Michiel Schinkel

9.4k total citations
31 papers, 800 citations indexed

About

Michiel Schinkel is a scholar working on Epidemiology, Health Informatics and Artificial Intelligence. According to data from OpenAlex, Michiel Schinkel has authored 31 papers receiving a total of 800 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Epidemiology, 7 papers in Health Informatics and 7 papers in Artificial Intelligence. Recurrent topics in Michiel Schinkel's work include Sepsis Diagnosis and Treatment (16 papers), Artificial Intelligence in Healthcare and Education (7 papers) and Bacterial Identification and Susceptibility Testing (6 papers). Michiel Schinkel is often cited by papers focused on Sepsis Diagnosis and Treatment (16 papers), Artificial Intelligence in Healthcare and Education (7 papers) and Bacterial Identification and Susceptibility Testing (6 papers). Michiel Schinkel collaborates with scholars based in Netherlands, United States and Singapore. Michiel Schinkel's co-authors include Prabath W.B. Nanayakkara, Ketan Paranjape, Rishi Panday, Josip Car, W. Joost Wiersinga, Bo Schouten, Tom van der Poll, Mark H.H. Kramer, Richard Hammer and Paul Elbers and has published in prestigious journals such as American Journal of Respiratory and Critical Care Medicine, Scientific Reports and CHEST Journal.

In The Last Decade

Michiel Schinkel

30 papers receiving 776 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michiel Schinkel Netherlands 13 365 216 198 187 106 31 800
Rishi Panday Netherlands 12 348 1.0× 267 1.2× 172 0.9× 151 0.8× 109 1.0× 29 873
Erkin Ötleş United States 10 289 0.8× 225 1.0× 137 0.7× 242 1.3× 95 0.9× 19 770
Samson Mataraso United States 10 109 0.3× 218 1.0× 149 0.8× 219 1.2× 45 0.4× 22 559
Michael Draugelis United States 8 142 0.4× 261 1.2× 102 0.5× 280 1.5× 55 0.5× 15 839
Katharine E. Henry United States 8 189 0.5× 354 1.6× 74 0.4× 448 2.4× 108 1.0× 14 826
Toshihiko Takada Japan 16 164 0.4× 138 0.6× 114 0.6× 170 0.9× 40 0.4× 74 924
Michael Gao United States 13 254 0.7× 183 0.8× 119 0.6× 259 1.4× 30 0.3× 39 886
David Shimabukuro United States 11 129 0.4× 593 2.7× 106 0.5× 491 2.6× 139 1.3× 12 1.1k
Suresh Balu United States 16 446 1.2× 204 0.9× 158 0.8× 374 2.0× 48 0.5× 54 1.1k
Emily Pellegrini United States 13 88 0.2× 243 1.1× 164 0.8× 207 1.1× 32 0.3× 25 611

Countries citing papers authored by Michiel Schinkel

Since Specialization
Citations

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

Fields of papers citing papers by Michiel Schinkel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michiel Schinkel. 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 Michiel Schinkel. The network helps show where Michiel Schinkel may publish in the future.

