiments were conducted between the proposed model and some representative machine learning models. Ablation experiments were also performed around the time-varying data representation, including replacing the refined LSTM with the standard LSTM, GRU-D and T-LSTM, respectively, and removing the Transformer encoder and the time-varying data representation module, respectively. The visualization of the attention weights of the time-invariant and time-varying features was used to clinically interpret the predictive performance. We used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the F1-score to evaluate the predictive performance of the models. RESULTS The proposed model achieved superior performance, with average AUROCs, AUPRCs and F1-scores of 0.960, 0.610 and 0.759 for KF prediction and 0.937, 0.353 and 0.537 for mortality prediction, respectively. Predictive performance improved with the addition of time-varying data from longer time periods. The proposed model outperformed the comparison and ablation references in both prediction tasks. CONCLUSIONS Both time-invariant and time-varying EMR data of patients could be efficiently represented by the proposed unified deep learning model, which shows higher performance in clinical prediction tasks. The way to use time-varying data in the current study is hopeful to be used in other kinds of time-varying data and other clinical tasks.
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Affiliation(s)
- Yanqun Huang
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Muyu Wang
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Zhimin Zheng
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Moxuan Ma
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Xiaolu Fei
- Information Center, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing 100053, China.
| | - Lan Wei
- Information Center, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing 100053, China.
| | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
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2
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Men K, Li Y, Wang X, Zhang G, Hu J, Gao Y, Han A, Liu W, Han H. Estimate the incubation period of coronavirus 2019 (COVID-19). Comput Biol Med 2023; 158:106794. [PMID: 37044045 PMCID: PMC10062796 DOI: 10.1016/j.compbiomed.2023.106794] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/23/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023]
Abstract
COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.
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Affiliation(s)
- Ke Men
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Yihao Li
- The Gabelli School of Business, Fordham University, Lincoln Center, New York, NY, 10023, USA
| | - Xia Wang
- The Air Force Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Guangwei Zhang
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Jingjing Hu
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Yanyan Gao
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Ashley Han
- The Skyline High School, Ann Arbor, MI, 48103, USA
| | - Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Henry Han
- The Laboratory of Data Science and Artificial Intelligence Innovation, Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX, 76789, USA.
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Ahmad J, Saudagar AKJ, Malik KM, Khan MB, AlTameem A, Alkhathami M, Hasanat MHA. Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning. Diagnostics (Basel) 2023; 13:diagnostics13081387. [PMID: 37189488 DOI: 10.3390/diagnostics13081387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/05/2023] [Accepted: 03/17/2023] [Indexed: 05/17/2023] Open
Abstract
The COVID-19 pandemic has presented a unique challenge for physicians worldwide, as they grapple with limited data and uncertainty in diagnosing and predicting disease outcomes. In such dire circumstances, the need for innovative methods that can aid in making informed decisions with limited data is more critical than ever before. To allow prediction with limited COVID-19 data as a case study, we present a complete framework for progression and prognosis prediction in chest X-rays (CXR) through reasoning in a COVID-specific deep feature space. The proposed approach relies on a pre-trained deep learning model that has been fine-tuned specifically for COVID-19 CXRs to identify infection-sensitive features from chest radiographs. Using a neuronal attention-based mechanism, the proposed method determines dominant neural activations that lead to a feature subspace where neurons are more sensitive to COVID-related abnormalities. This process allows the input CXRs to be projected into a high-dimensional feature space where age and clinical attributes like comorbidities are associated with each CXR. The proposed method can accurately retrieve relevant cases from electronic health records (EHRs) using visual similarity, age group, and comorbidity similarities. These cases are then analyzed to gather evidence for reasoning, including diagnosis and treatment. By using a two-stage reasoning process based on the Dempster-Shafer theory of evidence, the proposed method can accurately predict the severity, progression, and prognosis of a COVID-19 patient when sufficient evidence is available. Experimental results on two large datasets show that the proposed method achieves 88% precision, 79% recall, and 83.7% F-score on the test sets.
