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

Text-messaging (SMS, WeChat etc)-Based Interventions (663) New Methods (963) Machine Learning (1501) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1428) Cardiovascular Disease Prevention (142)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19200, first published .
Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text–Based Secondary Prevention Program: Feasibility Study

Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text–Based Secondary Prevention Program: Feasibility Study

Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text–Based Secondary Prevention Program: Feasibility Study

Authors of this article:

Nicole Lowres1, 2 Author Orcid Image ;   Andrew Duckworth3 Author Orcid Image ;   Julie Redfern2, 4 Author Orcid Image ;   Aravinda Thiagalingam2, 4, 5 Author Orcid Image ;   Clara K Chow2, 4, 5 Author Orcid Image
Article Authors Cited by (9) Tweetations (1) Metrics

Journals

  1. Aboyans V, Boukhris M. Secondary prevention after an acute cardiovascular event: far from targets and large room for improvement. European Journal of Preventive Cardiology 2022;29(2):360 View
  2. Brezulianu A, Burlacu A, Popa I, Arif M, Geman O. “Not by Our Feeling, But by Other's Seeing”: Sentiment Analysis Technique in Cardiology—An Exploratory Review. Frontiers in Public Health 2022;10 View
  3. Nguyen O, Turner K, Apathy N, Magoc T, Hanna K, Merlo L, Harle C, Thompson L, Berner E, Feldman S. Primary care physicians’ electronic health record proficiency and efficiency behaviors and time interacting with electronic health records: a quantile regression analysis. Journal of the American Medical Informatics Association 2022;29(3):461 View
  4. Klimis H, Nothman J, Lu D, Sun C, Cheung N, Redfern J, Thiagalingam A, Chow C. Text Message Analysis Using Machine Learning to Assess Predictors of Engagement With Mobile Health Chronic Disease Prevention Programs: Content Analysis. JMIR mHealth and uHealth 2021;9(11):e27779 View
  5. Guo M, Brar Prayaga R, Levitz C, Kuo E, Ruiz E, Torres-Ozadali E, Escaron A. Tailoring a Text Messaging and Fotonovela Program to Increase Patient Engagement in Colorectal Cancer Screening in a Large Urban Community Clinic Population: Quality Improvement Project. JMIR Cancer 2023;9:e43024 View
  6. Jiang Y, Xing X. Seismic communication data processing based on compressed sensing algorithm. Geophysical Prospecting 2024;72(5):1698 View
  7. Su J, Zhang Y, Ke Q, Su J, Yang Q. Mobilizing artificial intelligence to cardiac telerehabilitation. Reviews in Cardiovascular Medicine 2022;23(2) View
  8. Dobson R, Whittaker R, Abroms L, Bramley D, Free C, McRobbie H, Stowell M, Rodgers A. Don’t Forget the Humble Text Message: 25 Years of Text Messaging in Health. Journal of Medical Internet Research 2024;26:e59888 View

Books/Policy Documents

  1. Denecke K. Sentiment Analysis in the Medical Domain. View