Reducing the question burden of patient reported outcome measures using Bayesian networks-Reference-Cited by-同舟云学术

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Reducing the question burden of patient reported outcome measures using Bayesian networks

Published:2022-11 Issue: Volume:135 Page:104230
ISSN:1532-0464
Container-title:Journal of Biomedical Informatics
language:en
Short-container-title:Journal of Biomedical Informatics

Author:

Yücetürk Hakan,Gülle Halime,Şakar Ceren Tuncer,Joyner Christopher^ORCID,Marsh William,Ünal Edibe,Morrissey Dylan,Yet Barbaros^ORCID

Funder

British Council

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Newton Fund

Department for Business, Energy and Industrial Strategy, UK Government

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference62 articles.

1. Application of machine learning methods on patient reported outcome measurements for predicting outcomes: a literature review;Verma;Informatics,2021

2. Patient reported outcome measures could help transform healthcare;Black;BMJ,2013

3. Dynamic assessment of health outcomes: time to let the CAT out of the bag?;Cook;Health Serv. Res.,2005

4. Item response theory and computerized adaptive testing for orthopaedic outcomes measures;Brodke;J. Am. Acad. Orthop. Surg.,2016

5. The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008;Cella;J. Clin. Epidemiol.,2010

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