Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Foundation models for generalist medical artificial intelligence
2023738 citationsMichael Moor, Oishi Banerjee et al.Natureprofile →
Early prediction of circulatory failure in the intensive care unit using machine learning
2020233 citationsStephanie L. Hyland, Martin Faltys et al.Nature Medicineprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Michael Moor'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 Michael Moor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Moor more than expected).
This network shows the impact of papers produced by Michael Moor. 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 Michael Moor. The network helps show where Michael Moor may publish in the future.
Co-authorship network of co-authors of Michael Moor
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Moor.
A scholar is included among the top collaborators of Michael Moor 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 Michael Moor. Michael Moor is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hyland, Stephanie L., Martin Faltys, Matthias Hüser, et al.. (2020). Early prediction of circulatory failure in the intensive care unit using machine learning. Nature Medicine. 26(3). 364–373.233 indexed citations breakdown →
13.
Horn, Max, Michael Moor, Christian Bock, Bastian Rieck, & Karsten Borgwardt. (2020). Set Functions for Time Series. Repository for Publications and Research Data (ETH Zurich). 119. 4353–4363.2 indexed citations
14.
Rieck, Bastian, Matteo Togninalli, Christian Bock, et al.. (2019). Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology. arXiv (Cornell University).10 indexed citations
15.
Moor, Michael, Max Horn, Bastian Rieck, Damian Roqueiro, & Karsten Borgwardt. (2019). Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis.. arXiv (Cornell University).13 indexed citations
16.
Moor, Michael, Max Horn, Bastian Rieck, Damian Roqueiro, & Karsten Borgwardt. (2019). Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping. 106. 2–26.3 indexed citations
17.
Hugo‐Hamman, Christopher, et al.. (1991). Paediatric heart transplants--should we do them?. PubMed. 80(9). 434–6.2 indexed citations
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.