How to develop a more accurate risk prediction model when there are few events.
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Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.A scoring system to predict breast cancer mortality at 5 and 10 yearsAnxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.Explaining ethnic disparities in lung function among young adults: A pilot investigation.Risk prediction of pulmonary tuberculosis using genetic and conventional risk factors in adult Korean population.External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges.Early Response in Cellulitis: A Prospective Study of Dynamics and Predictors.Development of a prognostic model based on demographic, environmental and lifestyle information for predicting incidences of symptomatic respiratory or gastrointestinal infection in adult office workers.A Clinical Prediction Score to Guide Referral of Elderly Dialysis Patients for Kidney Transplant Evaluation.Development of a Prediction Model for Stress Fracture During an Intensive Physical Training Program: The Royal Marines Commandos.Enhancing risk stratification for use in integrated care: a cluster analysis of high-risk patients in a retrospective cohort study.Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.Normal Tissue Complication Probability (NTCP) Modelling of Severe Acute Mucositis using a Novel Oral Mucosal Surface Organ at Risk.Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapyCardiopulmonary exercise test and sudden cardiac death risk in hypertrophic cardiomyopathy.Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.Identifying patients at risk of acute symptomatic seizure after ischemic stroke.Survival analysis of heart failure patients: A case study.Assessing risk in pulmonary arterial hypertension: what we know, what we don't.The Pediatric Submersion Score Predicts Children at Low Risk for Injury Following Submersions.PRiMeUM: A Model for Predicting Risk of Metastasis in Uveal Melanoma.Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study.Predictive modeling of inpatient mortality in departments of internal medicine.Surgical Outcome Risk Tool (SORT) validation in hepatectomy.Development and validation of a risk assessment tool for gastric cancer in a general Japanese population.Hematological Parameters Outperform Plasma Markers in Predicting Long-Term Mortality After Coronary Angiography.Clinical Pharmacogenetic Models of Treatment Response to Methotrexate Monotherapy in Slovenian and Serbian Rheumatoid Arthritis Patients: Differences in Patient's Management May Preclude Generalization of the Models.Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy.Nomogram-based prediction of rebleeding in small bowel bleeding patients: the 'PRSBB' score.Validation of the American College of Surgeons Risk Calculator for preoperative risk stratification.Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.Systematic review of prediction models for delirium in the older adult inpatient.Prediction of autoimmune connective tissue disease in an at-risk cohort: prognostic value of a novel two-score system for interferon statusContribution of central and peripheral risk factors to prevalence, incidence and progression of knee pain: a community-based cohort studyCan patient-reported profiles avoid unnecessary referral to a spine surgeon? An observational study to further develop the Nijmegen Decision Tool for Chronic Low Back PainA scoring system to detect fixed airflow limitation in smokers from simple easy-to-use parametersPrediction of Violence, Suicide Behaviors and Suicide Ideation in a Sample of Institutionalized Offenders With Schizophrenia and Other Psychosis
P2860
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P2860
How to develop a more accurate risk prediction model when there are few events.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
How to develop a more accurate risk prediction model when there are few events.
@ast
How to develop a more accurate risk prediction model when there are few events.
@en
type
label
How to develop a more accurate risk prediction model when there are few events.
@ast
How to develop a more accurate risk prediction model when there are few events.
@en
prefLabel
How to develop a more accurate risk prediction model when there are few events.
@ast
How to develop a more accurate risk prediction model when there are few events.
@en
P2093
P2860
P356
P1433
P1476
How to develop a more accurate risk prediction model when there are few events
@en
P2093
Gareth Ambler
Menelaos Pavlou
Michael King
Oliver Guttmann
Perry Elliott
Rumana Z Omar
P2860
P356
10.1136/BMJ.H3868
P407
P577
2015-08-11T00:00:00Z