1
|
Ye W, Chen X, Li P, Tao Y, Wang Z, Gao C, Cheng J, Li F, Yi D, Wei Z, Yi D, Wu Y. OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features. Front Neurol 2023; 14:1158555. [PMID: 37416306 PMCID: PMC10321134 DOI: 10.3389/fneur.2023.1158555] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/22/2023] [Indexed: 07/08/2023] Open
Abstract
Background Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.
Collapse
Affiliation(s)
- Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Pengpeng Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yongjun Tao
- Department of Neurology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Jian Cheng
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Fang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dali Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
- Department of Health Education, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| |
Collapse
|
2
|
Miao C, Yu S, Zhang Y, Hu Y, He X, Chen W. Assessing outdoor air quality vertically in an urban street canyon and its response to microclimatic factors. J Environ Sci (China) 2023; 124:923-932. [PMID: 36182195 DOI: 10.1016/j.jes.2022.02.021] [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: 10/28/2021] [Revised: 01/09/2022] [Accepted: 02/12/2022] [Indexed: 06/16/2023]
Abstract
The vertical distribution of air pollutants in urban street canyons is closely related to residents' health. However, the vertical air quality in urban street canyons has rarely been assessed using field observations obtained throughout the year. Therefore, this study investigated the seasonal and annual concentrations of particulate matter (PM2.5 and PM10), CO, NO2, SO2, O3, air quality index, and their responses to microclimatic factors at three height levels (1.5, 27, and 69 m above street level) in an urban street canyon. The PM concentration was higher at 27 m than at 1.5 m in winter, whereas the situation was reversed in other seasons. It was found that photochemical pollutants such as NO2 and O3 were the primary pollutants in the urban street canyon. The days on which O3 was the primary pollutant at the height of 1.5 m accounted for 81.07% of the entire year. The days on which NO2 was the primary pollutant at the height of 27 and 69 m accounted for 82.49% and 72.33% of the entire year, respectively. Substantially higher concentrations of NO2 and O3 were found at the height of 27 m than at 69 m. In-canyon concentrations of NO2 and O3 were strongly correlated with air temperature, wind speed, and wind direction, which played important roles in photochemical reactions and pollutant dispersion.
Collapse
Affiliation(s)
- Chunping Miao
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Arboretum, Chinese Academy of Sciences, Shenyang 110016, China
| | - Shuai Yu
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Arboretum, Chinese Academy of Sciences, Shenyang 110016, China
| | - Yue Zhang
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Arboretum, Chinese Academy of Sciences, Shenyang 110016, China
| | - Yuanman Hu
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Xingyuan He
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Arboretum, Chinese Academy of Sciences, Shenyang 110016, China.
| | - Wei Chen
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Arboretum, Chinese Academy of Sciences, Shenyang 110016, China.
| |
Collapse
|
3
|
Bahathiq RA, Banjar H, Bamaga AK, Jarraya SK. Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front Neuroinform 2022; 16:949926. [PMID: 36246393 PMCID: PMC9554556 DOI: 10.3389/fninf.2022.949926] [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: 05/21/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.
Collapse
Affiliation(s)
- Reem Ahmed Bahathiq
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed K. Bamaga
- Neuromuscular Medicine Unit, Department of Pediatric, Faculty of Medicine and King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Salma Kammoun Jarraya
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
4
|
Zhao F, Pan H, Li N, Chen X, Zhang H, Mao N, Ren Y. High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder. Front Neurosci 2022; 16:976229. [PMID: 36017184 PMCID: PMC9396245 DOI: 10.3389/fnins.2022.976229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/15/2022] [Indexed: 12/04/2022] Open
Abstract
Brain functional network (BFN) based on electroencephalography (EEG) has been widely used to diagnose brain diseases, such as major depressive disorder (MDD). However, most existing BFNs only consider the correlation between two channels, ignoring the high-level interaction among multiple channels that contain more rich information for diagnosing brain diseases. In such a sense, the BFN is called low-order BFN (LO-BFN). In order to fully explore the high-level interactive information among multiple channels of the EEG signals, a scheme for constructing a high-order BFN (HO-BFN) based on the “correlation’s correlation” strategy is proposed in this paper. Specifically, the entire EEG time series is firstly divided into multiple epochs by sliding window. For each epoch, the short-term correlation between channels is calculated to construct a LO-BFN. The correlation time series of all channel pairs are formulated by these LO-BFNs obtained from all epochs to describe the dynamic change of short-term correlation along the time. To construct HO-BFN, we cluster all correlation time series to avoid the problems caused by high dimensionality, and the correlation of the average correlation time series from different clusters is calculated to reflect the high-order correlation among multiple channels. Experimental results demonstrate the efficiency of the proposed HO-BFN in MDD identification, and its integration with the LO-BFN can further improve the recognition rate.
