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DTSTART;VALUE=DATE:20240606
DTEND;VALUE=DATE:20240608
DTSTAMP:20260423T095708
CREATED:20240614T103753Z
LAST-MODIFIED:20240614T103753Z
UID:5492-1717632000-1717804799@ihelp-project.eu
SUMMARY:Dialogues in Neurodegenerative Disorders
DESCRIPTION:June 6th\, 2024\n\n\nHALL 1 – Innovations in Neurodegenerative Disease Research\nHALL 2 – Clinical Practices and Care\nHALL 3 – Family relations\, supportive environment\, and social networks\n\n\nHALL 1 – Innovations in Neurodegenerative Disease Research – Brain Banking and Biomarkers\nHALL 2 – Comprehensive Patient Care\, from Timely Diagnosis to Care Planning\nHALL 3 – Community health and care services\n\n\n\n\n\n\n\nJune 7th\, 2024\n\n\nHALL 1 – Novelties in Clinical Assessment\, Evaluation\, Treatment\nHALL 2 – Psychology in Care for Persons Living with Neurodegenerative Disorders\nHALL 3 – Digital Health\n\n\nHALL 1 – Prevention and Lifestyle Solutions\, Oral Health\, Speech and Sleep\nHALL 2 – Workshop with patients\nHALL 3 – Digital Health\n\n\nHALL 1 – Potential Novel Pharmacotherapies; Other Topics\nHALL 2 – Public health promotion and communication\nHALL 3 – Digital Health
URL:https://ihelp-project.eu/event/dialogues-in-neurodegenerative-disorders/
LOCATION:Ljubljana\, Slovenia
CATEGORIES:Conference
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20230808
DTEND;VALUE=DATE:20230811
DTSTAMP:20260423T095708
CREATED:20230607T133452Z
LAST-MODIFIED:20230710T095248Z
UID:5045-1691452800-1691711999@ihelp-project.eu
SUMMARY:ICAMCS 2023 iHelp Special Session
DESCRIPTION:AIPER: AI and Big Data Applications towards Person-centered eHealth\, Personalized Recommendations and Improved QoL\nIntroduction\nThe successful exploration and interpretation of all health-related data play a vital role [1]. Healthcare data are available in different forms (e.g.\, images\, signals\, wavelengths\, etc.). All these data may come from different healthcare entities (i.e.\, patients themselves\, healthcare professionals\, etc.)\, where more and more entities place data demands on other entities\, and many healthcare organizations find themselves overwhelmed with data\, but lacking truly valuable information. At the same time\, the use of electronic health records (EHRs) enables the management of patient data\, for health care as well as other purposes\, across any kind of institutional\, regional\, or national border. Data can thus be shared and used more effectively for quality assurance\, disease surveillance\, public health monitoring\, and research [2]. On top of that\, a crucial role in the huge expansion of healthcare data play the utilization of the Internet of Things (IoT) and of wearable and mobile devices. The collection of data from such sources\, called secondary data\, and their integration with primary (historical\, medical) data is of the highest importance in modern eHealth [3]. Most AI-based prevention and intervention care models and associated clinical decisions exploit minimal amounts of the patients’ personal data (e.g.\, part of the patient’s medical history). Hence\, other important data sources for personalized\, patient-centric clinical decisions (e.g.\, lifestyle and behavioral data\, real-world data (RWD)\, genetics\, and genomics) are largely underexploited. Lack of integrated data (e.g.\, lifestyle data\, Patient-Reported Outcome Measures (PROMs)\, Patient Reported Experience Measures (PREMs)\, and genomic data) from patients that would allow clinicians to make personalized treatment decisions as part of their clinical decisions limits the effectiveness of prevention and intervention strategies\, the potential of patient-centric interactions between HCPs\, healthcare authorities\, patients\, and caregivers [4]. Likewise\, many treatment protocols for many diseases target the prolongation of the patient’s life\, while ignoring the patient’s Quality of Life (QoL) and well-being as perceived by the patient themselves [5]. Timely diagnosis is critical in the modern healthcare domain\, as early detection allows for accurate planning and the implementation of appropriate and personalized interventions and treatments to improve the Quality of Life (QoL) of the affected individuals. Early identification of modifiable risk factors relies on healthcare professionals (HCPs) to possess sufficient and integrated knowledge\, appropriate care programs\, and evidence-based approaches towards specialized\, multidisciplinary services both in terms of prevention and diagnosis of diverse health-related factors. Lack of integrated knowledge bases and the existence of fragmented healthcare data results in a limited understanding of underlying causal risk factors for several diseases. Moreover\, a significant gap remains between the delivery of stratified healthcare leading to personalization as current approaches often take a one-size-fits-all approach. Personalization implies a level of precision that seeks to treat the patient as opposed to the disease\, taking into account as an example comorbidities\, genetic predisposition\, and environmental factors. To address these challenges the potential of advanced technologies (i.e.