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

Informazioni relative al progetto

HEARTBIT_4.0

ID dell’accordo di sovvenzione: 857446

DOI

10.3030/857446

Progetto chiuso

Data della firma CE 11 Luglio 2019

Data di avvio 1 Gennaio 2020

Data di completamento 31 Dicembre 2022

Finanziato da

• Twinning of research institutions

Costo totale

€ 787 825,25

Contributo UE

€ 787 825,25

Coordinato da

UNIWERSYTET MEDYCZNY IM PIASTOW SLASKICH WE WROCLAWIU
Poland

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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

Pubblicazioni

Peer reviewed articles (9)

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

Autori: 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
Pubblicato in: Biomedicines, ISSN 2227-9059, 2022, ISSN 2227-9059
Editore: MDPI
DOI: 10.3390/biomedicines10092188

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

Autori: Mieszko Wieckiewicz, Dariusz Danel, Maciej Pondel, Joanna Smardz, Helena Martynowicz, Tomasz Wieczorek, Grzegorz Mazur, Robert Pudlo, Gniewko Wieckiewicz
Pubblicato in: Scientific Reports, Numero 11/1, 2021, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-021-90566-z

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

Autori: Szymon Urban, Mikołaj Błaziak, Maksym Jura, Gracjan Iwanek, Barbara Ponikowska, Jolanta Horudko, Agnieszka Siennicka, Petr Berka, Jan Biegus, Piotr Ponikowski, Robert Zymliński
Pubblicato in: Biomolecules, Numero 12, 2022, Pagina/e 1616, ISSN 2218-273X
Editore: MDPI
DOI: 10.3390/biom12111616

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

Autori: Agnieszka Siennicka, Maciej Pondel, Szymon Urban, Ewa Anita Jankowska, Beata Ponikowska, Izabella Uchmanowicz
Pubblicato in: Medicina, 2022, ISSN 1010-660X
Editore: Lietuvos Gydytoju Sajunga
DOI: 10.3390/medicina58111542

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

Autori: 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
Pubblicato in: Biomedicines, 2022, ISSN 2227-9059
Editore: MDPI
DOI: 10.3390/biomedicines10071514

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

Autori: Maciej Siński, Petr Berka, Jacek Lewandowski, Piotr Sobieraj, Kacper Piechocki, Bartłomiej Paleczny, Agnieszka Siennicka
Pubblicato in: Journal of Clinical Medicine, 2022, ISSN 2077-0383
Editore: 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

Autori: Aleksandra Jastrzębska, Gabrielle Saden, Brygida Knysz, Maciej Pondel, Agnieszka Siennicka
Pubblicato in: Frontiers in Public Health,, 2022, ISSN 2296-2565
Editore: Frontiers
DOI: 10.3389/fpubh.2022.914462

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

Autori: Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz
Pubblicato in: Artificial Intelligence, Numero 295, 2021, Pagina/e 103458, ISSN 0004-3702
Editore: Elsevier BV
DOI: 10.1016/j.artint.2021.103458

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

Autori: Lucie Beranová, Marcin P. Joachimiak, Tomáš Kliegr, Gollam Rabby, Vilém Sklenák
Pubblicato in: Scientometrics, 2022, ISSN 0138-9130
Editore: Akademiai Kiado
DOI: 10.1007/s11192-022-04314-9