TY - GEN
T1 - Optimizing Neonatal Respiratory Support Through Network Modeling
T2 - 12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023
AU - Sebahi, Yassine
AU - Jabeen, Fakhra
AU - Treur, Jan
AU - Taal, H. Rob
AU - Roelofsma, Peter H.M.P.
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2024
Y1 - 2024
N2 - This paper presents an approach to enhancing neonatal care through the application of artificial intelligence (AI). Utilizing network-oriented modeling methodologies, the study aims to develop a network model to improve outcomes in neonatal respiratory support. The introduction sets the stage by outlining the significance of neonatal respiratory support and the challenges faced in this domain. The literature review delves into the existing body of work, highlighting the gaps and the need for a network modeling approach. The network-oriented modeling approach provides a robust framework that captures various states, such as world states, doctors’ mental states, and AI coach states, facilitating a comprehensive understanding of the complex interactions in neonatal respiratory support. Through Matlab simulations, the study investigates multiple scenarios, from optimal conditions to deviations from standard protocol. The main contribution focuses on the introduction of an AI coach, which serves as a real-time intervention mechanism to fill in the doctor's knowledge gaps. The research serves as a seminal work in the intersection of artificial intelligence and healthcare, demonstrating the potential of network-oriented modeling in improving patient outcomes and streamlining healthcare protocols.
AB - This paper presents an approach to enhancing neonatal care through the application of artificial intelligence (AI). Utilizing network-oriented modeling methodologies, the study aims to develop a network model to improve outcomes in neonatal respiratory support. The introduction sets the stage by outlining the significance of neonatal respiratory support and the challenges faced in this domain. The literature review delves into the existing body of work, highlighting the gaps and the need for a network modeling approach. The network-oriented modeling approach provides a robust framework that captures various states, such as world states, doctors’ mental states, and AI coach states, facilitating a comprehensive understanding of the complex interactions in neonatal respiratory support. Through Matlab simulations, the study investigates multiple scenarios, from optimal conditions to deviations from standard protocol. The main contribution focuses on the introduction of an AI coach, which serves as a real-time intervention mechanism to fill in the doctor's knowledge gaps. The research serves as a seminal work in the intersection of artificial intelligence and healthcare, demonstrating the potential of network-oriented modeling in improving patient outcomes and streamlining healthcare protocols.
KW - Adaptive network model
KW - AI Coach
KW - Infant Care
UR - http://www.scopus.com/inward/record.url?scp=85186709460&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-53472-0_21
DO - 10.1007/978-3-031-53472-0_21
M3 - Conference contribution
AN - SCOPUS:85186709460
SN - 978-3-031-53471-3
T3 - Studies in Computational Intelligence
SP - 245
EP - 257
BT - Complex Networks and Their Applications XII - Proceedings of The Twelfth International Conference on Complex Networks and their Applications
A2 - Cherifi, Hocine
A2 - Rocha, Luis M.
A2 - Cherifi, Chantal
A2 - Donduran, Murat
PB - Springer
Y2 - 28 November 2023 through 30 November 2023
ER -