Presented at 12th International Conference on Health Informatics (HEALTHINF 2019)
http://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=74026
Authors:
Romuald Carette, Mahmoud Elbattah, Federica Cilia, Gilles Dequen, Jean-Luc Guérin
Université de Picardie Jules Verne, France
mahmoud.elbattah@u-picardie.fr
Blindness Is defined as visual acuity in the better eye after best possible correction of < 3/60 or visual field less than or equal to 10° from point of fixation .
Avoidable blindness is either preventable or treatable.
Mainly caused by ocular diseases such as cataract, refractive errors, trachoma, Onchocerciasis and some eye conditions in children.
WHO’s early efforts on blindness prevention, starting in the 1950s and predating the formal establishment of a program for the prevention of blindness .
These efforts involved providing assistance to Member States to assess the magnitude of the problem and institute control activities, several research initiatives on treatment options.
The research activities included laboratory and field studies and, based on the results, strategies were evolved for the prevention and control of trachoma.
Coupling Simulation with Machine Learning:A Hybrid Approach for Elderly Disch...Mahmoud Elbattah
Coupling Simulation with Machine Learning:A Hybrid Approach for Elderly Discharge Planning
Paper presented at SIGSIM PADS 2016
https://dl.acm.org/citation.cfm?id=2901381
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Vision-Based Approach for Autism Diagnosis Using Transfer Learning and Eye-Tr...Mahmoud Elbattah
Presented at 2022 15th International Conference on Health Informatics (HEALTHINF)
Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen
Abstract
The potentials of Transfer Learning (TL) have been well-researched in areas such as Computer Vision and Natural Language Processing. This study aims to explore a novel application of TL to detect Autism Spectrum Disorder. We seek to develop an approach that combines TL and eye-tracking, which is commonly used for analyzing autistic features. The key idea is to transform eye-tracking scanpaths into a visual representation, which could facilitate using pretrained vision models. Our experiments implemented a set of widely used models including VGG-16, ResNet, and DenseNet. Our results showed that the TL approach could realize a promising accuracy of classification (ROC-AUC up to 0.78). The proposed approach is not claimed to provide superior performance compared to earlier work. However, the study is primarily thought to convey an interesting aspect regarding the use of (synthetic) visual representations of eye-tracking output as a means to transfer representations from models pretrained on large-scale datasets such as ImageNet.
NLP-Based Approach to Detect Autism Spectrum Disorder in Saccadic Eye MovementMahmoud Elbattah
Presented at 2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen
Abstract
Autism Spectrum Disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, yet it could be complicated by several factors. Standard tests typically require intensive efforts and experience, which calls for developing assistive tools. In this respect, this study aims to develop a Machine Learning-based approach to assist the diagnosis process. Our approach is based on learning the sequence-based patterns in the saccadic eye movements. The key idea is to represent eye-tracking records as textual strings describing the sequences of fixations and saccades. As such, the study could borrow Natural Language Processing (NLP) methods for transforming the raw eye-tracking data. The NLP-based transformation could yield interesting features for training classification models. The experimental results demonstrated that such representation could be beneficial in this regard. With standard ConvNet models, our approach could realize a promising accuracy of classification (ROC-AUC up to 0.84).
The e-patient: empowered or overwhelmed? Patient's perspective on new technol...jangeissler
"The e-patient: empowered or overwhelmed? Patient's perspective on new technologies", presented by Jan Geissler at EFGCP Annual Conference 2013 on "Virtual Future: Ethical dimensions of emerging technologies in clinical trials and research" on 29 January 2013 in Brussels
Blindness Is defined as visual acuity in the better eye after best possible correction of < 3/60 or visual field less than or equal to 10° from point of fixation .
Avoidable blindness is either preventable or treatable.
Mainly caused by ocular diseases such as cataract, refractive errors, trachoma, Onchocerciasis and some eye conditions in children.
WHO’s early efforts on blindness prevention, starting in the 1950s and predating the formal establishment of a program for the prevention of blindness .
These efforts involved providing assistance to Member States to assess the magnitude of the problem and institute control activities, several research initiatives on treatment options.
The research activities included laboratory and field studies and, based on the results, strategies were evolved for the prevention and control of trachoma.
