TY  - CHAP
AU  - Alaa, N. Roel
AU  - Omer, Alkhafaf
AU  - Al-sultani Ameer Basheer Yousif, Ameer
AU  - Szlávecz, Ákos József
AU  - Paláncz, Béla
AU  - Báint, Szabó
AU  - Cong, Zhou
AU  - J., Geoffrey Chase
AU  - Benyó, Balázs István
TI  - Prediction of Insulin Sensitivity Using Neural Network based Quantile Regression: In-Silico Validation
T2  - Proceedings of the Workshop on the Advances of Information Technology 2024
PB  - BME Irányítástechnika és Informatika Tanszék
CY  - Budapest
SN  - 9789634219422
PY  - 2024
SP  - 186
EP  - 192
PG  - 7
UR  - https://m2.mtmt.hu/api/publication/34597082
ID  - 34597082
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Omer, Alkhafaf
AU  - Al-sultani Ameer Basheer Yousif, Ameer
AU  - Szlávecz, Ákos József
AU  - Alaa, N. Roel
AU  - Paláncz, Béla
AU  - Báint, Szabó
AU  - Cong, Zhou
AU  - J., Geoffrey Chase
AU  - Benyó, Balázs István
TI  - Quantile Regression Based Prediction of Insulin Sensitivity Using Neural Network Model for Better Glycaemic Control in Intensive Care
T2  - Proceedings of the Workshop on the Advances of Information Technology 2024
PB  - BME Irányítástechnika és Informatika Tanszék
CY  - Budapest
SN  - 9789634219422
PY  - 2024
SP  - 176
EP  - 185
PG  - 10
UR  - https://m2.mtmt.hu/api/publication/34597041
ID  - 34597041
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Szabó, Bálint
AU  - Szlávecz, Ákos József
AU  - Paláncz, Béla
AU  - Kovács, Katalin
AU  - Chase, J. Geoffrey
AU  - Benyó, Balázs István
TI  - Comparison of Three Artificial Intelligence Methods for Predicting 90% Quantile Interval of Future Insulin Sensitivity of Intensive Care Patients
JF  - IFAC PAPERSONLINE
J2  - IFACOL
VL  - 56
PY  - 2023
IS  - 2
SP  - 2091
EP  - 2095
PG  - 5
SN  - 2405-8971
DO  - 10.1016/j.ifacol.2023.10.1110
UR  - https://m2.mtmt.hu/api/publication/34410684
ID  - 34410684
AB  - Three alternative artificial intelligence-based insulin sensitivity prediction methods are compared in this study. Insulin sensitivity prediction is an essential step in calculating the optimal treatment options in model-based glycemic control protocol of insulin-dependent intensive care patients. The prediction methods must predict not only the expected value of the insulin sensitivity for a given time horizon but also the 90% confidence interval making the prediction problem more specific compared to the common prediction problems. All of the proposed prediction methods - proposed in our previous publications - use different neural network models: a classification deep neural network model, a Mixture Density Network based model, and a Quantile regression based model. The patent data set used for the development and accuracy assessment is from 3 clinical ICU cohorts, including 820 treatment episodes of 606 patients and 68,631 hours of treatment. To evaluate the efficacy of the prediction in the context of clinical requirements, three metrics are used Success rate, Interval ratio, and I-Score are applied. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Benyó, Balázs István
AU  - Paláncz, Béla
AU  - Szlávecz, Ákos József
AU  - Szabó, Bálint
AU  - Kovács, Katalin
AU  - Chase, J. Geoffrey
TI  - Classification-based Deep Neural Network vs Mixture Density Network Models for Insulin Sensitivity Prediction Problem
JF  - COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
J2  - COMPUT METH PROG BIO
VL  - 240
PY  - 2023
PG  - 9
SN  - 0169-2607
DO  - 10.1016/j.cmpb.2023.107633
UR  - https://m2.mtmt.hu/api/publication/34012024
ID  - 34012024
N1  - Funding Agency and Grant Number: National Research, Development, and Innovation Office, Hungary [K116574, K137995];  [872488]
            Funding text: The research was supported by the National Research, Development, and Innovation Office, Hungary , (Grant No. K116574 , K137995 , TKP2021-EGA-02, and by H2020 MSCA-RISE DCPM (GA ID: 872488) ) grant.
AB  - Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classi-fication deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model-based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials. & COPY; 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
LA  - English
DB  - MTMT
ER  - 

TY  - BOOK
AU  - Awange, Joseph L.
AU  - Paláncz, Béla
AU  - Lewis, Robert H.
