David P. vanMaanen

1.0k total citations
8 papers, 74 citations indexed

About

David P. vanMaanen is a scholar working on Cellular and Molecular Neuroscience, Cardiology and Cardiovascular Medicine and Cognitive Neuroscience. According to data from OpenAlex, David P. vanMaanen has authored 8 papers receiving a total of 74 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Cellular and Molecular Neuroscience, 2 papers in Cardiology and Cardiovascular Medicine and 2 papers in Cognitive Neuroscience. Recurrent topics in David P. vanMaanen's work include Neural dynamics and brain function (2 papers), Machine Learning in Healthcare (2 papers) and Neuroscience and Neuropharmacology Research (2 papers). David P. vanMaanen is often cited by papers focused on Neural dynamics and brain function (2 papers), Machine Learning in Healthcare (2 papers) and Neuroscience and Neuropharmacology Research (2 papers). David P. vanMaanen collaborates with scholars based in United States. David P. vanMaanen's co-authors include Peter J. Siekmeier, John M. Pfeifer, Dustin N. Hartzel, Brandon K. Fornwalt, H. Lester Kirchner, Jonathan D Suever, Marios S. Pattichis, Alvaro Ulloa, Joseph B. Leader and Linyuan Jing and has published in prestigious journals such as Circulation, PLoS ONE and Neuropsychopharmacology.

In The Last Decade

David P. vanMaanen

6 papers receiving 74 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David P. vanMaanen United States 4 30 29 18 13 10 8 74
Robert Faist United States 5 15 0.5× 19 0.7× 30 1.7× 9 0.7× 17 1.7× 6 151
Dominik Krzemiński United Kingdom 5 16 0.5× 6 0.2× 84 4.7× 17 1.3× 9 0.9× 7 105
James Liley United Kingdom 4 10 0.3× 14 0.5× 3 0.2× 3 0.2× 30 3.0× 10 122
Ioanna Skampardoni United States 6 9 0.3× 16 0.6× 21 1.2× 26 2.6× 7 126
Sigang Yu China 6 6 0.2× 19 0.7× 35 1.9× 3 0.2× 3 0.3× 12 79
Naveen Gadapa United Kingdom 4 5 0.2× 36 1.2× 3 0.2× 9 0.7× 9 0.9× 6 185
José Alonso Solís-Lemus United Kingdom 6 101 3.4× 29 1.0× 3 0.2× 6 0.5× 9 0.9× 22 144
D. Y. Wu United States 3 10 0.3× 11 0.4× 8 0.4× 5 0.4× 1 0.1× 7 37
Mathijs S. van Schie Netherlands 10 218 7.3× 12 0.4× 7 0.4× 5 0.4× 19 1.9× 48 261
Yohan Davit France 2 4 0.1× 28 1.0× 6 0.3× 8 0.6× 10 1.0× 2 68

Countries citing papers authored by David P. vanMaanen

Since Specialization
Citations

This map shows the geographic impact of David P. vanMaanen'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 David P. vanMaanen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David P. vanMaanen more than expected).

Fields of papers citing papers by David P. vanMaanen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David P. vanMaanen. 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 David P. vanMaanen. The network helps show where David P. vanMaanen may publish in the future.

Co-authorship network of co-authors of David P. vanMaanen

This figure shows the co-authorship network connecting the top 25 collaborators of David P. vanMaanen. A scholar is included among the top collaborators of David P. vanMaanen 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 David P. vanMaanen. David P. vanMaanen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Ulloa, Alvaro, David P. vanMaanen, Linyuan Jing, et al.. (2025). A Large-scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Separable Risk Assessment. IEEE Journal of Biomedical and Health Informatics. 29(5). 3762–3771.
2.
Ulloa, Alvaro, Sushravya Raghunath, David P. vanMaanen, et al.. (2022). Abstract 11000: Deep Learning Prediction of New-Onset Atrial Fibrillation Using Echocardiography Videos. Circulation. 146(Suppl_1).
3.
Zhang, Xiaoyan, Alvaro Ulloa, Joshua V. Stough, et al.. (2022). Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset. The International Journal of Cardiovascular Imaging. 38(8). 1685–1697. 3 indexed citations
4.
Ulloa, Alvaro, Linyuan Jing, Christopher W. Good, et al.. (2021). Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality. Nature Biomedical Engineering. 5(6). 546–554. 45 indexed citations
5.
Raghunath, Sushravya, David P. vanMaanen, Joshua V. Stough, et al.. (2019). Abstract 14425: Deep Neural Networks Can Predict 1-Year Mortality Directly From ECG Signal, Even When Clinically Interpreted as Normal. Circulation. 2 indexed citations
6.
Ulloa, Alvaro, Christopher W. Good, David P. vanMaanen, et al.. (2018). A deep neural network predicts survival after heart imaging better than cardiologists.. 2 indexed citations
7.
Siekmeier, Peter J. & David P. vanMaanen. (2014). Dopaminergic Contributions to Hippocampal Pathophysiology in Schizophrenia: A Computational Study. Neuropsychopharmacology. 39(7). 1713–1721. 6 indexed citations
8.
Siekmeier, Peter J. & David P. vanMaanen. (2013). Development of Antipsychotic Medications with Novel Mechanisms of Action Based on Computational Modeling of Hippocampal Neuropathology. PLoS ONE. 8(3). e58607–e58607. 16 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.

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2026