@inproceedings{MTMT:34597082,
	title = {Prediction of Insulin Sensitivity Using Neural Network based Quantile Regression: In-Silico Validation},
	url = {https://m2.mtmt.hu/api/publication/34597082},
	author = {Alaa, N. Roel and Omer, Alkhafaf and Al-sultani Ameer Basheer Yousif, Ameer and Szlávecz, Ákos József and Paláncz, Béla and Báint, Szabó and Cong, Zhou and J., Geoffrey Chase and Benyó, Balázs István},
	booktitle = {Proceedings of the Workshop on the Advances of Information Technology 2024},
	unique-id = {34597082},
	year = {2024},
	pages = {186-192},
	orcid-numbers = {Benyó, Balázs István/0000-0003-2770-9127}
}

@inproceedings{MTMT:34597041,
	title = {Quantile Regression Based Prediction of Insulin Sensitivity Using Neural Network Model for Better Glycaemic Control in Intensive Care},
	url = {https://m2.mtmt.hu/api/publication/34597041},
	author = {Omer, Alkhafaf and Al-sultani Ameer Basheer Yousif, Ameer and Szlávecz, Ákos József and Alaa, N. Roel and Paláncz, Béla and Báint, Szabó and Cong, Zhou and J., Geoffrey Chase and Benyó, Balázs István},
	booktitle = {Proceedings of the Workshop on the Advances of Information Technology 2024},
	unique-id = {34597041},
	year = {2024},
	pages = {176-185},
	orcid-numbers = {Benyó, Balázs István/0000-0003-2770-9127}
}

@article{MTMT:34410684,
	title = {Comparison of Three Artificial Intelligence Methods for Predicting 90% Quantile Interval of Future Insulin Sensitivity of Intensive Care Patients},
	url = {https://m2.mtmt.hu/api/publication/34410684},
	author = {Szabó, Bálint and Szlávecz, Ákos József and Paláncz, Béla and Kovács, Katalin and Chase, J. Geoffrey and Benyó, Balázs István},
	doi = {10.1016/j.ifacol.2023.10.1110},
	journal-iso = {IFACOL},
	journal = {IFAC PAPERSONLINE},
	volume = {56},
	unique-id = {34410684},
	issn = {2405-8971},
	abstract = {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/)},
	keywords = {Artificial intelligence; INSULIN SENSITIVITY; artificial neural network; Prediction method; Tight Glycaemic Control},
	year = {2023},
	eissn = {2405-8963},
	pages = {2091-2095},
	orcid-numbers = {Kovács, Katalin/0000-0002-7605-4537; Benyó, Balázs István/0000-0003-2770-9127}
}

@article{MTMT:34012024,
	title = {Classification-based Deep Neural Network vs Mixture Density Network Models for Insulin Sensitivity Prediction Problem},
	url = {https://m2.mtmt.hu/api/publication/34012024},
	author = {Benyó, Balázs István and Paláncz, Béla and Szlávecz, Ákos József and Szabó, Bálint and Kovács, Katalin and Chase, J. Geoffrey},
	doi = {10.1016/j.cmpb.2023.107633},
	journal-iso = {COMPUT METH PROG BIO},
	journal = {COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE},
	volume = {240},
	unique-id = {34012024},
	issn = {0169-2607},
	abstract = {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/ )},
	year = {2023},
	eissn = {1872-7565},
	orcid-numbers = {Benyó, Balázs István/0000-0003-2770-9127; Kovács, Katalin/0000-0002-7605-4537; Chase, J. Geoffrey/0000-0001-9989-4849}
}

@book{MTMT:33770005,
	title = {Mathematical Geosciences Second Edition},
	url = {https://m2.mtmt.hu/api/publication/33770005},
	isbn = {9783030924942},
	author = {Awange, Joseph L. and Paláncz, Béla and Lewis, Robert H. and Völgyesi, Lajos},
	doi = {10.1007/978-3-030-92495-9},
	publisher = {Springer International Publishing},
	unique-id = {33770005},
	year = {2023},
	orcid-numbers = {Völgyesi, Lajos/0000-0002-3196-4887}
}

@inproceedings{MTMT:33704875,
	title = {Comparison of Neural Network Models for Quantile Regression Based Insulin Sensitivity Prediction},
	url = {https://m2.mtmt.hu/api/publication/33704875},
	author = {Szabó, Bálint and Omer, Alkhafaf and Al-sultani Ameer Basheer Yousif, Ameer and P., Pintér and Szlávecz, Ákos József and Paláncz, Béla and J., Geoffrey Chase and Benyó, Balázs István},
	booktitle = {Proceedings of the Workshop on the Advances of Information Technology 2023},
	unique-id = {33704875},
	year = {2023},
	pages = {1-6},
	orcid-numbers = {Benyó, Balázs István/0000-0003-2770-9127}
}

