Jan‐Oliver Neumann

1.0k total citations
40 papers, 525 citations indexed

About

Jan‐Oliver Neumann is a scholar working on Neurology, Surgery and Epidemiology. According to data from OpenAlex, Jan‐Oliver Neumann has authored 40 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Neurology, 13 papers in Surgery and 9 papers in Epidemiology. Recurrent topics in Jan‐Oliver Neumann's work include Traumatic Brain Injury and Neurovascular Disturbances (11 papers), Glioma Diagnosis and Treatment (8 papers) and Spine and Intervertebral Disc Pathology (6 papers). Jan‐Oliver Neumann is often cited by papers focused on Traumatic Brain Injury and Neurovascular Disturbances (11 papers), Glioma Diagnosis and Treatment (8 papers) and Spine and Intervertebral Disc Pathology (6 papers). Jan‐Oliver Neumann collaborates with scholars based in Germany, Lithuania and United Kingdom. Jan‐Oliver Neumann's co-authors include Andreas Unterberg, Jorg Thöming, J.F. Fernández, Karl Kiening, Julian Bösel, Werner Hacke, Hemasse Amiri, Thorsten Steiner, Petra Schiller and Silvia Schönenberger and has published in prestigious journals such as PLoS ONE, Stroke and Journal of neurosurgery.

In The Last Decade

Jan‐Oliver Neumann

35 papers receiving 511 citations

Peers

Jan‐Oliver Neumann
Malini Madhavan United States
Yongfang Zhou China
Thomas Kleemann Germany
Mandeep Singh India
Sergio Richter Germany
E. Sacristán Mexico
Helmut Hager Austria
Timothy Y. Wang United States
Nima Hatam Germany
Malini Madhavan United States
Jan‐Oliver Neumann
Citations per year, relative to Jan‐Oliver Neumann Jan‐Oliver Neumann (= 1×) peers Malini Madhavan

Countries citing papers authored by Jan‐Oliver Neumann

Since Specialization
Citations

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

Fields of papers citing papers by Jan‐Oliver Neumann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jan‐Oliver Neumann. 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 Jan‐Oliver Neumann. The network helps show where Jan‐Oliver Neumann may publish in the future.

Co-authorship network of co-authors of Jan‐Oliver Neumann

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

All Works

20 of 20 papers shown
1.
Mäurer, M., Kianush Karimian‐Jazi, Martin Jakobs, et al.. (2025). LINNDA: Lymphoma identification through neural network detection aid. iScience. 28(12). 114153–114153.
2.
Mäurer, M., Martin Jakobs, Kianush Karimian‐Jazi, et al.. (2025). u-LINNDA: A protocol for user-optimized lymphoma identification through neural network detection aid. STAR Protocols. 6(4). 104176–104176. 1 indexed citations
3.
Neumann, Jan‐Oliver, et al.. (2024). Routine ICU Surveillance after Brain Tumor Surgery: Patient Selection Using Machine Learning. Journal of Clinical Medicine. 13(19). 5747–5747. 1 indexed citations
4.
Nikolić, Marina, Christoph Eisner, Jan‐Oliver Neumann, et al.. (2024). Right-to-left-shunts in patients scheduled for neurosurgical intervention in semi-sitting position – a literature review based on two case scenarios. BMC Anesthesiology. 24(1). 375–375.
5.
Mäurer, M., Kianush Karimian‐Jazi, Martin Jakobs, et al.. (2024). Deep learning aided preoperative diagnosis of primary central nervous system lymphoma. iScience. 27(2). 109023–109023. 4 indexed citations
6.
Zivkovic, Aleksandar R., et al.. (2024). Feasibility of Fluid Responsiveness Assessment in Patients at Risk for Increased Intracranial Pressure. Journal of Clinical Medicine. 13(6). 1786–1786. 1 indexed citations
7.
8.
Neumann, Jan‐Oliver, et al.. (2023). Routine ICU admission after brain tumor surgery: retrospective validation and critical appraisal of two prediction scores. Acta Neurochirurgica. 165(6). 1655–1664. 3 indexed citations
9.
Neumann, Jan‐Oliver, Moritz Scherer, Basem Ishak, et al.. (2023). Spinal oncologic paraparesis: Analysis of neurological and surgical outcomes in patients with intramedullary, extramedullary, and extradural tumors. Frontiers in Oncology. 12. 1003084–1003084. 5 indexed citations
10.
Scherer, Moritz, Jan‐Oliver Neumann, Basem Ishak, et al.. (2023). Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches. Journal of Clinical Medicine. 12(7). 2646–2646. 4 indexed citations
11.
Piper, Ian, Barbara Gregson, Per Enblad, et al.. (2023). Decompressive craniectomy as a second/third-tier intervention in traumatic brain injury: A multicenter observational study. Injury. 54(9). 110911–110911.
12.
Walter, Johannes, Silvia Schönenberger, Peter A. Ringleb, et al.. (2023). Prior Thrombectomy Does Not Affect the Surgical Complication Rate of Decompressive Hemicraniectomy in Patients with Malignant Ischemic Stroke. Neurocritical Care. 40(2). 698–706. 2 indexed citations
13.
Lenga, Pavlina, Daniel Kühlwein, Silvia Schönenberger, et al.. (2023). The use of quantitative pupillometry in brain death determination: preliminary findings. Neurological Sciences. 45(5). 2165–2170. 2 indexed citations
14.
Neumann, Jan‐Oliver, Gerhard Dyckhoff, Moritz Kronlage, et al.. (2023). Anterior Access to the Cervicothoracic Junction via Partial Sternotomy: A Clinical Series Reporting on Technical Feasibility, Postoperative Morbidity, and Early Surgical Outcome. Journal of Clinical Medicine. 12(12). 4107–4107. 1 indexed citations
15.
Vosylius, Saulius, Saulius Ročka, Daiva Rastenytė, et al.. (2019). Optimal Cerebral Perfusion Pressure: Targeted Treatment for Severe Traumatic Brain Injury. Journal of Neurotrauma. 37(2). 389–396. 35 indexed citations
16.
Neumann, Jan‐Oliver, Benito Campos, Martin Jakobs, et al.. (2018). Frame-based stereotactic biopsies using an intraoperative MR-scanner are as safe and effective as conventional stereotactic procedures. PLoS ONE. 13(10). e0205772–e0205772. 14 indexed citations
17.
Jakobs, Martin, Manja Kloß, Jan‐Oliver Neumann, et al.. (2018). Intraoperative Stereotactic Magnetic Resonance Imaging for Deep Brain Stimulation Electrode Planning in Patients with Movement Disorders. World Neurosurgery. 119. e801–e808. 13 indexed citations
18.
Donald, Rob, Tim Howells, Ian Piper, et al.. (2018). Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care. Journal of Clinical Monitoring and Computing. 33(1). 39–51. 20 indexed citations
19.
Neumann, Jan‐Oliver, et al.. (2015). [Perspective of patients undergoing surgery for secondary peritonitis].. PubMed. 94(5). 199–203. 2 indexed citations
20.
Neumann, Jan‐Oliver, L. Fischer, T. Heimann, et al.. (2006). Branching Patterns and Drainage Territories of the Middle Hepatic Vein in Computer-Simulated Right Living-Donor Hepatectomies. American Journal of Transplantation. 6(6). 1407–1415. 28 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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