What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Observational study, Statistics, Bayesian inference, Econometrics and Artificial intelligence are his primary areas of study.
The study incorporates disciplines such as Research design, Health informatics, Study heterogeneity, Informatics and Cohort in addition to Observational study.
David Madigan focuses mostly in the field of Statistics, narrowing it down to topics relating to Feature selection and, in certain cases, Posterior probability, Regression analysis, Outlier and Logistic model tree.
His Bayesian inference study integrates concerns from other disciplines, such as Graphical model, Linear model and Markov chain Monte Carlo.
The various areas that David Madigan examines in his Markov chain Monte Carlo study include Linear regression, Focused information criterion, Model selection, Bayesian statistics and Markov chain.
His Artificial intelligence research includes elements of Machine learning and Logistic regression.
His most cited work include:
- Least angle regression (6995 citations)
- Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (3392 citations)
- Bayesian Model Averaging for Linear Regression Models (1367 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Observational study, Bayesian probability and Data mining.
His work in the fields of Association rule learning overlaps with other areas such as Context and Supervised learning.
To a larger extent, David Madigan studies Statistics with the aim of understanding Observational study.
His Statistics course of study focuses on Econometrics and Multivariate statistics.
Within one scientific family, David Madigan focuses on topics pertaining to Graphical model under Bayesian probability, and may sometimes address concerns connected to Variable-order Bayesian network.
Markov chain is closely connected to Markov chain Monte Carlo in his research, which is encompassed under the umbrella topic of Bayesian inference.
He most often published in these fields:
- Artificial intelligence (26.36%)
- Machine learning (23.64%)
- Observational study (25.91%)
What were the highlights of his more recent work (between 2014-2021)?
- Observational study (25.91%)
- Bayesian probability (18.18%)
- Internal medicine (5.00%)
In recent papers he was focusing on the following fields of study:
David Madigan mainly focuses on Observational study, Bayesian probability, Internal medicine, Econometrics and Machine learning.
His Econometrics research is multidisciplinary, incorporating elements of Meta-analysis, Linear regression and Time series.
His study in Machine learning is interdisciplinary in nature, drawing from both Consistency, Null and Artificial intelligence.
David Madigan works mostly in the field of Data science, limiting it down to topics relating to Regression analysis and, in certain cases, Database, as a part of the same area of interest.
Confidence interval is a subfield of Statistics that he investigates.
His work carried out in the field of Statistics brings together such families of science as Statistical classification and Categorization.
Between 2014 and 2021, his most popular works were:
- Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (350 citations)
- Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers (323 citations)
- Good practices for real‐world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR‐ISPE Special Task Force on real‐world evidence in health care decision making (174 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His main research concerns Observational study, Health policy, Informatics, Health informatics and Comparative effectiveness research.
David Madigan combines subjects such as Calibration, Sample size determination and Confounding with his study of Observational study.
His Sample size determination study deals with the bigger picture of Statistics.
His Informatics research is multidisciplinary, relying on both Health services research, Health Administration Informatics, Observational Studies as Topic and Data science.
His Health informatics research incorporates themes from Clinical trial, Gerontology, Econometrics, Generalizability theory and Cohort.
His biological study spans a wide range of topics, including Health technology, Evidence-based medicine, Medical education and Outcomes research.
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