Coupling Simulation with Machine Learning:A Hybrid Approach for Elderly Disch...Mahmoud Elbattah
Coupling Simulation with Machine Learning:A Hybrid Approach for Elderly Discharge Planning
Paper presented at SIGSIM PADS 2016
https://dl.acm.org/citation.cfm?id=2901381
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Vision-Based Approach for Autism Diagnosis Using Transfer Learning and Eye-Tr...Mahmoud Elbattah
Presented at 2022 15th International Conference on Health Informatics (HEALTHINF)
Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen
Abstract
The potentials of Transfer Learning (TL) have been well-researched in areas such as Computer Vision and Natural Language Processing. This study aims to explore a novel application of TL to detect Autism Spectrum Disorder. We seek to develop an approach that combines TL and eye-tracking, which is commonly used for analyzing autistic features. The key idea is to transform eye-tracking scanpaths into a visual representation, which could facilitate using pretrained vision models. Our experiments implemented a set of widely used models including VGG-16, ResNet, and DenseNet. Our results showed that the TL approach could realize a promising accuracy of classification (ROC-AUC up to 0.78). The proposed approach is not claimed to provide superior performance compared to earlier work. However, the study is primarily thought to convey an interesting aspect regarding the use of (synthetic) visual representations of eye-tracking output as a means to transfer representations from models pretrained on large-scale datasets such as ImageNet.
NLP-Based Approach to Detect Autism Spectrum Disorder in Saccadic Eye MovementMahmoud Elbattah
Presented at 2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen
Abstract
Autism Spectrum Disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, yet it could be complicated by several factors. Standard tests typically require intensive efforts and experience, which calls for developing assistive tools. In this respect, this study aims to develop a Machine Learning-based approach to assist the diagnosis process. Our approach is based on learning the sequence-based patterns in the saccadic eye movements. The key idea is to represent eye-tracking records as textual strings describing the sequences of fixations and saccades. As such, the study could borrow Natural Language Processing (NLP) methods for transforming the raw eye-tracking data. The NLP-based transformation could yield interesting features for training classification models. The experimental results demonstrated that such representation could be beneficial in this regard. With standard ConvNet models, our approach could realize a promising accuracy of classification (ROC-AUC up to 0.84).
The e-patient: empowered or overwhelmed? Patient's perspective on new technol...jangeissler
"The e-patient: empowered or overwhelmed? Patient's perspective on new technologies", presented by Jan Geissler at EFGCP Annual Conference 2013 on "Virtual Future: Ethical dimensions of emerging technologies in clinical trials and research" on 29 January 2013 in Brussels
A Descriptive Study to Assess the Level of Knowledge on Preventive Measures o...ijtsrd
The present study aim was a descriptive study to assess the level of knowledge on preventive measures of osteoarthritis among old age people at urban area. A quantitative research approach and quasi experimental research design have adopted for the present study. 50 samples were selected by the who are satisfied with inclusion criteria, they were selected by purposive sampling technique. A structured questionnaire to collect the demographic data and semi structured questionnaire to assess the level of knowledge on preventive measures. Among 50 clients 13 27.08 had inadequate knowledge and 35 72.92 had moderate adequate knowledge on preventive measures of osteoarthritis among old age people. The means score of knowledge on preventive measures of osteoarthritis among old age people was 12.48±2.58. The median score was 12.0 with minimum score of 7.0 and maximum score 19.0. Sathiyabama. G | Narmadha K | Nivetha. S "A Descriptive Study to Assess the Level of Knowledge on Preventive Measures of Osteoarthritis among Old Age People at Urban Area" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52203.pdf Paper URL: https://www.ijtsrd.com/medicine/nursing/52203/a-descriptive-study-to-assess-the-level-of-knowledge-on-preventive-measures-of-osteoarthritis-among-old-age-people-at-urban-area/sathiyabama-g
Digital healthcare refers to a broad range of categories such as mobile health, wearable devices, health information technology, telemedicine online platform and telehealth, and personalized medicine. Healthcare providers benefit from digital health as it gives them the tools to have a better view of the patient’s health, which gives them an extensive view of the patient, which allows them to give better healthcare to the patient. EMed HealthTech reveals the 10 digital healthcare trends to check in 2023.
AI-enabled Digital Transformation
Wearable tech and Continuous Health Monitoring
Better Privacy and Security
Universal Adoption of Telehealth
Use of Big Data and Analytics
Smart Implants
Augmented Reality and Virtual Reality
Nanomedicine
Investing in mental health
Social Determinants of Health (SDOH) and Healthcare Inequality
Request a free quote for any custom digital health services from EMed HealthTech.
