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

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
  3. Somani S, Yu K, Chiu A, Sykes K, Villwock J. Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review. Otolaryngology–Head and Neck Surgery 2022;167(4):620 View
  4. Cascella M, Schiavo D, Cuomo A, Ottaiano A, Perri F, Patrone R, Migliarelli S, Bignami E, Vittori A, Cutugno F, Hu L. Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives. Pain Research and Management 2023;2023:1 View

Books/Policy Documents

  1. Kanduri A, Shahhosseini S, Naeini E, Alikhani H, Liljeberg P, Dutt N, Rahmani A. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. View