Radosław Piotr Katarzyniak

Prof. Radosław Piotr Katarzyniak

Wroclaw University of Science and Technology, Wrocław, Dolnośląskie, Poland
orcid

Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2006
Verified
Book Chapter
Closed access
PDF file
KJKrzysztof Juszczyszyn
Krzysztof Juszczyszyn
NNNgoc Thanh Nguyen
Ngoc Thanh Nguyen
GKGrzegorz Kolaczek
Grzegorz Kolaczek
AGAdam Grzech
Adam Grzech
APAgnieszka Pieczynska
Agnieszka Pieczynska
RKRadosław Piotr Katarzyniak
Radosław Piotr Katarzyniak

1 January 2005
Verified
Book Chapter
Closed access
PDF file
GKGrzegorz Kolaczek
Grzegorz Kolaczek
APAgnieszka Pieczynska-Kuchtiak
Agnieszka Pieczynska-Kuchtiak
KJKrzysztof Juszczyszyn
Krzysztof Juszczyszyn
AGAdam Grzech
Adam Grzech
RKRadosław Piotr Katarzyniak
Radosław Piotr Katarzyniak
NNNgoc Thanh Nguyen
Ngoc Thanh Nguyen

9 September 2008 in Knowledge and Information Systems
Verified
Journal Article
Closed access
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NNNgoc Thanh Nguyen
Ngoc Thanh Nguyen
RKRadosław Piotr Katarzyniak
Radosław Piotr Katarzyniak

26 March 2024 in Advances in Clinical and Experimental Medicine
Verified
Journal Article
Open access
RSRemigiusz Szczepanowski
Remigiusz Szczepanowski
Izabella UchmanowiczIzabella Uchmanowicz
Izabella Uchmanowicz
APAleksandra Helena Pasieczna
Aleksandra Helena Pasieczna
JSJanusz Sobecki
Janusz Sobecki
RKRadosław Piotr Katarzyniak
Radosław Piotr Katarzyniak
AKAleksander Kahsin
Aleksander Kahsin
Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables.

1 January 2010 in Lecture Notes in Computer Science
Verified
Book Chapter
Closed access
PDF file
GSGrzegorz Skorupa
Grzegorz Skorupa
RKRadosław Piotr Katarzyniak
Radosław Piotr Katarzyniak

1 January 2010 in Lecture Notes in Computer Science
Verified
Book Chapter
Closed access
PDF file
GSGrzegorz Skorupa
Grzegorz Skorupa
RKRadosław Piotr Katarzyniak
Radosław Piotr Katarzyniak