Collapse
Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Hongxin Pan
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Na Li
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Yande Ren,
| |
Collapse
|
5
|
Abstract
High-throughput microarrays contain a huge number of genes. Determining the relationships between all these genes is a time-consuming computation. In this paper, the authors provide a parallel algorithm for finding the Pearson’s correlation coefficient between genes measured in the Affymetrix microarrays. The main idea in the proposed algorithm, ForkJoinPcc, mimics the well-known parallel programming model: the fork–join model. The parallel MATLAB APIs have been employed and evaluated on shared or distributed multiprocessing systems. Two performance metrics—the processing and communication times—have been used to assess the performance of the ForkJoinPcc. The experimental results reveal that the ForkJoinPcc algorithm achieves a substantial speedup on the cluster platform of 62× compared with a 3.8× speedup on the multicore platform.
Collapse
|
6
|
Wang LN, Tan GM, Zang CR. A network method to identify the dynamic changes of the data flow with spatio-temporal feature. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02591-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
7
|
Eslami T, Almuqhim F, Raiker JS, Saeed F. Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey. Front Neuroinform 2021; 14:575999. [PMID: 33551784 PMCID: PMC7855595 DOI: 10.3389/fninf.2020.575999] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/07/2020] [Indexed: 12/15/2022] Open
Abstract
Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.
Collapse
Affiliation(s)
- Taban Eslami
- Department of Computer Science, Western Michigan University, Kalamazoo, MI, United States
| | - Fahad Almuqhim
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
| | - Joseph S. Raiker
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Fahad Saeed
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
| |
Collapse
|
8
|
Iadarola P, Negrini M. Change of Title: From High-Throughput to BioTech. BIOTECH 2020; 9:biotech9040018. [PMID: 35822821 PMCID: PMC9258314 DOI: 10.3390/biotech9040018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 09/08/2020] [Indexed: 11/26/2022] Open
Affiliation(s)
- Paolo Iadarola
- Department of Biology and Biotechnologies “L.Spallanzani”, University of Pavia, 27100 Pavia, Italy;
| | - Massimo Negrini
- Department of Morphology, Surgery and Experimental Medicine, University of Ferrara, 44121 Ferrara, Italy
- Correspondence:
| |
Collapse
|
9
|
Rudas J, Martínez D, Castellanos G, Demertzi A, Martial C, Carriére M, Aubinet C, Soddu A, Laureys S, Gómez F. Time-Delay Latency of Resting-State Blood Oxygen Level-Dependent Signal Related to the Level of Consciousness in Patients with Severe Consciousness Impairment. Brain Connect 2020; 10:83-94. [DOI: 10.1089/brain.2019.0716] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Affiliation(s)
- Jorge Rudas
- Institute of Biotechnology, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Darwin Martínez
- Department of Computer Science, Universidad Nacional de Colombia, Bogotá, Colombia
- Department of Computer Science, Universidad Central de Colombia, Bogotá, Colombia
| | - Gabriel Castellanos
- Department of Physiological Sciences, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Athena Demertzi
- Physiology of Cognition Research Lab, GIGA-Consciousness, GIGA Institute, University of Liege, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Manon Carriére
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Charlène Aubinet
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Andrea Soddu
- Department of Physics and Astronomy, University of Western Ontario, London, Ontario
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Francisco Gómez
- Department of Mathematics, Universidad Nacional de Colombia, Bogotá, Colombia
| |
Collapse
|
10
|
Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F. ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data. Front Neuroinform 2019; 13:70. [PMID: 31827430 PMCID: PMC6890833 DOI: 10.3389/fninf.2019.00070] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 11/12/2019] [Indexed: 01/09/2023] Open
Abstract
Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron (SLP) which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1, 035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 min vs. 7 h on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).
Collapse
Affiliation(s)
- Taban Eslami
- Department of Computer Science, Western Michigan University, Kalamazoo, MI, United States
- School of Computing and Information Science, Florida International University, Miami, FL, United States
| | - Vahid Mirjalili
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Alvis Fong
- Department of Computer Science, Western Michigan University, Kalamazoo, MI, United States
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, United States
| | - Fahad Saeed
- School of Computing and Information Science, Florida International University, Miami, FL, United States
| |
Collapse
|
11
|
Saeed F. Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data. BIG DATA ANALYTICS 2018. [DOI: 10.1186/s41044-018-0033-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
|