Matthieu Komorowski

5.8k citations
63 papers · 2.3k indexed · 2 hit papers · h-index 20

Matthieu Komorowski

58 papers receiving 2.2k citations

Hit Papers

Artificial intelligence versus clinicians: systematic rev...201820262020202320202018200400600

Peers

Matthieu Komorowski
Comparison fields: 5 of 157
  • Artificial Intelligence 694
  • Health Informatics 617
  • Epidemiology 470
  • Radiology, Nuclear Medicine and Imaging 431
  • Pulmonary and Respiratory Medicine 282
Replace Karandeep Singh with:
Karandeep Singh United States
Jenna Wiens United States
Mintu P. Turakhia United States
Mahiben Maruthappu United Kingdom
Johanna AAG Damen Netherlands
Ritankar Das United States
Jie Ma China
Jana Hoffman United States
Kym I E Snell United Kingdom
Genevieve B. Melton United States
Matthieu Komorowski relative to Karandeep Singh United States Karandeep Singh's profile →
Citations per field
00.5×1.7×
Karandeep Singh · 1×
Citations per year

Countries citing papers authored by Matthieu Komorowski

Since Specialization
Citations

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

Fields of papers citing papers by Matthieu Komorowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthieu Komorowski

This figure shows the co-authorship network connecting the top 25 collaborators of Matthieu Komorowski. A scholar is included among the top collaborators of Matthieu Komorowski 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 Matthieu Komorowski. Matthieu Komorowski 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
#WorkIndexed citations
1 0
2 1
3 0
4 1
5 1
6 6
7 7
8 1
9 2
10 24
11 4
12 85
13 2
14 21
15 45
16
The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive carebreakdown →
636
17 177
18 20
19 11
20
Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach.
6

About Matthieu Komorowski

Matthieu Komorowski is a scholar working on Health Informatics, Family Practice and Anesthesiology and Pain Medicine, having authored 63 papers that have together received 2.3k indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (17 papers), Spaceflight effects on biology (15 papers) and Machine Learning in Healthcare (13 papers). The work is most often cited by research in Health Informatics (617 citations), Family Practice (99 citations) and Health Information Management (156 citations). Matthieu Komorowski has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Anthony Gordon, A. Aldo Faisal, Leo Anthony Celi, Omar Badawi, Myura Nagendran, Mahiben Maruthappu, Christopher A. Lovejoy, Eric J. Topol, Gary S. Collins and Hugh Harvey. Their work appears in journals such as Nature Medicine, PLoS ONE and American Journal of Respiratory and Critical Care Medicine.

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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