Video introduction

PItch presentations

DIGIPREDICT project outcomes

This page presents the main scientific outcomes of the DIGIPREDICT project, reflecting its impact as a pionneer in the field of digital twin for healthcare applications.

Project public events

Public events organized by DIGIPREDICT

DIGIPREDICT First International Symposium

Enabling a revolution in personalized and precision healthcare

May 16-17, 2024, Ayia Napa, Cyprus

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Download the booklet

Project introductory video

Below you will find a short video explaining the main features of the DIGIPREDICT project.

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DIGIPREDICT video series

DIGIPREDICT brings together various fields of expertise with one single goal: digital twin to predict the progression of disease and the need for early intervention in infectious and cardiovascular diseases.

Discover the various aspects of the DIGIPREDICT research through our video series.

Protein detection based on Si Nanowire FET Sensor Arrays.

Luca Capua, EPFL

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Design and simulation of a CNT strain sensor with low mechanical cross-sensitivity.

Maximilian Aue

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Multimodal CMOS MEA chip for OoC applications

Mar Cóndor, IMEC-BE

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Towards designing a Digital Twin for ICU patients

Hojjat Karami, EPFL

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Development of an automatic, modularized and multiplexed heart-on-a-chip platform

Shao-Hsuan Kuo, UTWENTE

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Quadro-channel organ-on-chip for modelling and studying the blood-brain barrier

Mariia Zakharova, UTWENTE

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Respiration rate V&V – DIGIPREDICT Physiopatch

Roberto Garcia, IMEC-NL

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Bioimpedance spectroscopy to assess (chronic) inflammation

Lucas Lindeboom, IMEC-NL

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Scientific publications

1.

Karami, H., Ionescu, A. & Atienza, D.

Point-process-based Representation Learning for Electronic Health Records.

in 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) 1–4 (IEEE, 2023). doi: 10.1109/BHI58575.2023.10313499

2.

Sprunger, Y., Capua, L., Ernst, T., Barraud, S., Locca, D., Ionescu, A. & Saeidi, A.

pH Quantification in Human Dermal Interstitial Fluid Using Ultra-Thin SOI Silicon Nanowire ISFETs and a High-Sensitivity Constant-Current Approach.

Biosensors 13, 908 (2023). doi: 10.3390/bios13100908

3.

Sprunger, Y., Capua, L., Ernst, T., Barraud, S., Ionescu, A. & Saeidi, A.

Ultra-High Sensitivity Silicon Nanowire Array Biosensor Based on a Constant-Current Method for Continuous Real-Time pH and Protein Monitoring in Interstitial Fluid.

in ESSCIRC 2023- IEEE 49th European Solid State Circuits Conference (ESSCIRC) 153–156 (IEEE, 2023). doi: 10.1109/ESSCIRC59616.2023.10268731

4.

Yakymets, N., Zanetti, R., Ionescu, A. & Atienza, D.

Model-Based ISO 14971 Risk Management of EEG-Based Medical Devices.

in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 1–7 (IEEE, 2023). doi: 10.1109/EMBC40787.2023.10340131. Archive:

5.

Buttia, C., Llanaj, E., Raeisi-Dehkordi, H., Kastrati, L., Amiri, M., Meçani, R., Taneri, P. E., Ochoa, S. A. G., Raguindin, P. F., Wehrli, F., Khatami, F., Espínola, O. P., Rojas, L. Z., de Mortanges, A. P., Macharia-Nimietz, E. F., Alijla, F., Minder, B., Leichtle, A. B., Lüthi, N., Ehrhard, S., Que, Y.-A., Fernandes, L. K., Hautz, W. & Muka, T.

Prognostic models in COVID-19 infection that predict severity: a systematic review.

European Journal of Epidemiology (2023). doi: 10.1007/s10654-023-00973-x. Archive: PMC

6.

Orlandic, L., Thevenot, J., Teijeiro, T. & Atienza, D.

A Multimodal Dataset for Automatic Edge-AI Cough Detection.

in (IEEE, 2023). doi: 10.5281/ZENODO.7562332. Archive: Zenodo

7.

Lin, Q., Sijbers, W., Avdikou, C., Gomez, D., Biswas, D., Tacca, B. & Van Helleputte, N.

A Multichannel Electrochemical Sensor Interface IC for Bioreactor Monitoring.

IEEE Transactions on Biomedical Circuits and Systems 1–9 (2023). doi: 10.1109/TBCAS.2023.3315480. Archive: Zenodo

8.

Orlandic, L., Teijeiro, T. & Atienza, D.

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification.

Computer Methods and Programs in Biomedicine 241, 107743 (2023). doi: 10.1016/j.cmpb.2023.107743. Archive: arXiv

9.

Zanoli, S., Ansaloni, G., Teijeiro, T. & Atienza, D.

Event-based sampled ECG morphology reconstruction through self-similarity.

Computer Methods and Programs in Biomedicine 240, 107712 (2023). doi: 10.1016/j.cmpb.2023.107712. Archive: arXiv

10.

Zanoli, S., Ponzina, F., Teijeiro, T., Levisse, A. & Atienza, D.

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors.

IEEE Journal on Emerging and Selected Topics in Circuits and Systems 13, 489–501 (2023). doi: 10.1109/JETCAS.2023.3269623. Archive: arXiv

11.

Capua, L., Sprunger, Y., Elettro, H., Risch, F., Grammoustianou, A., Midahuen, R., Ernst, T., Barraud, S., Gill, R. & Ionescu, A. M.

Label-Free C-Reactive Protein Si Nanowire FET Sensor Arrays With Super-Nernstian Back-Gate Operation.

IEEE Transactions on Electron Devices 1–7 (2022). doi: 10.1109/TED.2022.3144108. Archive: Infoscience

12.

Capua, L., Sprunger, Y., Elettro, H., Grammoustianou, A., Midahuen, R., Ernst, T., Barraud, S., Gill, R. & Ionescu, A.

Double-Gate Si Nanowire FET Sensor Arrays For Label-Free C-Reactive Protein detection enabled by antibodies fragments and pseudo-super-Nernstian back-gate operation.

in 2021 IEEE International Electron Devices Meeting (IEDM) 16.2.1–16.2.4 (IEEE, 2021). doi: 10.1109/IEDM19574.2021.9720670. Archive: Infoscience

13.

Koloskova, A., Lin, T. & Stich, S. U.

An Improved Analysis of Gradient Tracking for Decentralized Machine Learning.

in Advances in Neural Information Processing Systems 34, (NIPS, 2021). Archive: arXiv

14.

Vogels, T., He, L., Koloskova, A., Karimireddy, S. P., Lin, T., Stich, S. U. & Jaggi, M.

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

in Advances in Neural Information Processing Systems 34, (NIPS, 2021). Archive: arXiv

15.

Kong, L., Lin, T., Koloskova, A., Jaggi, M. & Stich, S. U.

Consensus Control for Decentralized Deep Learning.

in Proceedings of the 38th International Conference on Machine Learning 139, 5686–5696 (PMLR, 2021). Archive: arXiv

16.

Lin, T., Karimireddy, S. P., Stich, S. U. & Jaggi, M.

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data.

in Proceedings of the 38th International Conference on Machine Learning 139, 6654–6665 (PMLR, 2021). Archive: Infoscience

Project publications

DIGIPREDICT regularly produces publishable reports which describe the project achievements. They are public and can be downloaded by clicking on the links below.

Second publishable summary

August 2023

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First publishable summary

February 2022

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017915 (DIGIPREDICT).

©2021 DIGIPREDICT Project – Developed by SCIPROM —