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

Clinical Information and Decision Making (1328) Decision Support for Health Professionals (1137) Machine Learning (1385)

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 Park1 Author Orcid Image ;   Hyuk-Jae Chang2 Author Orcid Image ;   Hyo Suk Nam3 Author Orcid Image
Article Authors Cited by (23) Tweetations (27) Metrics

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 2023;55(4):3843 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 2022;29(5):331 View
  11. Park E, Kim J, Nam H, Chang H. Requirement Analysis and Implementation of Smart Emergency Medical Services. IEEE Access 2018;6:42022 View
  12. Heo J, Yoo J, Lee H, Lee I, Kim J, Park E, Kim Y, Nam H. Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke. Neurology 2022;99(1) View
  13. Suri J, Maindarkar M, Paul S, Ahluwalia P, Bhagawati M, Saba L, Faa G, Saxena S, Singh I, Chadha P, Turk M, Johri A, Khanna N, Viskovic K, Mavrogeni S, Laird J, Miner M, Sobel D, Balestrieri A, Sfikakis P, Tsoulfas G, Protogerou A, Misra D, Agarwal V, Kitas G, Kolluri R, Teji J, Al-Maini M, Dhanjil S, Sockalingam M, Saxena A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan P, Omerzu T, Naidu S, Nicolaides A, Paraskevas K, Kalra M, Ruzsa Z, Fouda M. Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review. Diagnostics 2022;12(7):1543 View
  14. Zheng Y, Guo Z, Zhang Y, Shang J, Yu L, Fu P, Liu Y, Li X, Wang H, Ren L, Zhang W, Hou H, Tan X, Wang W. Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine. EPMA Journal 2022;13(2):285 View
  15. Shi S, Qie S, Wang H, Wang J, Liu T. Recombination of the right cerebral cortex in patients with left side USN after stroke: fNIRS evidence from resting state. Frontiers in Neurology 2023;14 View
  16. Bathla P, Kumar R. A hybrid system to predict brain stroke using a combined feature selection and classifier. Intelligent Medicine 2024;4(2):75 View
  17. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01 View
  18. Micali G, Corallo F, Pagano M, Giambò F, Duca A, D’Aleo P, Anselmo A, Bramanti A, Garofano M, Mazzon E, Bramanti P, Cappadona I. Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice. Healthcare 2024;12(14):1380 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
  3. Bayrak E, Kirci P. Research Anthology on Big Data Analytics, Architectures, and Applications. View