@article{MTMT:34190536,
	title = {HUNGARIAN CONTRIBUTION TO THE RESEARCH ON POSITIONING AND APPLICATIONS (2019-2022) – IAG COMMISSION 4},
	url = {https://m2.mtmt.hu/api/publication/34190536},
	author = {Rózsa, Szabolcs and Ács, Ágnes Mária and Ambrus, Bence and Bányai, László and Békési, Eszter and Bozsó, István and Égető, Csaba and Farkas, Márton and Gönczy, Sándor and Horváth, Roland and Juni, Ildikó and Khaldi, Abir and Lupsic, Balázs and Magyar, Bálint and Nagy, Lajos and Paláncz, Béla and Siki, Zoltán and Somogyi, Rita and Szakács, Alexandru and Szárnya, Csilla and Szűcs, Eszter and Takács, Bence and Tóth, Sándor and Turák, Bence Dávid and Vanek, Bálint and Völgyesi, Lajos and Wesztergom, Viktor},
	journal-iso = {GEOMAT KÖZL},
	journal = {GEOMATIKAI KÖZLEMÉNYEK / PUBLICATIONS IN GEOMATICS},
	volume = {25},
	unique-id = {34190536},
	issn = {1419-6492},
	year = {2022},
	pages = {41-50},
	orcid-numbers = {Rózsa, Szabolcs/0000-0001-5335-6455; Ambrus, Bence/0000-0002-8896-9443; Békési, Eszter/0000-0003-3561-1656; Égető, Csaba/0000-0001-7722-852X; Gönczy, Sándor/0000-0003-0350-0484; Juni, Ildikó/0000-0002-3603-3387; Magyar, Bálint/0000-0002-2464-6805; Siki, Zoltán/0000-0002-9615-181X; Szárnya, Csilla/0000-0002-8880-194X; Szűcs, Eszter/0000-0001-6781-4269; Takács, Bence/0000-0003-4262-7461; Tóth, Sándor/0000-0001-8445-9027; Vanek, Bálint/0000-0002-2458-2725; Völgyesi, Lajos/0000-0002-3196-4887}
}

@article{MTMT:34068228,
	title = {Hungarian contribution to the research on numerical theories and solutions in mathematical geodesy (2019-2022) - IAG Inter-commission Committee},
	url = {https://m2.mtmt.hu/api/publication/34068228},
	author = {Jancsó, Tamás and Molnár, Gábor and Völgyesi, Lajos and Paláncz, Béla and Varga, Péter and Gribovszki, Katalin Eszter and Benedek, Judit and Kalmár, János and Fodor, Csilla},
	journal-iso = {GEOMAT KÖZL},
	journal = {GEOMATIKAI KÖZLEMÉNYEK / PUBLICATIONS IN GEOMATICS},
	volume = {25},
	unique-id = {34068228},
	issn = {1419-6492},
	year = {2022},
	pages = {11},
	orcid-numbers = {Jancsó, Tamás/0000-0003-4954-7202; Molnár, Gábor/0000-0001-9309-3418; Völgyesi, Lajos/0000-0002-3196-4887; Gribovszki, Katalin Eszter/0000-0003-2577-0127; Fodor, Csilla/0000-0001-9134-4017}
}

@inproceedings{MTMT:33700175,
	title = {Insulin sensitivity prediction using quantile regression},
	url = {https://m2.mtmt.hu/api/publication/33700175},
	author = {Szabó, Bálint and Omer, Alkhafaf and Al-sultani Ameer Basheer Yousif, Ameer and Szlávecz, Ákos József and Paláncz, Béla and Geoffrey, Chase and Benyó, Balázs István},
	booktitle = {Az egészségügyi informatika COVID előtt és COVID után - A XXXV. Neumann Kollokvium konferencia kiadványa},
	unique-id = {33700175},
	year = {2022},
	pages = {167-172},
	orcid-numbers = {Benyó, Balázs István/0000-0003-2770-9127}
}

@inproceedings{MTMT:33538509,
	title = {Patient group specific insulin sensitivity prediction using machine learning},
	url = {https://m2.mtmt.hu/api/publication/33538509},
	author = {Szabó, Bálint and Szlávecz, Ákos József and Paláncz, Béla and Geoffrey, Chase and Benyó, Balázs István},
	booktitle = {Proceedings of the Workshop on the Advances in Information Technology 2022},
	unique-id = {33538509},
	year = {2022},
	pages = {29-33},
	orcid-numbers = {Benyó, Balázs István/0000-0003-2770-9127}
}