\, Artificial Intelligence (AI)\, Big Data\, and Ambient Assisted Living (AAL)) for accurate risk prediction\, prevention\, and intervention should be examined. The utilization of such advanced technologies coupled with the introduction of data spaces and integrated knowledge bases will foster and enhance the delivery of personalized recommendations\, prevention and intervention measures\, and the introduction of long-lasting behavioral and lifestyle changes. \nAim of the Special Session\nA crucial role in the huge expansion of healthcare data play the utilization of the Internet of Things (IoT) and wearable and mobile devices. However\, there is a lack of integrated primary and secondary data from patients that would allow clinicians to make personalized treatment decisions. In addition\, a significant gap remains between the delivery of stratified healthcare leading to personalization as current approaches often take a one-size-fits-all approach\, while also ignoring the patient’s Quality of Life (QoL). The special session will focus on research and development efforts in the domain of eHealth and the introduction of emerging AI and Big Data solutions required to overcome these lacks\, driven by the research outcomes in the framework of an EU-funded project\, iHelp [6]. \nTopics\nIn this context\, the aim of this Special Session is to present research results on technologies\, tools\, and methods that support the development\, deployment\, and operation of advanced AI and Big Data systems. The main topics of interest include\, but are not limited to: \n\nStandardization\, harmonization\, and interoperability of healthcare data\nIntegrated Health Records\nAI tools for enhanced personalized recommendations\nPatient Digital Twins (PDTs)\nDialogue Language and Platforms targeted at eHealth applications\nVirtual Health Coaches and Agents\nFuzzy Cognitive Maps (FCMs) on personalized eHealth\nExplainable AI (XAI) techniques in the eHealth\nClinical Decision Support Systems (CDSS)\nIntegrated Care Model Libraries of AI Algorithms\nHealth Data Spaces\nIntegrated Healthcare Knowledge Bases\nBlockchain-based Data Exchange and Management in eHealth\nRobotic Systems Improving QoL\nPersonalized monitoring and alerting eHealth solutions\nDelivery Mechanisms for Personalised Healthcare\, Real-time Feedback\, and Behavioural Analytics\nSerious Games and Gamification (SGG) approaches towards personalization of prevention and intervention measures\nFuture research challenges in eHealth\n\nFor Authors\nThis special session will only accept review original papers that have not been previously published. Papers must be prepared in English and should be formatted based on according to the conference and IEEE proceedings guidelines (available here); two-column Manuscript Templates for Conference Proceedings with a maximum allowed camera-ready paper length of eight (8) printed pages including text\, figures\, and references. \nAuthors should send their manuscripts directly to gmanias@unipi.gr and dimos@unipi.gr to ensure that their manuscripts will be reviewed and published in the context of the Special Session.\nAccepted papers will be published in the ICAMCS conference proceedings by the CPS\, will be indexed by several platforms\, such as IEEEXplore\, Google Scholar\, ISI/CPCI\, Scopus\, etc. (http://www.icamcs.co/submit.html) and will also be presented directly in the Special Session. All papers will be reviewed and evaluated by independent experts and selected based on their originality\, merit\, and relevance to the special session. \n  \nFees \n\n320€ for publication of one paper in the IEEE Proceedings of ICAMCS 2023. More info here. All papers should not exceed the maximum length of eight (8) pages.\nFor extra paper from the same author\, the fee is 160€.