Learning Embeddings from Free-text Triage Notes using Pretrained Transformer ...Mahmoud Elbattah
Presented at 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022)
Émilien Arnaud, Mahmoud Elbattah, Maxime Gingon, Gilles Dequen
Abstract
The advent of transformer models has allowed for tremendous progress in the Natural Language Processing (NLP) domain. Pretrained transformers could successfully deliver the state-of-the-art performance in a myriad of NLP tasks. This study presents an application of transformers to learn contextual embeddings from free-text triage notes, widely recorded at the emergency department. A large-scale retrospective cohort of triage notes of more than 260K records was provided by the University Hospital of Amiens-Picardy in France. We utilize a set of Bidirectional Encoder Representations from Transformers (BERT) for the French language. The quality of embeddings is empirically examined based on a set of clustering models. In this regard, we provide a comparative analysis of popular models including CamemBERT, FlauBERT, and mBART. The study could be generally regarded as an addition to the ongoing contributions of applying the BERT approach in the healthcare context.
NLP-Based Prediction of Medical Specialties at Hospital Admission Using Triag...Mahmoud Elbattah
Presented at 2021 IEEE International Conference on Healthcare Informatics (ICHI)
Émilien Arnaud, Mahmoud Elbattah, Maxime Gingon, Gilles Dequen
Abstract
Data Analytics is rapidly expanding within the healthcare domain to help develop strategies for improving the quality of care and curbing costs as well. Natural Language Processing (NLP) solutions have received particular attention whereas a large part of clinical data is stockpiled into unstructured physician or nursing notes. In this respect, we attempt to employ NLP to provide an early prediction of the medical specialties at hospital admission. The study uses a large-scale dataset including more than 260K ED records provided by the Amiens-Picardy University Hospital in France. Our approach aims to integrate structured data with unstructured textual notes recorded at the triage stage. On one hand, a standard MLP model is used against the typical set of features. On the other hand, a Convolutional Neural Network is used to operate over the textual data. While both learning components are conducted independently in parallel. The empirical results demonstrated a promising accuracy in general. It is conceived that the study could be an additional contribution to the mounting efforts of applying NLP methods in the healthcare domain.
Generative Modeling of Synthetic Eye-Tracking Data: NLP-Based Approach with R...Mahmoud Elbattah
Presented at 12th International Conference on Neural Computation Theory and Applications (NCTA)
Authors:
Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen
Abstract
This study explores a Machine Learning-based approach for generating synthetic eye-tracking data. In this respect, a novel application of Recurrent Neural Networks is experimented. Our approach is based on learning the sequence patterns of eye-tracking data. The key idea is to represent eye-tracking records as textual strings, which describe the sequences of fixations and saccades. The study therefore could borrow methods from the Natural Language Processing (NLP) domain for transforming the raw eye-tracking data. The NLP-based transformation is utilised to convert the high-dimensional eye-tracking data into an amenable representation for learning. Furthermore, the generative modeling could be implemented as a task of text generation. Our empirical experiments support further exploration and development of such NLP-driven approaches for the purpose of producing synthetic eye-tracking datasets for a variety of potential applications.
Multi-Channel ConvNet Approach to Predict the Risk of In-Hospital Mortality f...Mahmoud Elbattah
Presented at International Conference on Deep Learning Theory and Applications (DeLTA) 2020
https://www.scitepress.org/PublicationsDetail.aspx?ID=1HSktBRmyxE=
Authors:
Fabien Viton, Mahmoud Elbattah, Jean-Luc Guérin, Gilles Dequen
Université de Picardie Jules Verne (UPJV), France
mahmoud.elbattah@u-picardie.fr
Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-...Mahmoud Elbattah
Presented at Presented at 41st Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
https://ieeexplore.ieee.org/document/8856904
Authors:
Mahmoud Elbattah, Romuald Carette, Gilles Dequen, Jean-Luc Guérin, Federica Cilia
Université de Picardie Jules Verne, France
mahmoud.elbattah@u-picardie.fr
Designing Care Pathways Using Simulation Modeling and Machine LearningMahmoud Elbattah
Presented at Winter Simulation Conference 2018, Gothenburg, Sweden
Authors:
Mahmoud Elbattah, Owen Molloy, Bernard P. Zeigler
Summary:
The paper presents a framework that incorporates Simulation Modeling along with Machine Learning (ML) for the purpose of designing pathways and evaluating the return on investment of implementation. The study goes through a use case in relation to elderly healthcare in Ireland, with a particular focus on the hip-fracture care scheme. Initially, unsupervised ML is utilised to extract knowledge from the Irish Hip Fracture Database. Data clustering is specifically applied to learn potential insights pertaining to patient characteristics, care-related factors, and outcomes. Subsequently, the data-driven knowledge is utilised within the process of simulation model development. Generally, the framework is conceived to provide a systematic approach for developing healthcare policies that help optimise the quality and cost of care.
