| scholarly article | Q13442814 |
| P356 | DOI | 10.1111/J.1468-1331.2010.02955.X |
| P698 | PubMed publication ID | 20136650 |
| P2093 | author name string | S-Y Peng | |
| Y-C Chuang | |||
| T-W Kang | |||
| K-H Tseng | |||
| P2860 | cites work | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 |
| Gene selection and classification of microarray data using random forest. | Q25255911 | ||
| A method of comparing the areas under receiver operating characteristic curves derived from the same cases | Q34272510 | ||
| Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma | Q34365572 | ||
| Emerging medical and surgical management strategies in the evaluation and treatment of intracerebral hemorrhage | Q36065818 | ||
| Treatment of intracerebral haemorrhage. | Q36260003 | ||
| Guidelines for the management of spontaneous intracerebral hemorrhage in adults: 2007 update: a guideline from the American Heart Association/American Stroke Association Stroke Council, High Blood Pressure Research Council, and the Quality of Care a | Q36810929 | ||
| Effects of computer-based clinical decision support systems on clinician performance and patient outcome. A critical appraisal of research | Q38567781 | ||
| External validation is necessary in prediction research: a clinical example | Q40549827 | ||
| Use of the original, modified, or new intracerebral hemorrhage score to predict mortality and morbidity after intracerebral hemorrhage | Q40581539 | ||
| The ICH score: a simple, reliable grading scale for intracerebral hemorrhage | Q43503411 | ||
| Multivariate analysis of predictors of hematoma enlargement in spontaneous intracerebral hemorrhage | Q47976074 | ||
| Guidelines for thrombolytic therapy for acute stroke: a supplement to the guidelines for the management of patients with acute ischemic stroke. A statement for healthcare professionals from a Special Writing Group of the Stroke Council, American Hea | Q48936157 | ||
| Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage. | Q52208079 | ||
| Surgical treatment for intracerebral hemorrhage (STICH): a single-center, randomized clinical trial. | Q53942562 | ||
| The ABCs of measuring intracerebral hemorrhage volumes. | Q54430652 | ||
| P433 | issue | 7 | |
| P921 | main subject | random forest | Q245748 |
| P304 | page(s) | 945-950 | |
| P577 | publication date | 2010-02-03 | |
| P1433 | published in | European Journal of Neurology | Q15757256 |
| P1476 | title | Random forest can predict 30-day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination. | |
| P478 | volume | 17 |
| Q31020461 | A prospective controlled study: minimally invasive stereotactic puncture therapy versus conventional craniotomy in the treatment of acute intracerebral hemorrhage |
| Q60690203 | Assessment and Comparison of the Four Most Extensively Validated Prognostic Scales for Intracerebral Hemorrhage: Systematic Review with Meta-analysis |
| Q33910768 | Hematoma shape, hematoma size, Glasgow coma scale score and ICH score: which predicts the 30-day mortality better for intracerebral hematoma? |
| Q34729396 | Minimally invasive stereotactic puncture and thrombolysis therapy improves long-term outcome after acute intracerebral hemorrhage |
| Q35690650 | Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting |
| Q64972320 | Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine. |
| Q35218610 | Predictive modeling in pediatric traumatic brain injury using machine learning. |
| Q36130061 | Prognostic Tools for Early Mortality in Hemorrhagic Stroke: Systematic Review and Meta-Analysis |
| Q59329882 | Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis |
| Q92290036 | The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis |
| Q50000107 | Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis |
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