139 related articles for article (PubMed ID: 34656249)
1. Machine learning-based modified BAT score in predicting hematoma enlargement after spontaneous intracerebral hemorrhage.
Zhou H; Zhou Z; Song Z; Li X
J Clin Neurosci; 2021 Nov; 93():206-212. PubMed ID: 34656249
[TBL] [Abstract][Full Text] [Related]
2. Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage.
Li H; Xie Y; Liu H; Wang X
Clin Neuroradiol; 2022 Jun; 32(2):517-528. PubMed ID: 34324004
[TBL] [Abstract][Full Text] [Related]
3. Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage.
Tanioka S; Yago T; Tanaka K; Ishida F; Kishimoto T; Tsuda K; Ikezawa M; Araki T; Miura Y; Suzuki H
Sci Rep; 2022 Jul; 12(1):12452. PubMed ID: 35864139
[TBL] [Abstract][Full Text] [Related]
4. Machine Learning-Based Perihematomal Tissue Features to Predict Clinical Outcome after Spontaneous Intracerebral Hemorrhage.
Qi X; Hu G; Sun H; Chen Z; Yang C
J Stroke Cerebrovasc Dis; 2022 Jun; 31(6):106475. PubMed ID: 35417846
[TBL] [Abstract][Full Text] [Related]
5. Noncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage.
Song Z; Guo D; Tang Z; Liu H; Li X; Luo S; Yao X; Song W; Song J; Zhou Z
Korean J Radiol; 2021 Mar; 22(3):415-424. PubMed ID: 33169546
[TBL] [Abstract][Full Text] [Related]
6. Radiomics features on non-contrast computed tomography predict early enlargement of spontaneous intracerebral hemorrhage.
Li H; Xie Y; Wang X; Chen F; Sun J; Jiang X
Clin Neurol Neurosurg; 2019 Oct; 185():105491. PubMed ID: 31470362
[TBL] [Abstract][Full Text] [Related]
7. Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm.
Du C; Li Y; Yang M; Ma Q; Ge S; Ma C
World Neurosurg; 2024 May; 185():e475-e483. PubMed ID: 38387789
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.
Geng Z; Yang C; Zhao Z; Yan Y; Guo T; Liu C; Wu A; Wu X; Wei L; Tian Y; Hu P; Wang K
J Transl Med; 2024 Mar; 22(1):236. PubMed ID: 38439097
[TBL] [Abstract][Full Text] [Related]
9. BAT Score Versus Spot Sign in Predicting Intracerebral Hemorrhage Expansion.
Yu Z; Zheng J; Xia F; Guo R; Ma L; You C; Li H
World Neurosurg; 2019 Jun; 126():e694-e698. PubMed ID: 30844526
[TBL] [Abstract][Full Text] [Related]
10. Identifying Modifiable Predictors of Patient Outcomes After Intracerebral Hemorrhage with Machine Learning.
Hall AN; Weaver B; Liotta E; Maas MB; Faigle R; Mroczek DK; Naidech AM
Neurocrit Care; 2021 Feb; 34(1):73-84. PubMed ID: 32385834
[TBL] [Abstract][Full Text] [Related]
11. Predicting Intracerebral Hemorrhage Expansion With Noncontrast Computed Tomography: The BAT Score.
Morotti A; Dowlatshahi D; Boulouis G; Al-Ajlan F; Demchuk AM; Aviv RI; Yu L; Schwab K; Romero JM; Gurol ME; Viswanathan A; Anderson CD; Chang Y; Greenberg SM; Qureshi AI; Rosand J; Goldstein JN;
Stroke; 2018 May; 49(5):1163-1169. PubMed ID: 29669875
[TBL] [Abstract][Full Text] [Related]
12. Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma.
Serrano E; Moreno J; Llull L; Rodríguez A; Zwanzger C; Amaro S; Oleaga L; López-Rueda A
Radiologia (Engl Ed); 2023; 65(6):519-530. PubMed ID: 38049251
[TBL] [Abstract][Full Text] [Related]
13. Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.
Liu J; Xu H; Chen Q; Zhang T; Sheng W; Huang Q; Song J; Huang D; Lan L; Li Y; Chen W; Yang Y
EBioMedicine; 2019 May; 43():454-459. PubMed ID: 31060901
[TBL] [Abstract][Full Text] [Related]
14. Deep Learning-Based Prediction of Hematoma Expansion Using a Single Brain Computed Tomographic Slice in Patients With Spontaneous Intracerebral Hemorrhages.
Tang Z; Zhu Y; Lu X; Wu D; Fan X; Shen J; Xiao L
World Neurosurg; 2022 Sep; 165():e128-e136. PubMed ID: 35680084
[TBL] [Abstract][Full Text] [Related]
15. Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model.
Xie H; Ma S; Wang X; Zhang X
Eur Radiol; 2020 Jan; 30(1):87-98. PubMed ID: 31385050
[TBL] [Abstract][Full Text] [Related]
16. Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion.
Duan C; Liu F; Gao S; Zhao J; Niu L; Li N; Liu S; Wang G; Zhou X; Ren Y; Xu W; Liu X
Clin Neuroradiol; 2022 Mar; 32(1):215-223. PubMed ID: 34156513
[TBL] [Abstract][Full Text] [Related]
17. Comparison of Ultra-Early Hematoma Growth and Common Noncontrast Computed Tomography Features in Predicting Hematoma Enlargement in Patients with Spontaneous Intracerebral Hemorrhage.
Xiang Y; Zhang T; Li Y; Liu J; Xu H; He W; Chen Q; Yang Y
World Neurosurg; 2020 Feb; 134():e75-e81. PubMed ID: 31648055
[TBL] [Abstract][Full Text] [Related]
18. Establishment of a Scale for Predicting Early Hematoma Enlargement of Spontaneous Intracerebral Hemorrhage Based on Non-Contrast CT Signs.
Li ZC; Kong XY; DU QQ; Zhang T; Wang X; Qian ZY
Turk Neurosurg; 2023; 33(4):556-567. PubMed ID: 37309626
[TBL] [Abstract][Full Text] [Related]
19. Calculation of Prognostic Scores, Using Delayed Imaging, Outperforms Baseline Assessments in Acute Intracerebral Hemorrhage.
Lun R; Yogendrakumar V; Demchuk AM; Aviv RI; Rodriguez-Luna D; Molina CA; Silva Y; Dzialowski I; Kobayashi A; Boulanger JM; Gubitz G; Srivastava P; Roy J; Kase CS; Bhatia R; Hill MD; Dowlatshahi D
Stroke; 2020 Apr; 51(4):1107-1110. PubMed ID: 32151235
[TBL] [Abstract][Full Text] [Related]
20. Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning.
Garg R; Prabhakaran S; Holl JL; Luo Y; Faigle R; Kording K; Naidech AM
J Stroke Cerebrovasc Dis; 2018 Dec; 27(12):3570-3574. PubMed ID: 30201458
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]