Co-authorship network of co-authors of Michiel Schinkel

This figure shows the co-authorship network connecting the top 25 collaborators of Michiel Schinkel. A scholar is included among the top collaborators of Michiel Schinkel 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 Michiel Schinkel. Michiel Schinkel 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.
Schinkel, Michiel, Ketan Paranjape, Milan L. Ridderikhof, et al.. (2024). Appropriate use of blood cultures in the emergency department through machine learning (ABC): study protocol for a randomised controlled non-inferiority trial. BMJ Open. 14(5). e084053–e084053. 1 indexed citations
2.
Schinkel, Michiel, Karen C. Carroll, Sara E. Cosgrove, et al.. (2023). Impact of Blood Culture Contamination on Antibiotic Use, Resource Utilization, and Clinical Outcomes: A Retrospective Cohort Study in Dutch and US Hospitals. Open Forum Infectious Diseases. 11(2). ofad644–ofad644. 8 indexed citations
3.
Schinkel, Michiel, et al.. (2023). Artificial Intelligence: its Future and Impact on Acute Medicine. Acute Medicine Journal. 22(3). 150–153. 1 indexed citations
4.
Ende, Eva S. van den, Bo Schouten, Hanneke Merten, et al.. (2023). Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands. International Journal for Quality in Health Care. 35(2). 6 indexed citations
5.
Schinkel, Michiel, Tom van der Poll, & W. Joost Wiersinga. (2023). Artificial Intelligence for Early Sepsis Detection: A Word of Caution. American Journal of Respiratory and Critical Care Medicine. 207(7). 853–854. 15 indexed citations
6.
Schinkel, Michiel, et al.. (2023). Host Response Biomarkers for Sepsis in the Emergency Room. Critical Care. 27(1). 97–97. 18 indexed citations
7.
Schinkel, Michiel, et al.. (2023). Detecting changes in the performance of a clinical machine learning tool over time. EBioMedicine. 97. 104823–104823. 10 indexed citations
8.
Schinkel, Michiel, Eva S. van den Ende, Paul Elbers, et al.. (2022). Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study. BMJ Open. 12(1). e053332–e053332. 12 indexed citations
9.
Schinkel, Michiel, Frank C. Bennis, Hessel Peters‐Sengers, et al.. (2022). Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective evaluation of a machine learning prediction tool. EBioMedicine. 82. 104176–104176. 26 indexed citations
10.
Schinkel, Michiel, et al.. (2022). Comparing complaint-based triage scales and early warning scores for emergency department triage. Emergency Medicine Journal. 39(9). 691–696. 5 indexed citations
11.
Schuurman, Alex R., Tom D. Y. Reijnders, Tjitske S. R. van Engelen, et al.. (2022). The host response in different aetiologies of community-acquired pneumonia. EBioMedicine. 81. 104082–104082. 17 indexed citations
12.
Sikkens, Jonne J., Karlijn van der Straten, Melissa Oomen, et al.. (2022). Recent infection with HCoV-OC43 may be associated with protection against SARS-CoV-2 infection. iScience. 25(10). 105105–105105. 12 indexed citations
13.
Schuurman, Alex R., Tom D. Y. Reijnders, Anno Saris, et al.. (2021). Integrated single-cell analysis unveils diverging immune features of COVID-19, influenza, and other community-acquired pneumonia. eLife. 10. 10 indexed citations
14.
Schinkel, Michiel, et al.. (2021). Towards Understanding the Effective Use of Antibiotics for Sepsis. CHEST Journal. 160(4). 1211–1221. 9 indexed citations
15.
Veldhuis, Lars I., Milan L. Ridderikhof, Michiel Schinkel, et al.. (2021). Early warning scores to assess the probability of critical illness in patients with COVID-19. Emergency Medicine Journal. 38(12). 901–905. 15 indexed citations
16.
Schinkel, Michiel, Harjeet Singh Virk, Prabath W.B. Nanayakkara, Tom van der Poll, & W. Joost Wiersinga. (2020). What Sepsis Researchers Can Learn from COVID-19. American Journal of Respiratory and Critical Care Medicine. 203(1). 125–127. 6 indexed citations
17.
Schinkel, Michiel, Rishi Panday, W. Joost Wiersinga, & Prabath W.B. Nanayakkara. (2020). Timeliness of antibiotics for patients with sepsis and septic shock. Journal of Thoracic Disease. 12(S1). S66–S71. 15 indexed citations
18.
Rothrock, Steven G., et al.. (2020). Outcome of Immediate Versus Early Antibiotics in Severe Sepsis and Septic Shock: A Systematic Review and Meta-analysis. Annals of Emergency Medicine. 76(4). 427–441. 33 indexed citations
19.
Paranjape, Ketan, Michiel Schinkel, Rishi Panday, Josip Car, & Prabath W.B. Nanayakkara. (2019). Introducing Artificial Intelligence Training in Medical Education. JMIR Medical Education. 5(2). e16048–e16048. 342 indexed citations
20.
Schinkel, Michiel, et al.. (2019). Clinical applications of artificial intelligence in sepsis: A narrative review. Computers in Biology and Medicine. 115. 103488–103488. 83 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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