Linda D. Sharples
researcher
Facts
12 properties- Instance of
- human
- Country of citizenship
- United Kingdom[2]
- Occupation
- researcher
- Educated at
- University of Nottingham
- Doctoral advisor
- Adrian Smith[4]
- Doctoral student
- Christopher Jackson, Andrew C. Titman[6][7]
- Maintained by WikiProject
- WikiProject Mathematics[12]
- Start of work period
- January 17, 1989[11]
🏷️ Also known as
en Linda Dawn Sharples
en Linda D. Sharples
en Linda Sharples
nl Linda D Sharples
🔖 Identifiers · 12
- ISNI
- 0000000133998928
- ORCID iD
- 0000-0003-0894-966X
- VIAF cluster ID
- 48752153
- Library of Congress Authorities ID
- nb2007000934
- Dimensions author ID
- 010013613077.96
- Google Knowledge Graph ID
- /g/11fyz9ts6k
- MR Author ID
- 305533
- Mathematics Genealogy Project ID
- 130303
- Scopus author ID
- 7004964663
- Springer Nature person ID
- 010013613077.96
- WorldCat Entities ID
- E39PBJdCkqvBKMWhb3JRPHkhpP
- ZbMATH author ID
- sharples.linda-d
🔗 Connections
Author of 7
Assessment of learning curves in complex surgical interventions: a consecutive case-series study,
Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data,
Dynamic Survival Prediction Combining Landmarking with a Machine Learning Ensemble: Methodology and Empirical Comparison,
Identifying the research priorities of healthcare professionals in UK vascular surgery: modified Delphi approach,
Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably,
Probabilistic linkage without personal information successfully linked national clinical datasets,
Reducing bias in trials due to reactions to measurement: experts produced recommendations informed by evidence
← Authored by 7
Assessment of learning curves in complex surgical interventions: a consecutive case-series study,
Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data,
Dynamic Survival Prediction Combining Landmarking with a Machine Learning Ensemble: Methodology and Empirical Comparison,
Identifying the research priorities of healthcare professionals in UK vascular surgery: modified Delphi approach,
Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably,
Probabilistic linkage without personal information successfully linked national clinical datasets,
Reducing bias in trials due to reactions to measurement: experts produced recommendations informed by evidence
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