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.

George 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.

ICTS4eHealth Program

Physically presented papers

Sunday 5, Session in common with the Workshops, 11.30 – 13.00

• iHelp: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records
• Analysis of Comorbidities of Alcohol Use Disorder

Virtually presented papers

Day 1: Monday 6

10.00 – 11.30 Session 1: IOT and Architectures -1

• Welcome
• A FHIR Based Architecture of a Multiprotocol IoT Home Gateway Supporting Dynamic Plug of New Devices Within Instrumented Environments
• Insights and Lessons Learned from Trialling a Mental Health Chatbot in the Wild
• Multidimensional Design and Analysis of a Data Mart Related to Healthcare Treatments with Biologic Drugs
• Blockchain-Based Strategy to Avoid Fake AI in eHealth Scenarios with Reinforcement Learning

12.00 – 13.30 Session 2: Machine Learning -1

• Understanding a Happiness Dataset: How the Machine Learning Classification Accuracy Changes with Different Demographic Groups
• Adverse Event Report Classification in Social and Medico-Social Sector
• Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models
• K-Nearest Neighbor Algorithm: Proposed Solution for Human Gait Data Classification

15.00 – 16.30 Session 3: ICT for Covid-19

• Machine Learning Techniques for Extracting Relevant Features from Clinical Data for COVID-19 Mortality Prediction
• An Agent-Based Model to Study the Impact of Non-Pharmaceutical Interventions During Covid-19 Pandemic on Social Isolation
• ARAIG and Minecraft: A COVID-19 Workaround
• Simulating and Predicting the Active Cases and Hospitalization Considering the Second Wave of COVID-19

16.30 – 18.00 Session 4: Machine Learning – 2

• BacillusNet: An Automated Approach Using RetinaNet for Segmentation of Pulmonary Tuberculosis Bacillus
• Automatic Face Mask Detection Using Deep Learning
• Forecasting Trends in an Ambient Assisted Living Environment Using Deep Learning
• Using Machine Learning Techniques to Predict RT-PCR Results for COVID-19 Patients

18.00 – 19.15 Keynote Speech

Speaker: 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

Subject 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/)

Day 2: Tuesday 7

11.30 – 13.00 Session 5: Images and Homecare Monitoring

• A DICOM Standard Pipeline for Microscope Imaging Modalities
• Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling
• A Deep Learning Assisted Digital Nutrition Diary to Support Nutrition Counseling for People Affected by the Geriatric Frailty Syndrome
• Your Privacy Preference Matters: A Qualitative Study Envisioned for Homecare

15.00 – 16.30 Session 6: Deep Learning

• End-To-End Semantic Joint Detection and Limb-Pose Estimation from Depth Images of Preterm Infants in NICUs
• Lightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction
• Tumor Segmentation in Breast DCE-MRI Slice Using Deep Learning Methods
• An Automated Method for Segmentation of COVID-19 Lesions Based on Computed
  Tomography Using Deep Learning Methods

16.30 – 18.00 Session 7: IoT and Architectures – 2

• “Can You Help Me Measure My Blood Sugar? Co-Design of a Voice Interface to Assist Patients and Caregivers at Home
• End-Stage Renal Disease Self-Management: Mobile App Development
• Towards Smart Tele-Medical Laboratory: Where We Are, Issues, and Future Challenges
• Ecological Momentary Assessment eXtensions 3 (EMAX3) Proposal: An App for EMA-Type Research
• Best paper announcement
• Conclusions