\n\nThe Special Session submission link is here. \nImportant Dates\n\nPaper submission deadline: July 23rd\, 2023\nAcceptance notification: July 25th\, 2023\nCamera-ready deadline: July 29th\, 2023\nRegistration deadline: July 31st\, 2023\nSpecial Session Days: August 8th-10th\, 2023\n\nThe Special Session will be held in the context of the ICAMCS conference from the 8th to the 10th of August in Lefkada\, Greece. \nSpecial Session Format and Agenda\nThe special session will be initiated by a demo application and presentation\, entitled “Clinical DSS and AI models for personalized recommendations and measures to raise awareness of relevant factors of pancreatic cancer” highlighting requirements and needs towards personalized eHealth solutions. Two talks will be provided by invited speakers\, followed by accepted paper presentations. \nInvited speakers\nWe have confirmed the participation of the following invited speakers with expertise within the field of AI-based personalized healthcare applications both from technical and medical perspectives: \n\nProf. Tanja Tomson\, Karolinska Institutet (tanja.tomson@ki.se)\nDr. Pavlos Kranas\, LeanXcale\, Spain (pavlos@leanxcale.com)\n\nSpecial Session chairs\n\nMr. George Manias: Department of Digital Systems\, University of Piraeus\nProf. Dimosthenis Kyriazis: Department of Digital Systems\, University of Piraeus\n\n \n \n \n \n\nReferences\n[1] Jayaraman\, P. P.\, et al.\, (2020). Healthcare 4.0: A review of frontiers in digital health. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery\, 10(2)\, e1350.\n[2] Atasoy\, H.\, Greenwood\, B. N.\, & McCullough\, J. S. (2019). The digitization of patient care: a review of the effects of electronic health records on health care quality and utilization. Annual review of public health\, 40\, 487-500.\n[3] Carroll\, J. K.\, Moorhead\, A.\, Bond\, R.\, LeBlanc\, W. G.\, Petrella\, R. J.\, & Fiscella\, K. (2017). Who uses mobile phone health apps and does use matter? A secondary data analytics approach. Journal of medical Internet research\, 19(4)\, e125.\n[4] Yaqoob\, I.\, Salah\, K.\, Jayaraman\, R.\, & Al-Hammadi\, Y. (2021). Blockchain for healthcare data management: opportunities\, challenges\, and future recommendations. Neural Computing and Applications\, 1-16.\n[5] Lorkowski\, J.\, Grzegorowska\, O.\, & Pokorski\, M. (2021). Artificial intelligence in the healthcare system: An overview. Best Practice in Health Care\, 1-10.\n[6] G. Manias et al.\, “iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records\,” 2021 IEEE Symposium on Computers and Communications (ISCC)\, 2021\, pp. 1-8\, doi: 10.1109/ISCC53001.2021.9631475.
URL:https://ihelp-project.eu/event/icamcs-2023-ihelp-special-session/
LOCATION:Ionian Blue\, A luxurious 5-Star Sea Resort\, Nikiana\, Lefkada\, Lefkada\, 31100
CATEGORIES:Conference,Workshop
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ORGANIZER;CN="iHelp Project":MAILTO:info@ihelp-project.eu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211208T080000
DTEND;TZID=UTC:20211210T170000
DTSTAMP:20260423T095708
CREATED:20211117T161001Z
LAST-MODIFIED:20211117T161213Z
UID:4455-1638950400-1639155600@ihelp-project.eu
SUMMARY:BioSMART Conference
DESCRIPTION:BioSMART will be the place where researchers from different Bio-engineering fields can meet and exchange ideas to build outstanding and innovative future projects. All accepted papers are expected to be included in IEEE Xplore and indexed by EI. \nDelegates can register to attend BioSMART either On-site in Paris and there will be also an Online version. \nConference Objectives \n\nBio-Signal and Image processing\nBio-engineering systems\nSmart Technologies and AI applications\n\nGeorge Marinos from the University of Piraeus Research Center present the iHelp project’s paper which will be published in the first track (~Bio-signal and image processing) because is closely related to the biomedical image processing field. \nPaper Description \nThis paper demonstrates a technique that can efficiently handle and manipulate CT Scan images annotated with survival information for the purpose of risk group identification in a patient’s cohort (or in other words in a dataset). Given the fact that we have a set of biomedical CT Scan images with survival annotations\, we can apply this technique to stratify the risk of survival and finally obtain clusters of patients that have similar CT Scans and also have similar survival distribution (in the same cluster) and have different CT Scans and their survival probability distribution differentiates (when they are in different clusters). This can be achieved by utilizing a Deep Convolutional Neural Network for Survival risk estimation in the way that is explained in the paper. Experiments in 3 different publically available datasets\, as presented in the paper\, have proven that this technique can be successfully used for survival risk stratification.