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Affiliation(s)
- Jamil Ahmad
- Department of Computer Science, Islamia College Peshawar, Peshawar 25120, Pakistan
| | | | - Khalid Mahmood Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA
| | - Muhammad Badruddin Khan
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Abdullah AlTameem
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Mohammed Alkhathami
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
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Huang J, Ma W, Li R, Zhao N, Zhou T. Myopia prediction for children and adolescents via time-aware deep learning. Sci Rep 2023; 13:5430. [PMID: 37012269 PMCID: PMC10070443 DOI: 10.1038/s41598-023-32367-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 03/27/2023] [Indexed: 04/05/2023] Open
Abstract
This is a retrospective analysis. Quantitative prediction of the children's and adolescents' spherical equivalent based on their variable-length historical vision records. From October 2019 to March 2022, we examined uncorrected visual acuity, sphere, astigmatism, axis, corneal curvature and axial length of 75,172 eyes from 37,586 children and adolescents aged 6-20 years in Chengdu, China. 80% samples consist of the training set, the 10% form the validation set and the remaining 10% form the testing set. Time-Aware Long Short-Term Memory was used to quantitatively predict the children's and adolescents' spherical equivalent within two and a half years. The mean absolute prediction error on the testing set was 0.103 ± 0.140 (D) for spherical equivalent, ranging from 0.040 ± 0.050 (D) to 0.187 ± 0.168 (D) if we consider different lengths of historical records and different prediction durations. Time-Aware Long Short-Term Memory was applied to captured the temporal features in irregularly sampled time series, which is more in line with the characteristics of real data and thus has higher applicability, and helps to identify the progression of myopia earlier. The overall error 0.103 (D) is much smaller than the criterion for clinically acceptable prediction, say 0.75 (D).
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Affiliation(s)
- Junjia Huang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Wei Ma
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
| | - Rong Li
- Eye See Inc., Chengdu, 610041, People's Republic of China
| | - Na Zhao
- Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kunming, 650091, People's Republic of China
- Computational Education Lab, SeekingTao Tech. Inc., Chengdu, 610095, People's Republic of China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China.
- Computational Education Lab, SeekingTao Tech. Inc., Chengdu, 610095, People's Republic of China.
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Sun C, Li H, Song M, Cai D, Zhang B, Hong S. Adaptive model training strategy for continuous classification of time series. APPL INTELL 2023; 53:1-19. [PMID: 36819946 PMCID: PMC9922045 DOI: 10.1007/s10489-022-04433-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2022] [Indexed: 02/13/2023]
Abstract
The classification of time series is essential in many real-world applications like healthcare. The class of a time series is usually labeled at the final time, but more and more time-sensitive applications require classifying time series continuously. For example, the outcome of a critical patient is only determined at the end, but he should be diagnosed at all times for timely treatment. For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS). Different from the existing single-shot classification, the key of CCTS is to model multiple distributions simultaneously due to the dynamic evolution of time series. But the deep learning model will encounter intertwined problems of catastrophic forgetting and over-fitting when learning multi-distribution. In this work, we found that the well-designed distribution division and replay strategies in the model training process can help to solve the problems. We propose a novel Adaptive model training strategy for CCTS (ACCTS). Its adaptability represents two aspects: (1) Adaptive multi-distribution extraction policy. Instead of the fixed rules and the prior knowledge, ACCTS extracts data distributions adaptive to the time series evolution and the model change; (2) Adaptive importance-based replay policy. Instead of reviewing all old distributions, ACCTS only replays important samples adaptive to their contribution to the model. Experiments on four real-world datasets show that our method outperforms all baselines.
