The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S)

Autor: Irwin Gratz, Martin Baruch, Magdy Takla, Julia Seaman, Isabel Allen, Brian McEniry, Edward Deal
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: BMC Anesthesiology, Vol 20, Iss 1, Pp 1-15 (2020)
Druh dokumentu: article
ISSN: 1471-2253
DOI: 10.1186/s12871-020-01015-9
Popis: Abstract Background Neural networks are increasingly used to assess physiological processes or pathologies, as well as to predict the increased likelihood of an impending medical crisis, such as hypotension. Method We compared the capabilities of a single hidden layer neural network of 12 nodes to those of a discrete-feature discrimination approach with the goal being to predict the likelihood of a given patient developing significant hypotension under spinal anesthesia when undergoing a Cesarean section (C/S). Physiological input information was derived from a non-invasive blood pressure device (Caretaker [CT]) that utilizes a finger cuff to measure blood pressure and other hemodynamic parameters via pulse contour analysis. Receiver-operator-curve/area-under-curve analyses were used to compare performance. Results The results presented here suggest that a neural network approach (Area Under Curve [AUC] = 0.89 [p
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje