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

Non-randomized Protocols and Methods (ehealth) (412) Digital Pain Assessment and Management (100) Telehealth and Telemonitoring (1126) Machine Learning (953) Wearable Devices and Sensors (634) Postoperative Monitoring and Telemonitoring (72)

Published on in Vol 9 , No 7 (2020) :July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17783, first published .
Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement

Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement

Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement

Authors of this article:

Emad Kasaeyan Naeini 1 Author Orcid Image ;   Mingzhe Jiang 2 Author Orcid Image ;   Elise Syrjälä 2 Author Orcid Image ;   Michael-David Calderon 3 Author Orcid Image ;   Riitta Mieronkoski 4 Author Orcid Image ;   Kai Zheng 5 Author Orcid Image ;   Nikil Dutt 1 Author Orcid Image ;   Pasi Liljeberg 2 Author Orcid Image ;   Sanna Salanterä 4, 6 Author Orcid Image ;   Ariana M Nelson 7 Author Orcid Image ;   Amir M Rahmani 1, 8 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Aqajari S, Cao R, Kasaeyan Naeini E, Calderon M, Zheng K, Dutt N, Liljeberg P, Salanterä S, Nelson A, Rahmani A. Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study. JMIR mHealth and uHealth 2021;9(5):e25258 View
  2. Kasaeyan Naeini E, Subramanian A, Calderon M, Zheng K, Dutt N, Liljeberg P, Salantera S, Nelson A, Rahmani A. Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study. Journal of Medical Internet Research 2021;23(5):e25079 View