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Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network [article]

Xi Sheryl Zhang, Jingyuan Chou, Fei Wang
2019 arXiv   pre-print
In this paper, we proposed a framework, Memory-Based Graph Convolution Network (MemGCN), to perform integrative analysis with such multi-modal data.  ...  Taking computational medicine as an example, we have both Electronic Health Records (EHR) and medical images for each patient.  ...  ACKNOWLEDGEMENT Discovery, Pfizer, Piramal, Roche, Sanofi, Servier, TEVA, UCB and Golub Capital.  ... 
arXiv:1809.06018v4 fatcat:tqawiaohcfcrfo4kazsx2fhm5y

Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges

Gavin Tsang, Xianghua Xie, Shangming Zhou
2019 IEEE Reviews in Biomedical Engineering  
Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced  ...  Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging  ...  Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less efforts have been made to make use of integrated heterogeneous data via advanced  ... 
doi:10.1109/rbme.2019.2904488 pmid:30872241 fatcat:2733gyaiovcvlffekjfqqdjmt4

Modern Views of Machine Learning for Precision Psychiatry [article]

Zhe Sage Chen, Prathamesh Kulkarni, Isaac R. Galatzer-Levy, Benedetta Bigio, Carla Nasca, Yu Zhang
2022 arXiv   pre-print
Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health.  ...  In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and  ...  .), the National Institutes of Health (R01-NS121776 and R01-MH118928 to Z.S.C.). We thank Robert MacKay for English proofreading.  ... 
arXiv:2204.01607v2 fatcat:coo557v2jzh6debycy3mhccfze

A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia from EEG Connectivity Patterns

Chun-Ren Phang, Fuad Mohammed Noman, Hadri Hussain, Chee-Ming Ting, Hernando Ombao
2019 IEEE journal of biomedical and health informatics  
We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients.  ...  We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of 1D and 2D CNNs to integrate the features from various domains and dimensions using different fusion strategies.  ...  Complex network analysis based on graph theory [18, 19] has also revealed altered topological organization of brain connectome in SZ patients.  ... 
doi:10.1109/jbhi.2019.2941222 pmid:31536026 fatcat:ivgkvbmqsvfmhiyxq2ha6sumh4

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis.  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis [article]

Guixiang Ma, Nesreen K. Ahmed, Ted Willke, Dipanjan Sengupta, Michael W. Cole, Nicholas B. Turk-Browne, Philip S. Yu
2019 arXiv   pre-print
The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks using two graph convolutional networks as the twin networks.  ...  Experimental results on four real fMRI datasets demonstrate the potential use cases of the proposed framework for multi-subject brain analysis in health and neuropsychiatric disorders.  ...  Our proposed framework performs higher-order convolutions by incorporating higher-order proximity via random walks in graph convolutional networks.  ... 
arXiv:1811.02662v5 fatcat:aovkttg67ffm5gtprk57tlqdpq

An Overview of Deep Learning Techniques for Epileptic Seizures Detection and Prediction Based on Neuroimaging Modalities: Methods, Challenges, and Future Works [article]

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Yinan Kong, Juan Manuel Gorriz, Javier Ramírez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya
2022 arXiv   pre-print
One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities.  ...  First, DL-based CADS for the epileptic seizures detection and prediction using neuroimaging modalities are discussed.  ...  S., and Westover, M. B. (2018). Eeg classification via convolutional neural network-based interictal epileptiform event detection.  ... 
arXiv:2105.14278v2 fatcat:dxv3nkbyajetjokhf5eitttpgi

COMPUTATIONAL MODELING OF DEMENTIA PREDICTION USING DEEP NEURAL NETWORK: ANALYSIS ON OASIS DATASET

Shakila Basheer, Surbhi Bhatia, Sapiah Sakri
2021 IEEE Access  
Conflicts of Interest: The authors declare no conflict of interest. Appendi x A Showing Correlation between various features Box plot  ...  Acknowledgments: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research through project number "PNU-DRI-RI -20  ...  EHR (Electronic health record) datasets from OLDW (Optum labs data warehouse) are very helpful in claiming the performance of this model.  ... 
doi:10.1109/access.2021.3066213 fatcat:yz6uz5mpwbdoxlstnc7pur74cm

