Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes.
about
Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression.Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.Predicting congenital heart defects: A comparison of three data mining methods.Modeling long-term human activeness using recurrent neural networks for biometric data.Prediction of in-hospital mortality after pancreatic resection in pancreatic cancer patients: A boosting approach via a population-based study using health administrative data.Detection of independent associations in a large epidemiologic dataset: a comparison of random forests, boosted regression trees, conventional and penalized logistic regression for identifying independent factors associated with H1N1pdm influenza inThe electronic health record for translational research.Refining hypertension surveillance to account for potentially misclassified cases.Exploring Guidelines for Classification of Major Heart Failure Subtypes by Using Machine Learning.Behavior Correlates of Post-Stroke Disability Using Data Mining and Infographics.A pilot study investigating changes in neural processing after mindfulness training in elite athletes.Using EHRs and Machine Learning for Heart Failure Survival Analysis.Real-time prediction of inpatient length of stay for discharge prioritization.Systematic mapping study of data mining-based empirical studies in cardiology.A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.Key steps and common pitfalls in developing and validating risk models.Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers.The Reliability of Classification of Terminal Nodes in GUIDE Decision Tree to Predict the Nonalcoholic Fatty Liver Disease.Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction.Risk prediction with machine learning and regression methods.Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation.Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference.Analysis of factors associated with extended recovery time after colonoscopy.Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical NarrativesDeep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
P2860
Q28075346-260DDDB8-9F48-4E14-ACFD-923326EB12A4Q30832046-821EAC0A-FCB8-49EE-9B3E-1BC3273E1A06Q31171239-B2534B7D-5296-4788-AA37-5578D76D74A1Q33724874-C7E11CD4-BC62-4E4B-83BE-54340182ACCBQ33726455-609453BD-1C9E-4B10-9C9A-DBD8A558FA88Q33773064-B5964617-C7FF-4403-9444-CC6E1B2B6F5FQ34096995-1A40D28C-E70D-497F-8AF2-53D6335531AEQ34101283-584C1633-E170-4788-9AAD-81139A791A9FQ35214328-C70DFBAD-E1F1-45B9-8A90-8693AC7B5A66Q35636729-AB9FCCA1-4AAA-46CA-B425-952819E215E9Q35911690-A6F4248A-6306-4F5D-8EAB-E0C776ED60ABQ35999176-7B05B105-0CFD-4E9B-8D56-02F79DFD92FAQ36999088-4CA75D85-D5CC-4427-AF6D-CF128A812C9DQ37112116-C58953C6-8EF7-4907-89EC-C44BFCC5E1CFQ38649982-E24C4415-4804-46FA-BC9D-B497C411FA9BQ38808665-B43759C2-30F2-4ACA-AF4B-DFC1CD1FD76DQ38881882-F6740FA4-E503-46A9-8B89-0F5F17B01F75Q38902965-EA866BF3-6C72-4367-BCC6-D0054CA98234Q39034492-21397C6D-2301-452B-ACE5-419DC6B469EDQ39039524-17862DA1-F40A-4B4D-B2E9-F83799DE50B8Q41528093-20437E79-8036-4AC6-B3DC-9B81C0A14CF3Q45957547-40B60EF9-1D01-45C1-9ED9-2AC00CF44BB8Q47107061-60A12B2D-330F-4451-9893-8451D8A2CBFDQ53819930-846693AB-4201-4C8B-B1D3-DEEB77D4139DQ55084096-8B8A4E3C-509B-460B-AAB7-84E9A56C70FFQ55439035-6963B533-183B-4358-89AB-A4359842F9FDQ56873456-9FEE4E4C-BFD8-44D3-9305-14874081593BQ57164138-7B44A01C-8FF7-4010-89FD-6040F55DA1B0
P2860
Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes.
description
2013 nî lūn-bûn
@nan
2013 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
Using methods from the data-mi ...... ion of heart failure subtypes.
@ast
Using methods from the data-mi ...... ion of heart failure subtypes.
@en
type
label
Using methods from the data-mi ...... ion of heart failure subtypes.
@ast
Using methods from the data-mi ...... ion of heart failure subtypes.
@en
prefLabel
Using methods from the data-mi ...... ion of heart failure subtypes.
@ast
Using methods from the data-mi ...... ion of heart failure subtypes.
@en
P2093
P2860
P921
P1476
Using methods from the data-mi ...... ion of heart failure subtypes.
@en
P2093
Daniel Levy
Douglas S Lee
P2860
P304
P356
10.1016/J.JCLINEPI.2012.11.008
P577
2013-02-04T00:00:00Z