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Improving pain management in patients with sickle cell disease from physiological measures using machine learning techniques

  • Published:2018-06 Issue: Volume:7-8 Page:48-59
  • ISSN:2352-6483
  • Container-title:Smart Health
  • language:en
  • Short-container-title:Smart Health

Author:

Yang Fan,Banerjee Tanvi,Narine Kalindi,Shah Nirmish

Funder

NIH

Publisher

Elsevier BV

Subject

Health Information Management,Computer Science Applications,Health Informatics,Information Systems,Medicine (miscellaneous)

Reference47 articles.

1. Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method;Acharya;Knowledge-based Systems,2015

2. Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes;Austin;Journal of Clinical Epidemiology,2013

3. Pain measurement in hospitalized adults with sickle cell painful episodes;Ballas;Annals of Clinical Laboratory Science,1993

4. Pain management of sickle cell disease;Ballas;Hematology/oncology Clinics of North America,2005

5. Missing data approaches in eHealth research: Simulation study and a tutorial for nonmathematically inclined researchers;Blankers;Journal of Medical Internet Research,2010

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Statistical Analysis of Hematological Parameters for Prediction of Sickle Cell Disease;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

2. Classification of chronic pain using fMRI data: Unveiling brain activity patterns for diagnosis;Turkish Journal of Electrical Engineering and Computer Sciences;2023-10-07

3. Several models combined with ultrasound techniques to predict breast muscle weight in broilers;Poultry Science;2023-10

4. Artificial intelligence in sickle disease;Blood Reviews;2023-09

5. Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study;2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE);2023-08-25
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