Clustering-Aided Approach for Predicting Patient Outcomes with Application to...Mahmoud Elbattah
Presented at Workshop on Health Intelligence (W3PHIAI) - AAAI 2017 Conference
https://aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15188
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Using Machine Learning to Predict Length of Stay and Discharge Destination fo...Mahmoud Elbattah
Using Machine Learning to Predict Length of Stay and Discharge Destination for Hip-Fracture Patients
Paper presented at Intelligent Systems Conference, London, 2016.
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
https://link.springer.com/chapter/10.1007/978-3-319-56994-9_15
https://www.researchgate.net/publication/319198340_Using_Machine_Learning_to_Predict_Length_of_Stay_and_Discharge_Destination_for_Hip-Fracture_Patients
Presenting a diversity of criteria that can be used to guide the selection of ERP systems. The criteria covers seven main groups including: i) Cost-Related, ii) Implementation Time, iii) Vendor-Related, iv) User-Related, v) Technology-Related, vi) System-Related, and vii)Organizational Requirements.
The paper below can be kindly cited in case of using the criteria.
Hegazy, A. E. F. A., ElBattah, M., & Kadry, M. (2012, October). Fuzzy-Based Framework for Enterprise Resource Planning System Selection. In Proceedings of the 22nd International Conference on Computer Theory and Applications (ICCTA), (pp. 139-147). IEEE.
https://ieeexplore.ieee.org/document/6523560/
ML-Aided Simulation: A Conceptual Framework for Integrating Simulation Models...Mahmoud Elbattah
ML-Aided Simulation: A Conceptual Framework for Integrating Simulation Models with Machine Learning
Paper presented at ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
https://dl.acm.org/citation.cfm?id=3200933
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
The Economic Burden of Hip Fractures among Elderly Patients in Ireland: A Com...Mahmoud Elbattah
Paper presented at the 34th International Conference of the System Dynamics Society, Delft, Netherlands - July 17-21, 2016
Full-Text available at:
https://www.systemdynamics.org/assets/conferences/2016/proceed/papers/P1332.pdf
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Using Simulation Modeling to Design Value-Based Healthcare SystemsMahmoud Elbattah
Paper presented at OR58 Annual Conference, Portsmouth, England
Full-text available at:
https://www.researchgate.net/publication/308138628_Using_Simulation_Modeling_to_Design_Value-Based_Healthcare_Systems
Authors:
Bernard P. Zeigler, Ernest L. Carter, Owen Molloy, Mahmoud Elbattah
The University of Arizona, Prince George's Health Department
, National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Large-Scale Ontology Storage and Query Using Graph Database-Oriented ApproachMahmoud Elbattah
Paper presented at 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).
Full-Text available at:
http://ieeexplore.ieee.org/document/7397191/
https://www.researchgate.net/publication/304414637_Large-Scale_Ontology_Storage_and_Query_Using_Graph_Database-Oriented_Approach
First Author:
Mahmoud Elbattah
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Towards Improving Modeling and Simulation of Clinical Pathways: Lessons Learn...Mahmoud Elbattah
Towards Improving Modeling and Simulation of Clinical Pathways: Lessons Learned and Future Insights
Paper presented at International Conference on Simulation and Modeling Methodologies, Technologies and Applications
(SimulTech) 2015
Full-Text available at:
https://www.researchgate.net/publication/284284807_Towards_Improving_Modeling_and_Simulation_of_Clinical_Pathways_Lessons_Learned_and_Future_Insights
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
FrrbaseViz-A Tool for Exploring Freebase Using Query-Driven VisualisationMahmoud Elbattah
A tool for the interactive exploration of Freebase schema using query-driven visualisation.
http://freebaseviz.apphb.com/
The original paper was presented at International Conference on Communication, Management and Information Technology (ICCMIT 2016).