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Affiliation(s)
- Chenxi Sun
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Hongyan Li
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Moxian Song
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Derun Cai
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Baofeng Zhang
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Health Science Center of Peking University, Beijing, China
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Continuous diagnosis and prognosis by controlling the update process of deep neural networks. PATTERNS (NEW YORK, N.Y.) 2023; 4:100687. [PMID: 36873902 PMCID: PMC9982300 DOI: 10.1016/j.patter.2023.100687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/07/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023]
Abstract
Continuous diagnosis and prognosis are essential for critical patients. They can provide more opportunities for timely treatment and rational allocation. Although deep-learning techniques have demonstrated superiority in many medical tasks, they frequently forget, overfit, and produce results too late when performing continuous diagnosis and prognosis. In this work, we summarize the four requirements; propose a concept, continuous classification of time series (CCTS); and design a training method for deep learning, restricted update strategy (RU). The RU outperforms all baselines and achieves average accuracies of 90%, 97%, and 85% on continuous sepsis prognosis, COVID-19 mortality prediction, and eight disease classifications, respectively. The RU can also endow deep learning with interpretability, exploring disease mechanisms through staging and biomarker discovery. We find four sepsis stages, three COVID-19 stages, and their respective biomarkers. Further, our approach is data and model agnostic. It can be applied to other diseases and even in other fields.
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Wendland P, Schmitt V, Zimmermann J, Häger L, Göpel S, Schenkel-Häger C, Kschischo M. Machine learning models for predicting severe COVID-19 outcomes in hospitals. INFORMATICS IN MEDICINE UNLOCKED 2023; 37:101188. [PMID: 36742350 PMCID: PMC9890886 DOI: 10.1016/j.imu.2023.101188] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023] Open
Abstract
The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.821) and the need for mechanical ventilation (AUC = 0.654) from a few laboratory data of the first 24 h after admission. Models based on dichotomous features indicating whether a laboratory value exceeds or falls below a threshold perform nearly as good as models based on numerical features. We devise completely data-driven and interpretable machine-learning models for the prediction of in-hospital mortality, transfer to ICU and mechanical ventilation for hospitalized Covid-19 patients within 24 h after admission. Numerical values of. CRP and blood sugar and dichotomous indicators for increased partial thromboplastin time (PTT) and glutamic oxaloacetic transaminase (GOT) are amongst the best predictors.
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Affiliation(s)
- Philipp Wendland
- University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany
| | - Vanessa Schmitt
- University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany
| | - Jörg Zimmermann
- University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany
| | - Lukas Häger
- University Clinic Tübingen, Department of Internal Medicine 1, Tübingen, DE, Germany
| | - Siri Göpel
- University Clinic Tübingen, Department of Internal Medicine 1, Tübingen, DE, Germany
| | - Christof Schenkel-Häger
- University of Applied Sciences Koblenz, Department of Economics and Social Care, Remagen, DE, Germany
| | - Maik Kschischo
- University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, DE, Germany
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Ye Y, Huang L, Wang J, Chuang YC, Pan L. Patient allocation method in major epidemics under the situation of hierarchical diagnosis and treatment. BMC Med Inform Decis Mak 2022; 22:331. [PMID: 36522752 PMCID: PMC9753027 DOI: 10.1186/s12911-022-02074-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES Patients are classified according to the severity of their condition and graded according to the diagnosis and treatment capacity of medical institutions. This study aims to correctly assign patients to medical institutions for treatment and develop patient allocation and medical resource expansion schemes among hospitals in the medical network. METHODS Illness severity, hospital level, allocation matching benefit, distance traveled, and emergency medical resource fairness were considered. A multi-objective planning method was used to construct a patient allocation model during major epidemics. A simulation study was carried out in two scenarios to test the proposed method. RESULTS (1) The single-objective model obtains an unbalanced solution in contrast to the multi-objective model. The proposed model considers multi-objective problems and balances the degree of patient allocation matching, distance traveled, and fairness. (2) The non-hierarchical model has crowded resources, and the hierarchical model assigns patients to matched medical institutions. (3) In the "demand exceeds supply" situation, the patient allocation model identified additional resources needed by each hospital. CONCLUSION Results verify the maneuverability and effectiveness of the proposed model. It can generate schemes for specific patient allocation and medical resource amplification and can serve as a quantitative decision-making tool in the context of major epidemics.
