BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

149 related articles for article (PubMed ID: 35350014)

  • 1. Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke.
    Kim JK; Lv Z; Park D; Chang MC
    Eur Neurol; 2022; 85(4):273-279. PubMed ID: 35350014
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of Motor Function in Stroke Patients Using Machine Learning Algorithm: Development of Practical Models.
    Kim JK; Choo YJ; Chang MC
    J Stroke Cerebrovasc Dis; 2021 Aug; 30(8):105856. PubMed ID: 34022582
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of Motor Outcome of Stroke Patients Using a Deep Learning Algorithm with Brain MRI as Input Data.
    Shin H; Kim JK; Choo YJ; Choi GS; Chang MC
    Eur Neurol; 2022; 85(6):460-466. PubMed ID: 35738236
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke.
    Choo YJ; Kim JK; Kim JH; Chang MC; Park D
    Sci Rep; 2021 Apr; 11(1):8499. PubMed ID: 33875716
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches.
    Thakkar HK; Liao WW; Wu CY; Hsieh YW; Lee TH
    J Neuroeng Rehabil; 2020 Sep; 17(1):131. PubMed ID: 32993692
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.
    Tozlu C; Edwards D; Boes A; Labar D; Tsagaris KZ; Silverstein J; Pepper Lane H; Sabuncu MR; Liu C; Kuceyeski A
    Neurorehabil Neural Repair; 2020 May; 34(5):428-439. PubMed ID: 32193984
    [No Abstract]   [Full Text] [Related]  

  • 7. The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke.
    Hossain D; Scott SH; Cluff T; Dukelow SP
    J Neuroeng Rehabil; 2023 Jan; 20(1):15. PubMed ID: 36707846
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study.
    Chen YW; Lin KC; Li YC; Lin CJ
    J Neuroeng Rehabil; 2023 Feb; 20(1):25. PubMed ID: 36823626
    [TBL] [Abstract][Full Text] [Related]  

  • 9. [Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].
    Wang Y; Yu L; Fu J; Fang Q
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2014 Apr; 31(2):251-6. PubMed ID: 25039122
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Use of Machine Learning in Stroke Rehabilitation: A Narrative Review.
    Choo YJ; Chang MC
    Brain Neurorehabil; 2022 Nov; 15(3):e26. PubMed ID: 36742082
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke.
    Liao WW; Hsieh YW; Lee TH; Chen CL; Wu CY
    Sci Rep; 2022 Jul; 12(1):11235. PubMed ID: 35787657
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject that Underwent a Rehabilitation Treatment in the Early Stage of Stroke.
    Sale P; Ferriero G; Ciabattoni L; Cortese AM; Ferracuti F; Romeo L; Piccione F; Masiero S
    J Stroke Cerebrovasc Dis; 2018 Nov; 27(11):2962-2972. PubMed ID: 30077601
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning is an effective method to predict the 90-day prognosis of patients with transient ischemic attack and minor stroke.
    Chen SD; You J; Yang XM; Gu HQ; Huang XY; Liu H; Feng JF; Jiang Y; Wang YJ
    BMC Med Res Methodol; 2022 Jul; 22(1):195. PubMed ID: 35842606
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.
    Iwamoto Y; Imura T; Tanaka R; Mitsutake T; Jung H; Suzukawa T; Taki S; Imada N; Inagawa T; Araki H; Araki O
    J Stroke Cerebrovasc Dis; 2022 Jul; 31(7):106517. PubMed ID: 35500359
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis.
    Barth J; Lohse KR; Bland MD; Lang CE
    J Neuroeng Rehabil; 2023 Feb; 20(1):24. PubMed ID: 36810072
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Construction of efficacious gait and upper limb functional interventions based on brain plasticity evidence and model-based measures for stroke patients.
    Daly JJ; Ruff RL
    ScientificWorldJournal; 2007 Dec; 7():2031-45. PubMed ID: 18167618
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study.
    Park E; Lee K; Han T; Nam HS
    J Med Internet Res; 2020 Sep; 22(9):e20641. PubMed ID: 32936079
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.
    Campagnini S; Arienti C; Patrini M; Liuzzi P; Mannini A; Carrozza MC
    J Neuroeng Rehabil; 2022 Jun; 19(1):54. PubMed ID: 35659246
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Models containing age and NIHSS predict recovery of ambulation and upper limb function six months after stroke: an observational study.
    Kwah LK; Harvey LA; Diong J; Herbert RD
    J Physiother; 2013 Sep; 59(3):189-97. PubMed ID: 23896334
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting motor recovery of the upper limb after stroke rehabilitation: value of a clinical examination.
    Feys H; De Weerdt W; Nuyens G; van de Winckel A; Selz B; Kiekens C
    Physiother Res Int; 2000; 5(1):1-18. PubMed ID: 10785907
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.