https://www.researchgate.net/publication/321716603_FreebaseViz_Interactive_Exploration_of_Freebase_Schema_Using_Query-Driven_Visualisation
Author:
Mahmoud Elbattah
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Supply Chains Modelling and Simulation Framework:Graph-Driven Approach Using ...Mahmoud Elbattah
Supply Chains Modelling and Simulation Framework:Graph-Driven Approach Using Ontology-Based Semantic Networks and Graph Database
Paper presented at International Conference on Simulation and Modeling Methodologies, Technologies and Applications
(SimulTech) 2014
Full-Text available at:
https://www.researchgate.net/profile/Mahmoud_Elbattah2/publication/294087825_Supply_Chains_Modelling_and_Simulation_Framework_Graph-Driven_Approach_Using_Ontology-Based_Semantic_Networks_and_Graph_Database/links/56bdd57308ae373cf1aaa930.pdf
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
mahmoud.elbattah@nuigalway.ie
Learning about Systems Using Machine Learning:Towards More Data-Driven Feedba...Mahmoud Elbattah
Learning about Systems Using Machine Learning- Paper presented at Winter Simulation Conference 2017
http://ieeexplore.ieee.org/document/8247895/
Authors:
Mahmoud Elbattah and Owen Molloy
National University of Ireland Galway
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
Learning to Predict Autism Spectrum Disorder Based on the Visual Patterns of Eye-Tracking Scanpaths
1. 12th International Conference on Health Informatics (HEALTHINF 2019)
Learning to Predict Autism Spectrum Disorder Based
on the Visual Patterns of Eye-Tracking Scanpaths
Romuald Carette, Mahmoud Elbattah, Federica Cilia,
Gilles Dequen, Jean-Luc Guérin
Université de Picardie Jules Verne, France
mahmoud.elbattah@u-picardie.fr
https://mahmoud-elbattah.github.io/ML4Autism/
https://www.researchgate.net/publication/331784416_Learning_to_Predict_Autism_Spectrum_Disorder_based_on_the_Visual_Patterns_of_Eye-
tracking_Scanpaths
2. 12th International Conference on Health Informatics (HEALTHINF 2019)
Background:AutismSpectrumDisorder
• Autism Spectrum Disorder (ASD) is a pervasive developmental disorder
characterised by a set of impairments including social communication
problems. 1
• ASD has been considered to affect about 1% of the world’s population (US
Dep. of Health, 2018). 2
• The hallmark of autism is an impairment of the ability to make and maintain
eye contact. 3
2
1 L. Wing, and J. Gould, “Severe Impairments of Social Interaction and Associated Abnormalities in Children: Epidemiology and
Classification”. Journal of Autism and Developmental Disorders, 9(1), pp.11-29, 1979.
2 U.S. Department of Health & Human Services. Data and statistics | autism spectrum disorder (asd) | ncbddd | cdc, 2018.
URL: https://www.cdc.gov/ncbddd/autism/data.html.
3 Coonrod, E. E. and Stone, W. L. (2004). Early concerns of parents of children with autistic and nonautistic disorders. Infants
& Young Children, 17(3), 258–268.
3. 12th International Conference on Health Informatics (HEALTHINF 2019)
Background:Eye-TrackingTechnology
3
Image Source: https://imotions.com/blog/eye-tracking/
J.H. Goldberg, and J.I. Helfman, “Visual scanpath representation”, In Proceedings of the 2010 Symposium on Eye-Tracking
Research & Applications, ACM, 2010, pp. 203-210.
Gaze Scan-path
4. 12th International Conference on Health Informatics (HEALTHINF 2019)
KeyIdea:
Learningthe VisualPatternsofEye-TrackingScanpaths
4
5. 12th International Conference on Health Informatics (HEALTHINF 2019)
Data Description
• 59 participants.
• Avg age ≈7.88 years old.
• 547 images: 328 (Non-ASD), 219 (ASD)
• Image dimensions: 640x480
• Dataset available on Figshare
5
ASD Non-ASD
6. 12th International Conference on Health Informatics (HEALTHINF 2019)
Image Augmentation
• Augmentation was applied to produce variations of images based on a
random set of transformations (e.g. rotation, shearing).
• The augmented dataset contained more than 3K samples.
• Implemented using Keras.
6https://github.com/Mahmoud-Elbattah/Predicting_ASD
7. 12th International Conference on Health Informatics (HEALTHINF 2019)
Results:BinaryClassifierAccuracy(10-foldCross-Validation)
7https://github.com/Mahmoud-Elbattah/Predicting_ASD
8. 12th International Conference on Health Informatics (HEALTHINF 2019)
Results:Multi-LabelClassifierAccuracy(10-foldCross-Validation)
8https://github.com/Mahmoud-Elbattah/Predicting_ASD
10. 12th International Conference on Health Informatics (HEALTHINF 2019)
THANK YOU!
mahmoud.elbattah@u-picardie.fr
Find the original publication on:
• https://www.researchgate.net/publication/331784416_Learning_to_Predict_Autism_Spectrum_Disorder_b
ased_on_the_Visual_Patterns_of_Eye-tracking_Scanpaths
• http://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=74026
Dataset available on Figshare:
• https://figshare.com/articles/Visualization_of_Eye-
Tracking_Scanpaths_in_Autism_Spectrum_Disorder_Image_Dataset/7073087
Project Website:
• https://mahmoud-elbattah.github.io/ML4Autism/
• https://www.researchgate.net/project/Predicting-Autism-Spectrum-Disorder-Using-Machine-Learning-and-
Eye-Tracking