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Affiliation(s)
- Yong Ye
- Institute of Public Health and Emergency Management, Taizhou University, Taizhou, 318000 Zhejiang China
- Business College, Taizhou University, Taizhou, 318000 Zhejiang China
| | - Lizhen Huang
- Institute of Public Health and Emergency Management, Taizhou University, Taizhou, 318000 Zhejiang China
- Business College, Taizhou University, Taizhou, 318000 Zhejiang China
| | - Jie Wang
- School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000 Zhejiang China
| | - Yen-Ching Chuang
- Institute of Public Health and Emergency Management, Taizhou University, Taizhou, 318000 Zhejiang China
- Business College, Taizhou University, Taizhou, 318000 Zhejiang China
| | - Lingle Pan
- Zhejiang College of Security Technology, Wenzhou, 325000 Zhejiang China
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Rodrigues DS, Nastri ACS, Magri MM, Oliveira MSD, Sabino EC, Figueiredo PHMF, Levin AS, Freire MP, Harima LS, Nunes FLS, Ferreira JE, Busatto G, Bonfá E, Utiyama E, Segurado A, Perondi B, Morais AM, Montal A, Fusco S, Fregonesi M, Rocha M, Marcilio I, Rios IC, Kawano FYO, de Jesus MA, Kallas EG, Marmo C, Tanaka C, de Souza HP, Marchini JFM, Carvalho C, Ferreira JC, Guimaraes T, Lazari CS, Duarte AJS, Francisco MCPB, Costa SF. Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records. BMC Med Inform Decis Mak 2022; 22:187. [PMID: 35843930 PMCID: PMC9288836 DOI: 10.1186/s12911-022-01931-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 07/07/2022] [Indexed: 12/12/2022] Open
Abstract
Background COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. Methods We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clínicas (São Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient’s outcome. Results Time series-based machine learning models are capable of predicting a COVID-19 patient’s outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). Conclusions Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.
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Giotta M, Trerotoli P, Palmieri VO, Passerini F, Portincasa P, Dargenio I, Mokhtari J, Montagna MT, De Vito D. Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13016. [PMID: 36293594 PMCID: PMC9602523 DOI: 10.3390/ijerph192013016] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 05/05/2023]
Abstract
Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model's performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.
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Affiliation(s)
- Massimo Giotta
- School of Specialization in Medical Statistics and Biometry, School of Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Paolo Trerotoli
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Vincenzo Ostilio Palmieri
- Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Francesca Passerini
- Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Piero Portincasa
- Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Ilaria Dargenio
- School of Specialization in Medical Statistics and Biometry, School of Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Jihad Mokhtari
- Department of Basic Medical Sciences, Neurosciences, and Sense Organs, Medical School, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Maria Teresa Montagna
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Danila De Vito
- Department of Basic Medical Sciences, Neurosciences, and Sense Organs, Medical School, University of Bari Aldo Moro, 70121 Bari, Italy
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Xu Y, Trivedi A, Becker N, Blazes M, Ferres JL, Lee A, Conrad Liles W, Bhatraju PK. Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19. Sci Rep 2022; 12:16913. [PMID: 36209335 PMCID: PMC9547892 DOI: 10.1038/s41598-022-20724-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 09/19/2022] [Indexed: 12/29/2022] Open
Abstract
COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.
