Rachael V. Phillips
- Health Informatics top 1%
- Artificial Intelligence
- Statistics and Probability top 5%
- Radiology, Nuclear Medicine and Imaging
- Molecular Biology
- Co-authors
- Alan HubbardIvana MalenicaRomain PirracchioAndrew BisharaLeo Anthony CeliJean FengHana LeeSusan Gruber
- Topics
- Advanced Causal Inference Techniques (10 papers)Statistical Methods and Inference (5 papers)Statistical Methods and Bayesian Inference (4 papers)
- Journals
- American Journal of EpidemiologyEnvironment InternationalInternational Journal of Epidemiology
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Rachael V. Phillips
20 papers receiving 435 citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Health Informatics 119
- Artificial Intelligence 86
- Statistics and Probability 75
- Radiology, Nuclear Medicine and Imaging 63
- Molecular Biology 57
Countries citing papers authored by Rachael V. Phillips
This map shows the geographic impact of Rachael V. Phillips'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 Rachael V. Phillips with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rachael V. Phillips more than expected).
Fields of papers citing papers by Rachael V. Phillips
This network shows the impact of papers produced by Rachael V. Phillips. 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 Rachael V. Phillips. The network helps show where Rachael V. Phillips may publish in the future.
Co-authorship network of co-authors of Rachael V. Phillips
This figure shows the co-authorship network connecting the top 25 collaborators of Rachael V. Phillips. A scholar is included among the top collaborators of Rachael V. Phillips 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 Rachael V. Phillips. Rachael V. Phillips is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 58 | |
| 5 | 3 | |
| 6 | 18 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 7 | |
| 10 | Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcarebreakdown → | 195 |
| 11 | 8 | |
| 12 | 2 | |
| 13 | 14 | |
| 14 | 11 | |
| 15 | 19 | |
| 16 | 40 | |
| 17 | 12 | |
| 18 | 16 | |
| 19 | 15 | |
| 20 | 18 |
About Rachael V. Phillips
Rachael V. Phillips is a scholar working on Statistics and Probability, Health Informatics and Health, having authored 20 papers that have together received 444 indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (10 papers), Statistical Methods and Inference (5 papers) and Statistical Methods and Bayesian Inference (4 papers). The work is most often cited by research in Health Informatics (119 citations), Statistics and Probability (75 citations) and Health Information Management (28 citations). Rachael V. Phillips has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Alan Hubbard, Ivana Malenica, Romain Pirracchio, Andrew Bishara, Leo Anthony Celi, Jean Feng, Hana Lee, Susan Gruber, Mark J. van der Laan and Martyn T. Smith. Their work appears in journals such as American Journal of Epidemiology, Environment International and International Journal of Epidemiology.
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.