"Small World" architecture in brain connectivity and hippocampal volume in Alzheimer's disease: a study via graph theory from EEG data

Fabrizio Vecchio, Francesca Miraglia, Francesca Piludu, Giuseppe Granata, Roberto Romanello, Massimo Caulo, Valeria Onofrj, Placido Bramanti, Cesare Colosimo, Paolo Maria Rossini
2016 Brain Imaging and Behavior  
Weighted and undirected networks were built by the eLORETA solutions of the cortical sources' activities moving from EEG recordings.  ...  and volumetric segmentation using the Freesurfer image analysis software.  ...  Regarding this kind of functional network analysis, Watts and Strogatz introduced the concept of 'small-world' networks, allowing for an optimal balance between local specialization (C) and global integration  ... 
doi:10.1007/s11682-016-9528-3 pmid:26960946 fatcat:4lo4yuzayjhdhh4gkl7ql4ylki

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis.  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  The poses that patients occupy carry important information about their physical and mental health [5] .  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM)

Mohammed Rashad Baker, D. Lakshmi Padmaja, R. Puviarasi, Suman Mann, Jeidy Panduro-Ramirez, Mohit Tiwari, Issah Abubakari Samori, Deepika Koundal
2022 Computational and Mathematical Methods in Medicine  
affect the accuracy of neuroimaging discrimination.  ...  Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.  ...  The integration of traditional neural networks is constantly contributing to the field of neuroimaging research.  ... 
doi:10.1155/2022/6501975 pmid:35465018 pmcid:PMC9023163 fatcat:lwvelihzlrhm7kqw4kamgwhwju

Deep Learning in Mining Biological Data

Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain
2021 Cognitive Computation  
Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied  ...  To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the  ...  Acknowledgements The authors would like to thank the members of the acslab (http://www.acsla b.info/) for valuable discussions. Author Contributions  ... 
doi:10.1007/s12559-020-09773-x pmid:33425045 pmcid:PMC7783296 fatcat:n4nk7gakfbb4fbhdi5pqeojwjm

Graph Representation Learning in Biomedicine [article]

Michelle M. Li, Kexin Huang, Marinka Zitnik
2022 arXiv   pre-print
Exemplary domains covered include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients,  ...  Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge.  ...  Acknowledgements We gratefully acknowledge the support of NSF under Nos. IIS-and IIS-, US Air Force Contract No.  ... 
arXiv:2104.04883v3 fatcat:lrhxlztborbylazvdfmaxk5zem

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

Saman Sargolzaei
2021 Frontiers in Computational Neuroscience  
Neurological disorders dramatically impact patients of any age population, their families, and societies.  ...  Deep learning has recently gained an ever-increasing role in the era of health and medical investigations.  ...  ****Model describes the core model utilized for deep learning given provided information in each reference (CNN, convolutional neural network; LSTM, long short-term memory; RNN, recurrent neural network  ... 
doi:10.3389/fncom.2021.670489 pmid:34025380 pmcid:PMC8131543 fatcat:7i2pc3x7lvcubabfjybt2xueeu

Task-related changes in degree centrality and local coherence of the posterior cingulate cortex after major cardiac surgery in older adults

Jeffrey N. Browndyke, Miles Berger, Patrick J. Smith, Todd B. Harshbarger, Zachary A. Monge, Viral Panchal, Tiffany L. Bisanar, Donald D. Glower, John H. Alexander, Roberto Cabeza, Kathleen Welsh-Bohmer, Mark F. Newman (+1 others)
2017 Human Brain Mapping  
graph theory-based FC metrics.  ...  Experimental design: Older cardiac surgery patients (n 5 25) completed a verbal N-back working memory task during MRI scanning and cognitive testing before and 6 weeks after surgery; nonsurgical controls  ...  CONFLICTS OF INTEREST All the authors declare that they have no conflicts of interest with the contents of this article. ORCID Jeffrey N. Browndyke http://orcid.org/0000-0002-8573-7073  ... 
doi:10.1002/hbm.23898 pmid:29164774 fatcat:cym6l2ij2bgljhwnmv5nmoyx5q
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