Published on in Vol 19, No 4 (2017): April

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients

Authors of this article:

Eunjeong Park 1 Author Orcid Image ;   Hyuk-Jae Chang 2 Author Orcid Image ;   Hyo Suk Nam 3 Author Orcid Image

Journals

  1. Du Z, Yang Y, Zheng J, Li Q, Lin D, Li Y, Fan J, Cheng W, Chen X, Cai Y. Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation. JMIR Medical Informatics 2020;8(7):e17257 View
  2. Rahman Q, Janmohamed T, Pirbaglou M, Clarke H, Ritvo P, Heffernan J, Katz J. Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods. Journal of Medical Internet Research 2018;20(11):e12001 View
  3. Becker A. Artificial intelligence in medicine: What is it doing for us today?. Health Policy and Technology 2019;8(2):198 View
  4. Zhang Y, Zhou Y, Zhang D, Song W. A Stroke Risk Detection: Improving Hybrid Feature Selection Method. Journal of Medical Internet Research 2019;21(4):e12437 View
  5. Schlemm L. Disability Adjusted Life Years due to Ischaemic Stroke Preventable by Real-Time Stroke Detection—A Cost-Utility Analysis of Hypothetical Stroke Detection Devices. Frontiers in Neurology 2018;9 View
  6. Pradeepa S, Manjula K, Vimal S, Khan M, Chilamkurti N, Luhach A. DRFS: Detecting Risk Factor of Stroke Disease from Social Media Using Machine Learning Techniques. Neural Processing Letters 2020 View
  7. Álvarez-Machancoses Ó, DeAndrés Galiana E, Cernea A, Fernández Sánchez de la Viña J, Fernández-Martínez J. <p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>. Pharmacogenomics and Personalized Medicine 2020;Volume 13:105 View
  8. Park E, Lee K, Han T, Nam H. Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study. Journal of Medical Internet Research 2020;22(9):e20641 View
  9. Ma Y, Zhang P, Tang Y, Pan C, Li G, Liu N, Hu Y, Tang Z. Artificial intelligence: The dawn of a new era for cutting-edge technology based diagnosis and treatment for stroke. Brain Hemorrhages 2020;1(1):1 View
  10. Luvizutto G, Silva G, Nascimento M, Sousa Santos K, Appelt P, de Moura Neto E, de Souza J, Wincker F, Miranda L, Hamamoto Filho P, de Souza L, Simões R, de Oliveira Vidal E, Bazan R. Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept. Topics in Stroke Rehabilitation 2021:1 View

Books/Policy Documents

  1. Bayrak E, Kirci P. Early Detection of Neurological Disorders Using Machine Learning Systems. View
  2. Vashistha R, Yadav D, Chhabra D, Shukla P. Leveraging Biomedical and Healthcare Data. View