This paper is in the following e-collection/theme issue:

Reviews (133) Clinical Informatics (1325) Clinical Information and Decision Making (1914) Digital Health, Telehealth and e-Innovation in Clinical Settings (556) Machine Learning (2074) Computer-Aided Diagnosis (251) Computational modeling of biological processes (13)

Published on in Vol 13 (2024)

This is a member publication of CSIRO, Australian e-Health Research Centre, Sydney, Australia

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46946, first published .
Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

Authors of this article:

Jessica Rahman1 Author Orcid Image ;   Aida Brankovic2 Author Orcid Image ;   Mark Tracy3 Author Orcid Image ;   Sankalp Khanna2 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Barrett R, Lawler B, Liu S, Park W, Davoodi M, Martin B, Kalyanam S, Makker K, Kuiper J, Aziz K. Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations. Seminars in Perinatology 2025;49(7):152144 View
  2. Hakeem D, Iqbal J, Saad M, Tahir T, Jamil A, Kainat khan . Early Detection of Sepsis and NEC Using Serial Vital Sign Trends (HR, SpO₂, RR) on Standard NICU Monitors in Preterm Neonates. Pakistan Journal of Health Sciences 2025:140 View