Jonathan S. Schildcrout
- Cardiology and Cardiovascular Medicine top 2%
- General Health Professions top 2%
- Surgery top 5%
- Health, Toxicology and Mutagenesis top 2%
- Nephrology top 1%
- Co-authors
- Josh F. PetersonNathaniel D. MercaldoJoshua C. DennyLianne SheppardYaping ShiSunil KripalaniJoel D. KaufmanJill M. Pulley
- Topics
- Statistical Methods and Bayesian Inference (22 papers)Advanced Causal Inference Techniques (18 papers)Statistical Methods and Inference (14 papers)
- Journals
- NatureJAMACirculation
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Jonathan S. Schildcrout
123 papers receiving 4.0k citations
Peers
Comparison fields: 5 of 183
- Cardiology and Cardiovascular Medicine 769
- General Health Professions 601
- Surgery 588
- Health, Toxicology and Mutagenesis 564
- Nephrology 465
Countries citing papers authored by Jonathan S. Schildcrout
This map shows the geographic impact of Jonathan S. Schildcrout's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jonathan S. Schildcrout with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan S. Schildcrout more than expected).
Fields of papers citing papers by Jonathan S. Schildcrout
This network shows the impact of papers produced by Jonathan S. Schildcrout. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jonathan S. Schildcrout. The network helps show where Jonathan S. Schildcrout may publish in the future.
Co-authorship network of co-authors of Jonathan S. Schildcrout
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan S. Schildcrout. A scholar is included among the top collaborators of Jonathan S. Schildcrout based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jonathan S. Schildcrout. Jonathan S. Schildcrout is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 3 | |
| 8 | 2 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 6 | |
| 12 | 22 | |
| 13 | 13 | |
| 14 | Extracting Drug Exposure Epochs and Drug Response Outcomes from Electronic Health Records. | 0 |
| 15 | 53 | |
| 16 | 17 | |
| 17 | 20 | |
| 18 | Abstract 2684: Modulators of Normal ECG Intervals Identified in a large Electronic Medical Record | 1 |
| 19 | 26 | |
| 20 | 194 |
About Jonathan S. Schildcrout
Jonathan S. Schildcrout is a scholar working on Statistics and Probability, Geriatrics and Gerontology and Family Practice, having authored 131 papers that have together received 4.1k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (22 papers), Advanced Causal Inference Techniques (18 papers) and Statistical Methods and Inference (14 papers). The work is most often cited by research in Nephrology (465 citations), Family Practice (95 citations) and Health, Toxicology and Mutagenesis (564 citations). Jonathan S. Schildcrout has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Josh F. Peterson, Nathaniel D. Mercaldo, Joshua C. Denny, Lianne Sheppard, Yaping Shi, Sunil Kripalani, Joel D. Kaufman, Jill M. Pulley, Jane Q. Koenig and Gail G. Shapiro. Their work appears in journals such as Nature, JAMA and Circulation.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.