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Affiliation(s)
- Yixi Xu
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA
| | - Anusua Trivedi
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA
| | - Nicholas Becker
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA ,grid.34477.330000000122986657Computer Science and Engineering, University of Washington, Seattle, USA
| | - Marian Blazes
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA
| | - Juan Lavista Ferres
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA
| | - Aaron Lee
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA
| | - W. Conrad Liles
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.34477.330000000122986657Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, USA
| | - Pavan K. Bhatraju
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.34477.330000000122986657Pulmonary, Critical Care and Sleep Medicine, University of Washington Division of Pulmonary, Seattle, USA ,grid.34477.330000000122986657Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, USA
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12
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Soriano JB, Murthy S, Marshall JC, Relan P, Diaz JV. A clinical case definition of post-COVID-19 condition by a Delphi consensus. THE LANCET INFECTIOUS DISEASES 2022; 22:e102-e107. [PMID: 34951953 PMCID: PMC8691845 DOI: 10.1016/s1473-3099(21)00703-9] [Citation(s) in RCA: 1058] [Impact Index Per Article: 529.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/03/2021] [Accepted: 11/03/2021] [Indexed: 12/13/2022]
Abstract
People with COVID-19 might have sustained postinfection sequelae. Known by a variety of names, including long COVID or long-haul COVID, and listed in the ICD-10 classification as post-COVID-19 condition since September, 2020, this occurrence is variable in its expression and its impact. The absence of a globally standardised and agreed-upon definition hampers progress in characterisation of its epidemiology and the development of candidate treatments. In a WHO-led Delphi process, we engaged with an international panel of 265 patients, clinicians, researchers, and WHO staff to develop a consensus definition for this condition. 14 domains and 45 items were evaluated in two rounds of the Delphi process to create a final consensus definition for adults: post-COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include, but are not limited to, fatigue, shortness of breath, and cognitive dysfunction, and generally have an impact on everyday functioning. Symptoms might be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms might also fluctuate or relapse over time. A separate definition might be applicable for children. Although the consensus definition is likely to change as knowledge increases, this common framework provides a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy.
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Affiliation(s)
- Joan B Soriano
- Hospital Universitario de la Princesa, Servicio de Neumología, Madrid, Spain; Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain; Centro de Investigación en Red de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.
| | - Srinivas Murthy
- Division of Critical Care, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, Canada
| | - John C Marshall
- Department of Surgery, University of Toronto, Toronto, Canada
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13
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Soriano JB, Murthy S, Marshall JC, Relan P, Diaz JV. A clinical case definition of post-COVID-19 condition by a Delphi consensus. THE LANCET. INFECTIOUS DISEASES 2022. [PMID: 34951953 DOI: 10.1016/s1473-3099(21)00703-9/attachment/ef4fd06b-88fa-4a0c-b837-dcfee13e82d7/mmc1.pdf] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
People with COVID-19 might have sustained postinfection sequelae. Known by a variety of names, including long COVID or long-haul COVID, and listed in the ICD-10 classification as post-COVID-19 condition since September, 2020, this occurrence is variable in its expression and its impact. The absence of a globally standardised and agreed-upon definition hampers progress in characterisation of its epidemiology and the development of candidate treatments. In a WHO-led Delphi process, we engaged with an international panel of 265 patients, clinicians, researchers, and WHO staff to develop a consensus definition for this condition. 14 domains and 45 items were evaluated in two rounds of the Delphi process to create a final consensus definition for adults: post-COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include, but are not limited to, fatigue, shortness of breath, and cognitive dysfunction, and generally have an impact on everyday functioning. Symptoms might be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms might also fluctuate or relapse over time. A separate definition might be applicable for children. Although the consensus definition is likely to change as knowledge increases, this common framework provides a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy.
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Affiliation(s)
- Joan B Soriano
- Hospital Universitario de la Princesa, Servicio de Neumología, Madrid, Spain; Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain; Centro de Investigación en Red de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.
| | - Srinivas Murthy
- Division of Critical Care, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, Canada
| | - John C Marshall
- Department of Surgery, University of Toronto, Toronto, Canada
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Ahmad J, Saudagar AKJ, Malik KM, Ahmad W, Khan MB, Hasanat MHA, AlTameem A, AlKhathami M, Sajjad M. Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:480. [PMID: 35010740 PMCID: PMC8744904 DOI: 10.3390/ijerph19010480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/23/2021] [Accepted: 12/27/2021] [Indexed: 01/27/2023]
Abstract
The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.
