Alejandro Schuler
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Statistics and Probability top 5%
- Advanced Causal Inference Techniques
- Statistical Methods and Inference
Papers in
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- Neural Networks and Applications 7
- Machine Learning in Healthcare 5
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- Sepsis Diagnosis and Treatment 5
- Co-authors
- Gabriel J. Escobar (10 shared papers)Nigam H. Shah (8 shared papers)Patricia Kipnis (2 shared papers)J Greene (2 shared papers)Kenneth Jung (3 shared papers)Brian L. Lawson (2 shared papers)Vincent X. Liu (6 shared papers)Josef A. Nossek (7 shared papers)
- Journals
- Statistics in Medicine (2 papers)The International Journal of Biostatistics (2 papers)Journal of the American Medical Informatics Association (1 paper)New England Journal of Medicine (1 paper)Journal of Biomedical Informatics (1 paper)
- Partner nations
- United StatesGermanyDenmark
In The Last Decade
Alejandro Schuler
29 papers receiving 860 citations
Peers
Comparison fields: 5 of 125
- Health Informatics 42
- Statistics and Probability 128
- Health Information Management 43
- Family Practice 15
- Critical Care and Intensive Care Medicine 33
Countries citing papers authored by Alejandro Schuler
This map shows the geographic impact of Alejandro Schuler'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 Alejandro Schuler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alejandro Schuler more than expected).
Fields of papers citing papers by Alejandro Schuler
This network shows the impact of papers produced by Alejandro Schuler. 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 Alejandro Schuler. The network helps show where Alejandro Schuler may publish in the future.
Co-authors
The 25 scholars most cited alongside Alejandro Schuler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 174 | |
| 2 | 2018 | 117 | |
| 3 | 2018 | 98 | |
| 4 | NGBoost: Natural Gradient Boosting for Probabilistic Prediction | 2020 | 72 |
| 5 | 2018 | 65 | |
| 6 | 1993 | 59 | |
| 7 | 2016 | 47 | |
| 8 | 2021 | 34 | |
| 9 | 2020 | 29 | |
| 10 | 2018 | 26 | |
| 11 | 2015 | 21 | |
| 12 | 2020 | 21 | |
| 13 | 2020 | 18 | |
| 14 | 2021 | 17 | |
| 15 | 2021 | 15 | |
| 16 | 2018 | 12 | |
| 17 | 2020 | 12 | |
| 18 | 2021 | 9 | |
| 19 | 2002 | 8 | |
| 20 | 2022 | 5 |
About Alejandro Schuler
Alejandro Schuler is a scholar working on Artificial Intelligence, Epidemiology, Computer Networks and Communications, Statistics and Probability and Cardiology and Cardiovascular Medicine, having authored 33 papers that have together received 884 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Advanced Causal Inference Techniques (6 papers), Neural Networks Stability and Synchronization (6 papers), Machine Learning in Healthcare (5 papers), Sepsis Diagnosis and Treatment (5 papers), Advanced Memory and Neural Computing (4 papers), Health Systems, Economic Evaluations, Quality of Life (3 papers) and Statistical Methods and Inference (3 papers). The work is most often cited by research in Health Informatics (42 citations), Statistics and Probability (128 citations), Health Information Management (43 citations), Family Practice (15 citations) and Critical Care and Intensive Care Medicine (33 citations). Alejandro Schuler has collaborated with scholars based in United States, Germany and Denmark. Frequent co-authors include Gabriel J. Escobar, Nigam H. Shah, Patricia Kipnis, J Greene, Kenneth Jung, Brian L. Lawson, Vincent X. Liu, Josef A. Nossek, Alison Callahan and Yun Lu. Their work appears in journals such as Statistics in Medicine, The International Journal of Biostatistics, Journal of the American Medical Informatics Association, New England Journal of Medicine and Journal of Biomedical Informatics.
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.