237 related articles for article (PubMed ID: 34022582)
1. 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]
2. 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]
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-Based Model for Prediction of Outcomes in Acute Stroke.
Heo J; Yoon JG; Park H; Kim YD; Nam HS; Heo JH
Stroke; 2019 May; 50(5):1263-1265. PubMed ID: 30890116
[TBL] [Abstract][Full Text] [Related]
5. Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.
Han L; Askari M; Altman RB; Schmitt SK; Fan J; Bentley JP; Narayan SM; Turakhia MP
Circ Cardiovasc Qual Outcomes; 2019 Oct; 12(10):e005595. PubMed ID: 31610712
[TBL] [Abstract][Full Text] [Related]
6. 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]
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. Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.
Imura T; Iwamoto Y; Inagawa T; Imada N; Tanaka R; Toda H; Inoue Y; Araki H; Araki O
J Stroke Cerebrovasc Dis; 2021 Apr; 30(4):105636. PubMed ID: 33545520
[TBL] [Abstract][Full Text] [Related]
9. Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation.
Lin WY; Chen CH; Tseng YJ; Tsai YT; Chang CY; Wang HY; Chen CK
Int J Med Inform; 2018 Mar; 111():159-164. PubMed ID: 29425627
[TBL] [Abstract][Full Text] [Related]
10. Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.
Imura T; Toda H; Iwamoto Y; Inagawa T; Imada N; Tanaka R; Inoue Y; Araki H; Araki O
J Stroke Cerebrovasc Dis; 2021 Oct; 30(10):106011. PubMed ID: 34325274
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Developing predictive models of excellent and devastating outcome after stroke.
Reid JM; Dai D; Christian C; Reidy Y; Counsell C; Gubitz GJ; Phillips SJ
Age Ageing; 2012 Jul; 41(4):560-4. PubMed ID: 22440586
[TBL] [Abstract][Full Text] [Related]
13. Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms.
Sung SM; Kang YJ; Cho HJ; Kim NR; Lee SM; Choi BK; Cho G
Clin Neurol Neurosurg; 2020 Aug; 195():105892. PubMed ID: 32416324
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Machine Learning for Brain Stroke: A Review.
Sirsat MS; Fermé E; Câmara J
J Stroke Cerebrovasc Dis; 2020 Oct; 29(10):105162. PubMed ID: 32912543
[TBL] [Abstract][Full Text] [Related]
16. Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
Guo A; Mazumder NR; Ladner DP; Foraker RE
PLoS One; 2021; 16(8):e0256428. PubMed ID: 34464403
[TBL] [Abstract][Full Text] [Related]
17. Fetal health status prediction based on maternal clinical history using machine learning techniques.
Akbulut A; Ertugrul E; Topcu V
Comput Methods Programs Biomed; 2018 Sep; 163():87-100. PubMed ID: 30119860
[TBL] [Abstract][Full Text] [Related]
18. Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.
Zack CJ; Senecal C; Kinar Y; Metzger Y; Bar-Sinai Y; Widmer RJ; Lennon R; Singh M; Bell MR; Lerman A; Gulati R
JACC Cardiovasc Interv; 2019 Jul; 12(14):1304-1311. PubMed ID: 31255564
[TBL] [Abstract][Full Text] [Related]
19. Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction.
Qiu C; Su K; Luo Z; Tian Q; Zhao L; Wu L; Deng H; Shen H
Front Artif Intell; 2024; 7():1355287. PubMed ID: 38919268
[TBL] [Abstract][Full Text] [Related]
20. Effects of the Interaction among Motor Functions on Self-care in Individuals with Stroke.
Fujita T; Iokawa K; Sone T; Yamane K; Yamamoto Y; Ohira Y; Otsuki K
J Stroke Cerebrovasc Dis; 2019 Nov; 28(11):104387. PubMed ID: 31542365
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]