HeartBIT_4.0 - Application of innovative Medical Data Science technologies for heart diseases.

Informations projet

HEARTBIT_4.0

N° de convention de subvention: 857446

DOI

10.3030/857446

Projet clôturé

Date de signature de la CE 11 Juillet 2019

Date de début 1 Janvier 2020

Date de fin 31 Decembre 2022

Financé au titre de

• Twinning of research institutions

Coût total

€ 787 825,25

Contribution de l’UE

€ 787 825,25

Coordonné par

UNIWERSYTET MEDYCZNY IM PIASTOW SLASKICH WE WROCLAWIU
Poland

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Websites, patent fillings, videos etc. (4)

Preparation of scientific manuscripts and presentations for congresses including

Preparation of scientific manuscripts and presentations for congresses including . After 18

Summer school

Summer school Delivery date 3436

Participation in brokerage meetings across Europe

Participation in brokerage meetings across Europe Delivery date 3436

The website and social-media profiles, posters, leaflets and other materials

Documents, reports (4)

Educational activities (lectures, laboratories and workshops) finished with reports

Educational activities (lectures, laboratories and workshops) finished with reports. Delivery date: 1-15

Course materials
Lecture and Workshop Content (for four frontal lectures and team projects)

Lecture and Workshop Content (for four frontal lectures and team projects). Months: 7,13,15,20,24,28

Workshop and lectures materials and Report from training.

Workshop and lectures materials and Report from training. Delivery date: 12,24,30

Publications

Peer reviewed articles (9)

An artificial intelligence approach to guiding the management of heart failure patients using predictive models: a systematic review

Auteurs: Mikołaj Błaziak, Szymon Urban, Weronika Wietrzyk, Maksym Jura, Gracjan Iwanek, Bartłomiej Stańczykiewicz, Wiktor Kuliczkowski, Robert Zymliński, Maciej Pondel, Petr Berka, Dariusz Danel, Jan Biegus, Agnieszka Siennicka
Publié dans: Biomedicines, ISSN 2227-9059, 2022, ISSN 2227-9059
Éditeur: MDPI
DOI: 10.3390/biomedicines10092188

Identification of risk groups for mental disorders, headache and oral behaviors in adults during the COVID-19 pandemic

Auteurs: Mieszko Wieckiewicz, Dariusz Danel, Maciej Pondel, Joanna Smardz, Helena Martynowicz, Tomasz Wieczorek, Grzegorz Mazur, Robert Pudlo, Gniewko Wieckiewicz
Publié dans: Scientific Reports, Numéro 11/1, 2021, ISSN 2045-2322
Éditeur: Nature Publishing Group
DOI: 10.1038/s41598-021-90566-z

Machine learning approach to understand worsening renal function in acute heart failure

Auteurs: Szymon Urban, Mikołaj Błaziak, Maksym Jura, Gracjan Iwanek, Barbara Ponikowska, Jolanta Horudko, Agnieszka Siennicka, Petr Berka, Jan Biegus, Piotr Ponikowski, Robert Zymliński
Publié dans: Biomolecules, Numéro 12, 2022, Page(s) 1616, ISSN 2218-273X
Éditeur: MDPI
DOI: 10.3390/biom12111616

Patterns of locus of control in people suffering from heart failure: an approach by clustering method

Auteurs: Agnieszka Siennicka, Maciej Pondel, Szymon Urban, Ewa Anita Jankowska, Beata Ponikowska, Izabella Uchmanowicz
Publié dans: Medicina, 2022, ISSN 1010-660X
Éditeur: Lietuvos Gydytoju Sajunga
DOI: 10.3390/medicina58111542

Novel phenotyping for acute heart failure - unsupervised machine learning-based approach

Auteurs: Szymon Urban, Mikołaj Błaziak, Maksym Jura, Gracjan Iwanek, Agata Zdanowicz, Mateusz Guzik, Artur Borkowski, Piotr Gajewski, Jan Biegus, Agnieszka Siennicka, Maciej Pondel, Petr Berka, Piotr Ponikowski, Robert Zymliński
Publié dans: Biomedicines, 2022, ISSN 2227-9059
Éditeur: MDPI
DOI: 10.3390/biomedicines10071514

Answering clinical questions using machine learning: should we look at diastolic blood pressure when tailoring blood pressure control?Autorzy?

Auteurs: Maciej Siński, Petr Berka, Jacek Lewandowski, Piotr Sobieraj, Kacper Piechocki, Bartłomiej Paleczny, Agnieszka Siennicka
Publié dans: Journal of Clinical Medicine, 2022, ISSN 2077-0383
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/jcm11247454

The relationship between knowledge about the pandemic and willingness to get vaccinated against SARS-CoV-2 in medical students in Poland: a cross-sectional survey

Auteurs: Aleksandra Jastrzębska, Gabrielle Saden, Brygida Knysz, Maciej Pondel, Agnieszka Siennicka
Publié dans: Frontiers in Public Health,, 2022, ISSN 2296-2565
Éditeur: Frontiers
DOI: 10.3389/fpubh.2022.914462

A review of possible effects of cognitive biases on interpretation of rule-based machine learning models

Auteurs: Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz
Publié dans: Artificial Intelligence, Numéro 295, 2021, Page(s) 103458, ISSN 0004-3702
Éditeur: Elsevier BV
DOI: 10.1016/j.artint.2021.103458

Why was this cited? Explainable machine learning appliedto COVID‑19 research literature

Auteurs: Lucie Beranová, Marcin P. Joachimiak, Tomáš Kliegr, Gollam Rabby, Vilém Sklenák
Publié dans: Scientometrics, 2022, ISSN 0138-9130
Éditeur: Akademiai Kiado
DOI: 10.1007/s11192-022-04314-9