scholarly article | Q13442814 |
P50 | author | Hemant Ishwaran | Q70962941 |
Eugene H Blackstone | Q96650503 | ||
Michael S. Lauer | Q27205541 | ||
P2093 | author name string | Eiran Z Gorodeski | |
Eileen Hsich | |||
P2860 | cites work | Analysis of multiple SNPs in genetic association studies: comparison of three multi-locus methods to prioritize and select SNPs. | Q51910939 |
Optimum Lymphadenectomy for Esophageal Cancer | Q63546499 | ||
Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation | Q73437901 | ||
Heart rate recovery and treadmill exercise score as predictors of mortality in patients referred for exercise ECG | Q74318055 | ||
The Munich score: a clinical index to predict survival in ambulatory patients with chronic heart failure in the era of new medical therapies | Q80679605 | ||
Screening large-scale association study data: exploiting interactions using random forests | Q24809535 | ||
Prediction of creatinine clearance from serum creatinine | Q29615603 | ||
Evaluation of different biological data and computational classification methods for use in protein interaction prediction | Q31031427 | ||
Short-term prediction of mortality in patients with systemic lupus erythematosus: classification of outcomes using random forests | Q33370218 | ||
Importance of treadmill exercise time as an initial prognostic screening tool in patients with systolic left ventricular dysfunction | Q34380854 | ||
Multiple biomarkers for the prediction of first major cardiovascular events and death | Q34593507 | ||
Immunogenetic risk and protective factors for juvenile dermatomyositis in Caucasians. | Q36116606 | ||
A novel approach to cancer staging: application to esophageal cancer | Q36661978 | ||
An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer | Q36985056 | ||
Logistic regression had superior performance compared with regression trees for predicting in-hospital mortality in patients hospitalized with heart failure | Q39890107 | ||
The Seattle Heart Failure Model: prediction of survival in heart failure | Q40343385 | ||
Electrocardiographic abnormalities that predict coronary heart disease events and mortality in postmenopausal women: the Women's Health Initiative | Q40354008 | ||
Risk stratification in middle-aged patients with congestive heart failure: prospective comparison of the Heart Failure Survival Score (HFSS) and a simplified two-variable model | Q44350261 | ||
Prognostic evaluation of ambulatory patients with advanced heart failure | Q44361780 | ||
A multivariate model for predicting mortality in patients with heart failure and systolic dysfunction | Q44925862 | ||
P433 | issue | 1 | |
P921 | main subject | heart failure | Q181754 |
systole | Q496359 | ||
patient | Q181600 | ||
systolic heart failure | Q17326470 | ||
P304 | page(s) | 39-45 | |
P577 | publication date | 2010-11-23 | |
P1433 | published in | Circulation: Cardiovascular Quality and Outcomes | Q15816436 |
P1476 | title | Identifying important risk factors for survival in patient with systolic heart failure using random survival forests | |
P478 | volume | 4 |
Q92678846 | A 5-Gene Prognostic Combination for Predicting Survival of Patients with Gastric Cancer |
Q50148804 | A prediction-based alternative to P values in regression models |
Q33865743 | An adjustable predictive score of graft survival in kidney transplant patients and the levels of risk linked to de novo donor-specific anti-HLA antibodies |
Q35731376 | An international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies. |
Q62491891 | Application of machine learning in rheumatic disease research |
Q41521441 | Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption |
Q55083039 | Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells. |
Q55147156 | Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis. |
Q37905948 | Biomarkers in advanced heart failure: diagnostic and therapeutic insights |
Q40088646 | Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis |
Q64103578 | Complex inter-relationship of body mass index, gender and serum creatinine on survival: exploring the obesity paradox in melanoma patients treated with checkpoint inhibition |
Q64066290 | Deep learning-based survival prediction of oral cancer patients |
Q90611283 | Determining hypertensive patients' beliefs towards medication and associations with medication adherence using machine learning methods |
Q41581523 | Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach |
Q45782679 | Discordance between 'actual' and 'scheduled' check-in times at a heart failure clinic |
Q33764535 | Do Non-Clinical Factors Improve Prediction of Readmission Risk?: Results From the Tele-HF Study |
Q36283423 | Early illness features associated with mortality in the juvenile idiopathic inflammatory myopathies. |
Q92551681 | Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions |
Q38642866 | Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention. |
Q37573616 | Home monitoring of heart failure patients at risk for hospital readmission using a novel under-the-mattress piezoelectric sensor: A preliminary single centre experience |
Q22673963 | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests |
Q61811312 | Impact of time to local recurrence on the occurrence of metastasis in breast cancer patients treated with neoadjuvant chemotherapy: A random forest survival approach |
Q38866170 | Individualized Knowledge Graph: A Viable Informatics Path to Precision Medicine. |
Q38634420 | Machine Learning in Medicine |
Q98181103 | Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients |
Q59340740 | Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data |
Q31052875 | Prediction of Hematopoietic Stem Cell Transplantation Related Mortality- Lessons Learned from the In-Silico Approach: A European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study |
Q37592322 | Publication of trials funded by the National Heart, Lung, and Blood Institute |
Q37285517 | Right atrial area and right ventricular outflow tract akinetic length predict sustained tachyarrhythmia in repaired tetralogy of Fallot. |
Q92068939 | Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model |
Q36035649 | Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest |
Q40340737 | Severe chronic norovirus diarrheal disease in transplant recipients: Clinical features of an under-recognized syndrome |
Q88992040 | Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival |
Q91939594 | The application of convolutional neural network to stem cell biology |
Q92468889 | Understanding and Addressing Variation in Health Care-Associated Infections After Durable Ventricular Assist Device Therapy: Protocol for a Mixed Methods Study |
Q92051076 | Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol |
Q35855191 | Using machine learning to examine medication adherence thresholds and risk of hospitalization |
Search more.