URL:https://ihelp-project.eu/event/biosmart-conference-2021/
LOCATION:Online
CATEGORIES:Conference
ATTACH;FMTTYPE=image/png:https://ihelp-project.eu/wp-content/uploads/2021/11/BioSMART-Conference_iHelp.png
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BEGIN:VEVENT
DTSTART;TZID=UTC:20210905T113000
DTEND;TZID=UTC:20210908T180000
DTSTAMP:20260423T095708
CREATED:20210917T151213Z
LAST-MODIFIED:20211009T093736Z
UID:4402-1630841400-1631124000@ihelp-project.eu
SUMMARY:ICTS4eHealth 2021 Conference
DESCRIPTION:Following five successful workshop editions\, ICTS4eHealth 2021 is the International IEEE Conference dedicated to ICT solutions for e-Health\, especially based on Cloud computing\, Internet of Things (IoT)\, and Computational Intelligence. The conference will bring together researchers from academia\, industry\, government\, and medical centers in order to present the state of the art in the emerging area of the use of cloud systems in connected health infrastructure and applications\, and the use of IoT and Computational Intelligence techniques in the area of eHealth. \nGeorge Manias from the University of Piraeus Research Center presented the iHelp project and the corresponding paper entitled “iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records” in the ICTS4eHealth2021 Conference. \n \n  \n\n\nICTS4eHealth Program\nPhysically presented papers\nSunday 5\, Session in common with the Workshops\, 11.30 – 13.00 \n\niHelp: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records\nAnalysis of Comorbidities of Alcohol Use Disorder\n\nVirtually presented papers\nDay 1: Monday 6 \n10.00 – 11.30 Session 1: IOT and Architectures -1 \n\nWelcome\nA FHIR Based Architecture of a Multiprotocol IoT Home Gateway Supporting Dynamic Plug of New Devices Within Instrumented Environments\nInsights and Lessons Learned from Trialling a Mental Health Chatbot in the Wild\nMultidimensional Design and Analysis of a Data Mart Related to Healthcare Treatments with Biologic Drugs\nBlockchain-Based Strategy to Avoid Fake AI in eHealth Scenarios with Reinforcement Learning\n\n12.00 – 13.30 Session 2: Machine Learning -1 \n\nUnderstanding a Happiness Dataset: How the Machine Learning Classification Accuracy Changes with Different Demographic Groups\nAdverse Event Report Classification in Social and Medico-Social Sector\nGrammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models\nK-Nearest Neighbor Algorithm: Proposed Solution for Human Gait Data Classification\n\n15.00 – 16.30 Session 3: ICT for Covid-19 \n\nMachine Learning Techniques for Extracting Relevant Features from Clinical Data for COVID-19 Mortality Prediction\nAn Agent-Based Model to Study the Impact of Non-Pharmaceutical Interventions During Covid-19 Pandemic on Social Isolation\nARAIG and Minecraft: A COVID-19 Workaround\nSimulating and Predicting the Active Cases and Hospitalization Considering the Second Wave of COVID-19\n\n16.30 – 18.00 Session 4: Machine Learning – 2 \n\nBacillusNet: An Automated Approach Using RetinaNet for Segmentation of Pulmonary Tuberculosis Bacillus\nAutomatic Face Mask Detection Using Deep Learning\nForecasting Trends in an Ambient Assisted Living Environment Using Deep Learning\nUsing Machine Learning Techniques to Predict RT-PCR Results for COVID-19 Patients\n\n18.00 – 19.15 Keynote Speech \nSpeaker: Professor Risto Miikkulainen\, Professor of Computer Science at the University of Texas at Austin (USA) and AVP of Evolutionary Intelligence at Cognizant Technology Solutions Precise title: to be defined \nSubject of the talk: discovering decision strategies through neuroevolution\, with an application and demo on optimizing non-pharmaceutical interventions in the COVID-19 pandemic (demo at: https://evolution.ml/demos/npidashboard/) \nDay 2: Tuesday 7 \n11.30 – 13.00 Session 5: Images and Homecare Monitoring \n\nA DICOM Standard Pipeline for Microscope Imaging Modalities\nExemplars and Counterexemplars Explanations for Image Classifiers\, Targeting Skin Lesion Labeling\nA Deep Learning Assisted Digital Nutrition Diary to Support Nutrition Counseling for People Affected by the Geriatric Frailty Syndrome\nYour Privacy Preference Matters: A Qualitative Study Envisioned for Homecare\n\n15.00 – 16.30 Session 6: Deep Learning \n\nEnd-To-End Semantic Joint Detection and Limb-Pose Estimation from Depth Images of Preterm Infants in NICUs\nLightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction\nTumor Segmentation in Breast DCE-MRI Slice Using Deep Learning Methods\nAn Automated Method for Segmentation of COVID-19 Lesions Based on Computed\nTomography Using Deep Learning Methods\n\n16.30 – 18.00 Session 7: IoT and Architectures – 2 \n\n“Can You Help Me Measure My Blood Sugar? Co-Design of a Voice Interface to Assist Patients and Caregivers at Home\nEnd-Stage Renal Disease Self-Management: Mobile App Development\nTowards Smart Tele-Medical Laboratory: Where We Are\, Issues\, and Future Challenges\nEcological Momentary Assessment eXtensions 3 (EMAX3) Proposal: An App for EMA-Type Research\nBest paper announcement\nConclusions\n\n 
URL:https://ihelp-project.eu/event/icts4ehealth-2021/
LOCATION:Online
CATEGORIES:Conference
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