TY - GEN
T1 - Adaptive Learning and AI to Support Medication Management
AU - Oliveira, João
AU - Vafaei, Nazanin
AU - Delgado-Gomes, Vasco
AU - Figueiras, Paulo
AU - Agostinho, Carlos
AU - Jardim-Gonçalves, Ricardo
N1 - Funding Information:
ACKNOWLEDGMENT The research work leading to these results was funded by the Portuguese FCT program, Center of Technology and Systems (CTS) UIDB/00066/2020 / UIDP/00066/2020; and from the European Union’s Horizon 2020 research and innovation program under grant agreement No 826117
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Artificial Intelligence (AI) has proven to be very helpful in different areas, and its application in the medical field can help clinicians to make more informed decisions, especially in the Intensive Care Unit (ICU), where the COVID-19 pandemic exposed several challenges. If a model is correctly selected, AI can help improve healthcare systems by predicting future occurrences. This study analyzes Machine Learning (ML) techniques, discusses their application in an ICU environment and proposes a methodology to develop ML models to predict mean arterial pressure (MAP) values that can assist healthcare professionals' decision making. The current study starts by doing a brief review of ML methods and identifying interesting models to train. The chosen models are then trained and the one considered to be the most appropriate is chosen to be tested in a controlled environment, in a system that can generate medical data. Data is then cleaned, classified, and fed to the model, to give a prediction related to future MAP values. Finally, the developed system is integrated into the ICU4Covid project, more precisely in the B-Health IoT Box, which allows for deployments of new applications for ICUs. The proposed system's integration into ICU4Covid enables remote medical operations and can improve the quality care of healthcare services in ICU environments.
AB - Artificial Intelligence (AI) has proven to be very helpful in different areas, and its application in the medical field can help clinicians to make more informed decisions, especially in the Intensive Care Unit (ICU), where the COVID-19 pandemic exposed several challenges. If a model is correctly selected, AI can help improve healthcare systems by predicting future occurrences. This study analyzes Machine Learning (ML) techniques, discusses their application in an ICU environment and proposes a methodology to develop ML models to predict mean arterial pressure (MAP) values that can assist healthcare professionals' decision making. The current study starts by doing a brief review of ML methods and identifying interesting models to train. The chosen models are then trained and the one considered to be the most appropriate is chosen to be tested in a controlled environment, in a system that can generate medical data. Data is then cleaned, classified, and fed to the model, to give a prediction related to future MAP values. Finally, the developed system is integrated into the ICU4Covid project, more precisely in the B-Health IoT Box, which allows for deployments of new applications for ICUs. The proposed system's integration into ICU4Covid enables remote medical operations and can improve the quality care of healthcare services in ICU environments.
KW - Blood Pressure
KW - Hypotension
KW - Intensive Care Unit
KW - Machine Learning
KW - Telemedicine
UR - http://www.scopus.com/inward/record.url?scp=85181137077&partnerID=8YFLogxK
U2 - 10.1109/ICE/ITMC58018.2023.10332377
DO - 10.1109/ICE/ITMC58018.2023.10332377
M3 - Conference contribution
AN - SCOPUS:85181137077
SN - 979-8-3503-1518-9
T3 - IEEE International Conference on Engineering, Technology and Innovation
BT - Proceedings of the 29th International Conference on Engineering, Technology, and Innovation
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 29th International Conference on Engineering, Technology, and Innovation, ICE 2023
Y2 - 19 June 2023 through 22 June 2023
ER -