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Affiliation(s)
- Jamil Ahmad
- Department of Computer Science, Islamia College Peshawar, Chartered University, Peshawar 25000, Pakistan; (J.A.); (M.S.)
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Khalid Mahmood Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA;
| | - Waseem Ahmad
- Lady Reading Hospital-Medical Teaching Institute, Peshawar 25000, Pakistan;
| | - Muhammad Badruddin Khan
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Mozaherul Hoque Abul Hasanat
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Abdullah AlTameem
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Mohammed AlKhathami
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.B.K.); (M.H.A.H.); (A.A.); (M.A.)
| | - Muhammad Sajjad
- Department of Computer Science, Islamia College Peshawar, Chartered University, Peshawar 25000, Pakistan; (J.A.); (M.S.)
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15
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Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters. SENSORS 2021; 21:s21248503. [PMID: 34960595 PMCID: PMC8705488 DOI: 10.3390/s21248503] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 12/26/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548–49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141–2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.
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16
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Xu Y, Trivedi A, Becker N, Blazes M, Ferres J, Lee A, Liles W, Bhatraju P. Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19. RESEARCH SQUARE 2021:rs.3.rs-1009310. [PMID: 34816256 PMCID: PMC8609901 DOI: 10.21203/rs.3.rs-1009310/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.
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17
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Chauhan T, Palivela H, Tiwari S. Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT DATA INSIGHTS 2021. [PMCID: PMC8189817 DOI: 10.1016/j.jjimei.2021.100020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
It’s been more than a year that the entire world is fighting against COVID-19 pandemic. Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its rapid progression. But seeking the importance towards the human situation, it has become essential to build such an automated model to diagnose COVID-19 within less computational time easily. As the disease has spread, there is not enough data to implement an accurate COVID-19 predicting model. But technology is a boon, which makes it possible. Effective techniques based on medical imaging using artificial intelligence have approached to assist humans in needful time. It has become very essential to detect COVID-19 in humans at an early stage to prevent it from becoming more infectious. The neural networks have shown promising results in medical imaging. In this research, a deep learning-based approach is used for image classification to detect COVID-19 using chest X-ray images (CXR). A CNN classifier have been used to classify the normal-healthy images from the COVID-19 images, using transfer learning. The concept of early stopping is used to enhance the accuracy of the proposed DenseNet model. The results of the system have been evaluated using accuracy, precision, recall and F1-score metrics. An automated comparative analysis among multiple optimizers, LR Scheduler and Loss Function is performed to get the highest accuracy suitable for the proposed system. The Adamax optimizer with Cross Entropy loss function and StepLR scheduler have outperformed with 98.45% accuracy for normal-healthy CXR images and 98.32% accuracy for COVID-19 images.
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18
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Ezugwu AE, Hashem IAT, Oyelade ON, Almutari M, Al-Garadi MA, Abdullahi IN, Otegbeye O, Shukla AK, Chiroma H. A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5546790. [PMID: 34518801 PMCID: PMC8434904 DOI: 10.1155/2021/5546790] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/17/2021] [Accepted: 08/17/2021] [Indexed: 12/25/2022]
Abstract
The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.
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Affiliation(s)
- Absalom E. Ezugwu
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg Campus, Pietermaritzburg, KwaZulu-Natal 3201, South Africa
| | | | - Olaide N. Oyelade
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg Campus, Pietermaritzburg, KwaZulu-Natal 3201, South Africa
| | - Mubarak Almutari
- College of Computer Science, University of Hafr Al Batin, Saudi Arabia
| | | | - Idris Nasir Abdullahi
- Department of Medical Laboratory Science, College of Medical Sciences, Ahmadu Bello University, Zaria, Nigeria
| | - Olumuyiwa Otegbeye
- School of Computer Science and Applied Mathematics, University of the Witwatersrand, South Africa
| | - Amit K. Shukla
- IRISA Laboratory, ENSSAT, University of Rennes 1, France
| | - Haruna Chiroma
- Future Technology Research Center, National Yunlin University of Science and Technology, Taiwan
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19
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Ortíz-Barrios MA, Coba-Blanco DM, Alfaro-Saíz JJ, Stand-González D. Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8814. [PMID: 34444561 PMCID: PMC8392152 DOI: 10.3390/ijerph18168814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/23/2022]
Abstract
The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.
