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Yu S, Han S, Shi M, Harada M, Ge J, Li X, Cai X, Heier M, Karstenmüller G, Suhre K, Gieger C, Koenig W, Rathmann W, Peters A, Wang-Sattler R. Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function. Metabolites 2024; 14:258. [PMID: 38786735 PMCID: PMC11122941 DOI: 10.3390/metabo14050258] [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: 04/03/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.
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Affiliation(s)
- Shixiang Yu
- TUM School of Medicine and Health, Technical University of Munich, 81675 München, Germany; (S.Y.); (S.H.); (M.S.); (J.G.)
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Siyu Han
- TUM School of Medicine and Health, Technical University of Munich, 81675 München, Germany; (S.Y.); (S.H.); (M.S.); (J.G.)
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Mengya Shi
- TUM School of Medicine and Health, Technical University of Munich, 81675 München, Germany; (S.Y.); (S.H.); (M.S.); (J.G.)
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Makoto Harada
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Jianhong Ge
- TUM School of Medicine and Health, Technical University of Munich, 81675 München, Germany; (S.Y.); (S.H.); (M.S.); (J.G.)
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Xuening Li
- Biocomputing R&D Department, Beijing Huanyang Bole Consulting Co., Ltd., Beijing 100010, China;
| | - Xiang Cai
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541214, China;
| | - Margit Heier
- KORA Study Centre, University Hospital of Augsburg, 86153 Augsburg, Germany;
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Gabi Karstenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine and Director of the Bioinformatics Core, Doha 24144, Qatar;
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, 80636 München, Germany;
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, 40225 Düsseldorf, Germany;
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Pettenkofer School of Public Health, Faculty of Medicine, Ludwig-Maximilians-Universität München, 81377 München, Germany
| | - Rui Wang-Sattler
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
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Figueira JR, Oliveira HM, Serro AP, Colaço R, Froes F, Robalo Cordeiro C, Diniz A, Guimarães M. A multiple criteria approach for building a pandemic impact assessment composite indicator: The case of COVID-19 in Portugal. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 309:795-818. [PMID: 36688141 PMCID: PMC9847371 DOI: 10.1016/j.ejor.2023.01.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 01/14/2023] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic has caused major damage and disruption to social, economic, and health systems (among others). In addition, it has posed unprecedented challenges to public health and policy/decision-makers who have been responsible for designing and implementing measures to mitigate its strong negative impact. The Portuguese health authorities have used decision analysis techniques to assess the impact of the pandemic and implemented measures for counties, regions, or across the entire country. These decision tools have been subject to some criticism and many stakeholders requested novel approaches. In particular, those which considered the dynamic changes in the pandemic's behaviour due to new virus variants and vaccines. A multidisciplinary team formed by researchers from the COVID-19 Committee of Instituto Superior Técnico at the University of Lisbon (CCIST analyst team) and physicians from the Crisis Office of the Portuguese Medical Association (GCOM expert team) collaborated to create a new tool to help politicians and decision-makers to fight the pandemic. This paper presents the main steps that led to the building of a pandemic impact assessment composite indicator applied to the specific case of COVID-19 in Portugal. A multiple criteria approach based on an additive multi-attribute value theory aggregation model was used to build the pandemic assessment composite indicator. The parameters of the additive model were devised based on an interactive socio-technical and co-constructive process between the CCIST and GCOM team members. The deck of cards method was the adopted technical tool to assist in the assessment the value functions as well as in the assessment of the criteria weights. The final tool was presented at a press conference and had a powerful impact on the Portuguese media and on the main health decision-making stakeholders in the country. In this paper, a completed mathematical and graphical description of this tool is presented.
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Affiliation(s)
- José Rui Figueira
- CEGIST, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, Lisboa 1049-001, Portugal
| | | | - Ana Paula Serro
- CQE, Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | - Rogério Colaço
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Portugal
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López-Flores FJ, Lira-Barragán LF, Rubio-Castro E, El-Halwagi MM, Ponce-Ortega JM. Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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Khan JI, Ullah F, Lee S. Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model. CHAOS, SOLITONS, AND FRACTALS 2022; 165:112818. [PMID: 36338376 PMCID: PMC9618449 DOI: 10.1016/j.chaos.2022.112818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 06/16/2023]
Abstract
In this work, we propose a new mathematical modeling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.
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Affiliation(s)
- Junaid Iqbal Khan
- School of Electronics and Information Engineering, Korea Aerospace University, Goyang, 10540, South Korea
| | - Farman Ullah
- Department of Electrical and Computer Engineering, COMSATS University Islamabad-Attock, Punjab 43600, Pakistan
| | - Sungchang Lee
- School of Electronics and Information Engineering, Korea Aerospace University, Goyang, 10540, South Korea
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Palermo MB, Policarpo LM, Costa CAD, Righi RDR. Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022; 11:40. [PMID: 36249862 PMCID: PMC9553296 DOI: 10.1007/s13721-022-00384-0] [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] [Received: 06/30/2022] [Revised: 09/09/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022]
Abstract
This systematic review aims to study and classify machine learning models that predict pandemics' evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region's criticality and optimize hospitals' approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models' ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], I 2 =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.
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Affiliation(s)
- Marcelo Benedeti Palermo
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Lucas Micol Policarpo
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Cristiano André da Costa
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Rodrigo da Rosa Righi
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
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Ding L, Wang K, Zhang C, Zhang Y, Wang K, Li W, Wang J. A Machine Learning Algorithm for Predicting the Risk of Developing to M1b Stage of Patients With Germ Cell Testicular Cancer. Front Public Health 2022; 10:916513. [PMID: 35844840 PMCID: PMC9277219 DOI: 10.3389/fpubh.2022.916513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Distant metastasis other than non-regional lymph nodes and lung (i.e., M1b stage) significantly contributes to the poor survival prognosis of patients with germ cell testicular cancer (GCTC). The aim of this study was to develop a machine learning (ML) algorithm model to predict the risk of patients with GCTC developing the M1b stage, which can be used to assist in early intervention of patients. Methods The clinical and pathological data of patients with GCTC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Combing the patient's characteristic variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression(LR), eXtreme Gradient Boosting (XGBoost), light Gradient Boosting Machine (lightGBM), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor (kNN). Model performances were evaluated by 10-fold cross-receiver operating characteristic (ROC) curves, which calculated the area under the curve (AUC) of models for predictive accuracy. A total of 54 patients from our own center (October 2006 to June 2021) were collected as the external validation cohort. Results A total of 4,323 patients eligible for inclusion were screened for enrollment from the SEER database, of which 178 (4.12%) developing M1b stage. Multivariate logistic regression showed that lymph node dissection (LND), T stage, N stage, lung metastases, and distant lymph node metastases were the independent predictors of developing M1b stage risk. The models based on both the XGBoost and RF algorithms showed stable and efficient prediction performance in the training and external validation groups. Conclusion S-stage is not an independent factor for predicting the risk of developing the M1b stage of patients with GCTC. The ML models based on both XGBoost and RF algorithms have high predictive effectiveness and may be used to predict the risk of developing the M1b stage of patients with GCTC, which is of promising value in clinical decision-making. Models still need to be tested with a larger sample of real-world data.
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Affiliation(s)
- Li Ding
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Kun Wang
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Chi Zhang
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yang Zhang
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | | | - Wang Li
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Junqi Wang
- Department of Urology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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Incorporating Machine Learning for Thermal Engines Modeling in Industrial Waste Heat Recovery. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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