AU  - Völgyesi, Lajos
TI  - Mathematical Geosciences Second Edition
PB  - Springer International Publishing
CY  - Cham
PY  - 2023
SP  - 715
SN  - 9783030924959
DO  - 10.1007/978-3-030-92495-9
UR  - https://m2.mtmt.hu/api/publication/33770005
ID  - 33770005
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szabó, Bálint
AU  - Omer, Alkhafaf
AU  - Al-sultani Ameer Basheer Yousif, Ameer
AU  - P., Pintér
AU  - Szlávecz, Ákos József
AU  - Paláncz, Béla
AU  - J., Geoffrey Chase
AU  - Benyó, Balázs István
TI  - Comparison of Neural Network Models for Quantile Regression Based Insulin Sensitivity Prediction
T2  - Proceedings of the Workshop on the Advances of Information Technology 2023
PB  - BME Irányítástechnika és Informatika Tanszék
CY  - Budapest
SN  - 9789634218968
PY  - 2023
SP  - 1
EP  - 6
PG  - 6
UR  - https://m2.mtmt.hu/api/publication/33704875
ID  - 33704875
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Rózsa, Szabolcs
AU  - Ács, Ágnes Mária
AU  - Ambrus, Bence
AU  - Bányai, László
AU  - Békési, Eszter
AU  - Bozsó, István
AU  - Égető, Csaba
AU  - Farkas, Márton
AU  - Gönczy, Sándor
AU  - Horváth, Roland
AU  - Juni, Ildikó
AU  - Khaldi, Abir
AU  - Lupsic, Balázs
AU  - Magyar, Bálint
AU  - Nagy, Lajos
AU  - Paláncz, Béla
AU  - Siki, Zoltán
AU  - Somogyi, Rita
AU  - Szakács, Alexandru
AU  - Szárnya, Csilla
AU  - Szűcs, Eszter
AU  - Takács, Bence
AU  - Tóth, Sándor
AU  - Turák, Bence Dávid
AU  - Vanek, Bálint
AU  - Völgyesi, Lajos
AU  - Wesztergom, Viktor
TI  - HUNGARIAN CONTRIBUTION TO THE RESEARCH ON POSITIONING AND APPLICATIONS (2019-2022) – IAG COMMISSION 4
JF  - GEOMATIKAI KÖZLEMÉNYEK / PUBLICATIONS IN GEOMATICS
J2  - GEOMAT KÖZL
VL  - 25
PY  - 2022
IS  - 1
SP  - 41
EP  - 50
PG  - 10
SN  - 1419-6492
UR  - https://m2.mtmt.hu/api/publication/34190536
ID  - 34190536
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Jancsó, Tamás
AU  - Molnár, Gábor
AU  - Völgyesi, Lajos
AU  - Paláncz, Béla
AU  - Varga, Péter
AU  - Gribovszki, Katalin Eszter
AU  - Benedek, Judit
AU  - Kalmár, János
AU  - Fodor, Csilla
TI  - Hungarian contribution to the research on numerical theories and solutions in mathematical geodesy (2019-2022) - IAG Inter-commission Committee
JF  - GEOMATIKAI KÖZLEMÉNYEK / PUBLICATIONS IN GEOMATICS
J2  - GEOMAT KÖZL
VL  - 25
PY  - 2022
SP  - 11
SN  - 1419-6492
UR  - https://m2.mtmt.hu/api/publication/34068228
ID  - 34068228
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szabó, Bálint
AU  - Omer, Alkhafaf
AU  - Al-sultani Ameer Basheer Yousif, Ameer
AU  - Szlávecz, Ákos József
AU  - Paláncz, Béla
AU  - Geoffrey, Chase
AU  - Benyó, Balázs István
ED  - Bari, Ferenc
ED  - Rárosi, Ferenc
ED  - Szűcs, Mónika
TI  - Insulin sensitivity prediction using quantile regression
T2  - Az egészségügyi informatika COVID előtt és COVID után - A XXXV. Neumann Kollokvium konferencia kiadványa
PB  - Neumann János Számítógép-tudományi Társaság
CY  - Szeged
SN  - 9786155036224
PY  - 2022
SP  - 167
EP  - 172
PG  - 6
UR  - https://m2.mtmt.hu/api/publication/33700175
ID  - 33700175
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szabó, Bálint
AU  - Szlávecz, Ákos József
AU  - Paláncz, Béla
AU  - Geoffrey, Chase
AU  - Benyó, Balázs István
ED  - Kiss, Bálint
ED  - Szirmay-Kalos, László
TI  - Patient group specific insulin sensitivity prediction using machine learning
T2  - Proceedings of the Workshop on the Advances in Information Technology 2022
PB  - OSZK
CY  - Budapest
SN  - 9789634218715
PY  - 2022
SP  - 29
EP  - 33
PG  - 5
UR  - https://m2.mtmt.hu/api/publication/33538509
ID  - 33538509
LA  - English
DB  - MTMT
ER  -