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Affiliation(s)
- Miguel Angel Ortíz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia; (D.M.C.-B.); (D.S.-G.)
| | - Dayana Milena Coba-Blanco
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia; (D.M.C.-B.); (D.S.-G.)
| | - Juan-José Alfaro-Saíz
- Research Centre on Production Management and Engineering, Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Daniela Stand-González
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia; (D.M.C.-B.); (D.S.-G.)
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20
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Personalized vital signs control based on continuous action-space reinforcement learning with supervised experience. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102847] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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21
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Aktar S, Talukder A, Ahamad MM, Kamal AHM, Khan JR, Protikuzzaman M, Hossain N, Azad AKM, Quinn JMW, Summers MA, Liaw T, Eapen V, Moni MA. Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19. Diagnostics (Basel) 2021; 11:1383. [PMID: 34441317 PMCID: PMC8393412 DOI: 10.3390/diagnostics11081383] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/12/2021] [Accepted: 07/29/2021] [Indexed: 02/06/2023] Open
Abstract
Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes, and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.
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Affiliation(s)
- Sakifa Aktar
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (S.A.); (M.M.A.); (M.P.)
| | - Ashis Talukder
- Statistics Discipline, Khulna University, Khulna 9208, Bangladesh;
| | - Md. Martuza Ahamad
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (S.A.); (M.M.A.); (M.P.)
| | - A. H. M. Kamal
- Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh 2220, Bangladesh;
| | - Jahidur Rahman Khan
- Health Research Institute, University of Canberra, Canberra, ACT 2617, Australia;
| | - Md. Protikuzzaman
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (S.A.); (M.M.A.); (M.P.)
| | - Nasif Hossain
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki 852-8523, Japan;
| | - A. K. M. Azad
- Faculty of Science, Engineering & Technology, Swinburne University of Technology Sydney, Sydney, VIC 2150, Australia;
| | - Julian M. W. Quinn
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia; (J.M.W.Q.); (M.A.S.)
| | - Mathew A. Summers
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia; (J.M.W.Q.); (M.A.S.)
- St Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW 2010, Australia
| | - Teng Liaw
- School of Health & Rehabilitation Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;
| | - Valsamma Eapen
- World Health Organization (WHO) Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Mohammad Ali Moni
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia; (J.M.W.Q.); (M.A.S.)
- School of Health & Rehabilitation Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;
- World Health Organization (WHO) Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia
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Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11062466] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The emergence of COVID-19 progressed into a global pandemic that has functionally put the world at a standstill and catapulted major healthcare systems into an overburdened state. The dire need for therapeutic strategies to mitigate and successfully treat COVID-19 is now a public health crisis with national security implications for many countries. The current study employed Bayesian networks to a longitudinal proteomic dataset generated from Caco-2 cells transfected with SARS-CoV-2 (isolated from patients returning from Wuhan to Frankfurt). Two different approaches were employed to assess the Bayesian models, a titer-center topology analysis and a drug signature enrichment analysis. Topology analysis identified a set of proteins directly linked to the SAR-CoV2 titer, including ACE2, a SARS-CoV-2 binding receptor, MAOB and CHECK1. Aligning with the topology analysis, MAOB and CHECK1 were also identified within the enriched drug-signatures. Taken together, the data output from this network has identified nodal host proteins that may be connected to 18 chemical compounds, some already marketed, which provides an immediate opportunity to rapidly triage these assets for safety and efficacy against COVID-19.
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1661] [Impact Index Per Article: 415.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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