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Wang D, Wu Q, Hong D. Extracting default mode network based on graph neural network for resting state fMRI study. FRONTIERS IN NEUROIMAGING 2022; 1:963125. [PMID: 37555154 PMCID: PMC10406295 DOI: 10.3389/fnimg.2022.963125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/08/2022] [Indexed: 08/10/2023]
Abstract
Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously.
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Affiliation(s)
| | | | - Don Hong
- Program of Computational and Data Science, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN, United States
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2
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Dipnall JF, Page R, Du L, Costa M, Lyons RA, Cameron P, de Steiger R, Hau R, Bucknill A, Oppy A, Edwards E, Varma D, Jung MC, Gabbe BJ. Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol. PLoS One 2021; 16:e0257361. [PMID: 34555069 PMCID: PMC8460020 DOI: 10.1371/journal.pone.0257361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The "Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)" study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data. METHODS AND DESIGN Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS. DISCUSSION The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.
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Affiliation(s)
- Joanna F. Dipnall
- Clinical Registries, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - Richard Page
- School of Medicine, Deakin University, St. John of God Hospital, University Hospital Geelong, Geelong, Victoria, Australia
| | - Lan Du
- Department of Data Science & AI, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Matthew Costa
- Oxford Trauma and Emergency Care, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ronan A. Lyons
- Clinical Registries, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Health Data Research UK, Swansea University, Swansea, United Kingdom
- National Centre for Population Health and Wellbeing Research, Swansea University, Swansea, United Kingdom
| | - Peter Cameron
- Department of Epidemiology & Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- The Alfred Hospital, Prahran, Victoria, Australia
| | - Richard de Steiger
- Department of Surgery, University of Melbourne, Epworth HealthCare, Epworth, Richmond, Victoria, Australia
| | - Raphael Hau
- Eastern Health Clinical School, Monash University, Box Hill, Victoria, Australia
| | - Andrew Bucknill
- Department of Orthopaedic Surgery, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Oppy
- Department of Orthopaedic Surgery, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- The University of Melbourne, Melbourne, Victoria, Australia
- Epworth Healthcare, Melbourne, Victoria, Australia
| | - Elton Edwards
- Department of Epidemiology & Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- The Alfred Hospital, Prahran, Victoria, Australia
| | - Dinesh Varma
- Department of Surgery, Monash University, Melbourne, Australia
- National Trauma Research Institute, Melbourne, Australia
- Department of Radiology, Alfred Hospital, Melbourne, Australia
| | - Myong Chol Jung
- Clinical Registries, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Data Science & AI, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Belinda J. Gabbe
- Clinical Registries, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- National Centre for Population Health and Wellbeing Research, Swansea University, Swansea, United Kingdom
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3
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Klén R, Karhunen M, Elo LL. Likelihood contrasts: a machine learning algorithm for binary classification of longitudinal data. Sci Rep 2020; 10:1016. [PMID: 31974488 PMCID: PMC6978422 DOI: 10.1038/s41598-020-57924-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 12/31/2019] [Indexed: 12/02/2022] Open
Abstract
Machine learning methods have gained increased popularity in biomedical research during the recent years. However, very few of them support the analysis of longitudinal data, where several samples are collected from an individual over time. Additionally, most of the available longitudinal machine learning methods assume that the measurements are aligned in time, which is often not the case in real data. Here, we introduce a robust longitudinal machine learning method, named likelihood contrasts (LC), which supports study designs with unaligned time points. Our LC method is a binary classifier, which uses linear mixed models for modelling and log-likelihood for decision making. To demonstrate the benefits of our approach, we compared it with existing methods in four simulated and three real data sets. In each simulated data set, LC was the most accurate method, while the real data sets further supported the robust performance of the method. LC is also computationally efficient and easy to use.
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Affiliation(s)
- Riku Klén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland
| | - Markku Karhunen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
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4
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Ding X, Gellad ZF, Mather C, Barth P, Poon EG, Newman M, Goldstein BA. Designing risk prediction models for ambulatory no-shows across different specialties and clinics. J Am Med Inform Assoc 2019; 25:924-930. [PMID: 29444283 PMCID: PMC6077778 DOI: 10.1093/jamia/ocy002] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 01/07/2018] [Indexed: 12/31/2022] Open
Abstract
Objective As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.
