TY - GEN
T1 - Artificial intelligence based early diagnosis of sepsis
AU - Kareem, Amer
AU - Bin Sulaiman, Rejwan
AU - Akram, Shaik Vaseem
AU - Maurya, M.
AU - Chakrapani, I.S.
AU - Sasikala, P.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/7/23
Y1 - 2023/7/23
N2 - Sepsis is a major killer of those who are already in a serious condition. The morbidity and death rates in this field remain high, despite the fact that medical technology has been advancing steadily over the last several years. This is due mostly to people not beginning therapy quickly enough and doctors not following best practices. Medical decision support solutions have advanced greatly with the help of artificial intelligence (AI), a rapidly developing sector in the medical industry. Great promise has been shown in its ability to anticipate patients' clinical conditions and aid clinical decision-making. Early prediction, prognosis evaluation, mortality prediction, and optimum treatment are just few of the areas where algorithms developed using artificial intelligence may be put to use. This article summarizes the most recent research on AI based clinical decision support in sepsis as well as explains how this cutting-edge technology might aid in sepsis prediction, identification, sub phenotyping, prognostic evaluation, and clinical treatment. We also spoke about the difficulties of using this non-conventional approach in clinical practice.
AB - Sepsis is a major killer of those who are already in a serious condition. The morbidity and death rates in this field remain high, despite the fact that medical technology has been advancing steadily over the last several years. This is due mostly to people not beginning therapy quickly enough and doctors not following best practices. Medical decision support solutions have advanced greatly with the help of artificial intelligence (AI), a rapidly developing sector in the medical industry. Great promise has been shown in its ability to anticipate patients' clinical conditions and aid clinical decision-making. Early prediction, prognosis evaluation, mortality prediction, and optimum treatment are just few of the areas where algorithms developed using artificial intelligence may be put to use. This article summarizes the most recent research on AI based clinical decision support in sepsis as well as explains how this cutting-edge technology might aid in sepsis prediction, identification, sub phenotyping, prognostic evaluation, and clinical treatment. We also spoke about the difficulties of using this non-conventional approach in clinical practice.
KW - artificial intelligence
KW - Disease Prediction using AI.
KW - Sepsis
KW - machine learning
KW - Artificial Intelligence
KW - Machine Learning
UR - https://www.scopus.com/pages/publications/85178312839
U2 - 10.1109/icacite57410.2023.10182599
DO - 10.1109/icacite57410.2023.10182599
M3 - Conference contribution
SN - 9798350399264
T3 - 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023
SP - 1557
EP - 1562
BT - 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)
Y2 - 12 May 2023 through 13 May 2023
ER -