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Affiliation(s)
- Xiruo Ding
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, 27710, USA
| | - Ziad F Gellad
- Department of Medicine, Duke University, Durham, North Carolina, 27703, USA.,Department of Medicine, Durham VA Medical Center, Durham, North Carolina, 27705, USA
| | - Chad Mather
- Department of Medicine, Duke University, Durham, North Carolina, 27703, USA
| | - Pamela Barth
- Duke Health Technology Solutions, Duke University, Durham, North Carolina, 27713, USA
| | - Eric G Poon
- Duke Health Technology Solutions, Duke University, Durham, North Carolina, 27713, USA
| | - Mark Newman
- Department of Anesthesiology, University of Kentucky, Lexington, Kentucky, 40536, USA
| | - Benjamin A Goldstein
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, 27710, USA.,Center for Predictive Medicine, Duke Clinical Research Institute, Durham, North Carolina, 27705, USA
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5
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Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J 2017; 38:1805-1814. [PMID: 27436868 PMCID: PMC5837244 DOI: 10.1093/eurheartj/ehw302] [Citation(s) in RCA: 160] [Impact Index Per Article: 22.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 05/14/2016] [Accepted: 06/16/2016] [Indexed: 11/14/2022] Open
Abstract
Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning.
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Affiliation(s)
- Benjamin A. Goldstein
- Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin
Road, Suite 1104, Durham, NC 27705, USA
- Center for Predictive Medicine, Duke Clinical Research Institute, Durham,
NC, USA
| | - Ann Marie Navar
- Center for Predictive Medicine, Duke Clinical Research Institute, Durham,
NC, USA
| | - Rickey E. Carter
- Department of Health Sciences Research, Division of Biomedical Statistics
and Informatics, Mayo Clinic, Rochester, MN, USA
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Du W, Cheung H, Goldberg I, Thambisetty M, Becker K, Johnson CA. A Longitudinal Support Vector Regression for Prediction of ALS Score. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2016; 2015:1586-1590. [PMID: 27042700 DOI: 10.1109/bibm.2015.7359912] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Longitudinal studies play a key role in various fields, including epidemiology, clinical research, and genomic analysis. Currently, the most popular methods in longitudinal data analysis are model-driven regression approaches, which impose strong prior assumptions and are unable to scale to large problems in the manner of machine learning algorithms. In this work, we propose a novel longitudinal support vector regression (LSVR) algorithm that not only takes the advantage of one of the most popular machine learning methods, but also is able to model the temporal nature of longitudinal data by taking into account observational dependence within subjects. We test LSVR on publicly available data from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. Results suggest that LSVR is at a minimum competitive with favored machine learning methods and is able to outperform those methods in predicting ALS score one month in advance.
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Affiliation(s)
- Wei Du
- Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5624
| | - Huey Cheung
- Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5624
| | - Ilya Goldberg
- Intramural Research Program, National Institutes on Aging, Baltimore, MD 21224-6825
| | - Madhav Thambisetty
- Intramural Research Program, National Institutes on Aging, Baltimore, MD 21224-6825
| | - Kevin Becker
- Intramural Research Program, National Institutes on Aging, Baltimore, MD 21224-6825
| | - Calvin A Johnson
- Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5624
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7
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Hou J, Archer KJ. Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data. Stat Appl Genet Mol Biol 2015; 14:93-111. [PMID: 25720102 PMCID: PMC4454613 DOI: 10.1515/sagmb-2014-0004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract An ordinal scale is commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical methodology based on statistical inference, in particular, ordinal modeling has contributed to the analysis of data in which the response categories are ordered and the number of covariates (p) remains smaller than the sample size (n). With the emergence of genomic technologies being increasingly applied for more accurate diagnosis and prognosis, high-dimensional data where the number of covariates (p) is much larger than the number of samples (n), are generated. To meet the emerging needs, we introduce our proposed model which is a two-stage algorithm: Extend the generalized monotone incremental forward stagewise (GMIFS) method to the cumulative logit ordinal model; and combine the GMIFS procedure with the classical mixed-effects model for classifying disease status in disease progression along with time. We demonstrate the efficiency and accuracy of the proposed models in classification using a time-course microarray dataset collected from the Inflammation and the Host Response to Injury study.
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Affiliation(s)
- Jiayi Hou
- Department of Biostatistics, Virginia Commonwealth University.
| | - Kellie J. Archer
- Department of Biostatistics, Virginia Commonwealth University, 830 East Main St., Room 718, Richmond, VA 23298-0032, United States.
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8
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Zeng P, Zhao Y, Qian C, Zhang L, Zhang R, Gou J, Liu J, Liu L, Chen F. Statistical analysis for genome-wide association study. J Biomed Res 2014; 29:285-97. [PMID: 26243515 PMCID: PMC4547377 DOI: 10.7555/jbr.29.20140007] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Revised: 06/07/2014] [Accepted: 09/27/2014] [Indexed: 12/19/2022] Open
Abstract
In the past few years, genome-wide association study (GWAS) has made great successes in identifying genetic susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, set-based association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.,Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical College, Xuzhou, Jiangsu 221004, China
| | - Yang Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Cheng Qian
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Liwei Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Ruyang Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Jianwei Gou
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Jin Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Liya Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Feng Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
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