Patrick Thoral

9.6k total citations · 1 hit paper
37 papers, 966 citations indexed

About

Patrick Thoral is a scholar working on Epidemiology, Artificial Intelligence and Surgery. According to data from OpenAlex, Patrick Thoral has authored 37 papers receiving a total of 966 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Epidemiology, 14 papers in Artificial Intelligence and 10 papers in Surgery. Recurrent topics in Patrick Thoral's work include Sepsis Diagnosis and Treatment (24 papers), Machine Learning in Healthcare (13 papers) and Hemodynamic Monitoring and Therapy (8 papers). Patrick Thoral is often cited by papers focused on Sepsis Diagnosis and Treatment (24 papers), Machine Learning in Healthcare (13 papers) and Hemodynamic Monitoring and Therapy (8 papers). Patrick Thoral collaborates with scholars based in Netherlands, United States and United Kingdom. Patrick Thoral's co-authors include Paul Elbers, Armand R. J. Girbes, Lucas M. Fleuren, Ari Ercole, Luca F. Roggeveen, Tingjie Guo, Mark Hoogendoorn, Eleonora L. Swart, Thomas Klausch and Linda Schoonmade and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Antimicrobial Agents and Chemotherapy.

In The Last Decade

Patrick Thoral

33 papers receiving 950 citations

Hit Papers

Machine learning for the prediction of sepsis: a systemat... 2020 2026 2022 2024 2020 100 200 300 400
What are hit papers?

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.

  1. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
    2020 414 citations Lucas M. Fleuren, Thomas Klausch et al. Intensive Care Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Patrick Thoral Netherlands 15 447 351 167 134 125 37 966
Lucas M. Fleuren Netherlands 11 345 0.8× 217 0.6× 91 0.5× 78 0.6× 81 0.6× 22 708
Cara O’Brien United States 14 538 1.2× 233 0.7× 162 1.0× 143 1.1× 48 0.4× 20 1.2k
Tingjie Guo Netherlands 11 329 0.7× 190 0.5× 66 0.4× 86 0.6× 67 0.5× 24 682
Gabriel Wardi United States 17 370 0.8× 184 0.5× 81 0.5× 146 1.1× 57 0.5× 75 779
Luca F. Roggeveen Netherlands 9 309 0.7× 192 0.5× 72 0.4× 74 0.6× 66 0.5× 14 612
Lucinda Archer United Kingdom 13 198 0.4× 195 0.6× 91 0.5× 274 2.0× 94 0.8× 34 1.2k
Sicheng Hao United States 6 462 1.0× 297 0.8× 98 0.6× 194 1.4× 69 0.6× 14 1.2k
Lucas Bulgarelli United States 10 485 1.1× 336 1.0× 114 0.7× 203 1.5× 82 0.7× 22 1.3k
Ayad Shammout United States 2 443 1.0× 284 0.8× 94 0.6× 188 1.4× 60 0.5× 2 1.1k
David Shimabukuro United States 11 593 1.3× 491 1.4× 129 0.8× 150 1.1× 106 0.8× 12 1.1k

Countries citing papers authored by Patrick Thoral

Since Specialization
Citations

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

Fields of papers citing papers by Patrick Thoral

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Patrick Thoral

This figure shows the co-authorship network connecting the top 25 collaborators of Patrick Thoral. A scholar is included among the top collaborators of Patrick Thoral 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 Patrick Thoral. Patrick Thoral 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.
Struja, Tristan, Christopher Martin Sauer, Martine Otten, et al.. (2025). Sharing is caring: A systematic review of publicly available intensive care data sets. Journal of Critical Care. 90. 155205–155205.
2.
Bruin, Daniël M. de, et al.. (2025). ICU readmission and mortality risk prediction: Generalizability of a multi-hospital model. PubMed. 5(4). 377–384.
3.
Dongelmans, Dave A., Sylvia Brinkman, Patrick Thoral, et al.. (2024). Comparative performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data. International Journal of Medical Informatics. 188. 105477–105477. 1 indexed citations
4.
Williams, Christopher Y. K., et al.. (2024). Application of the Sepsis-3 criteria to describe sepsis epidemiology in the Amsterdam UMCdb intensive care dataset. PLoS ONE. 19(6). e0304133–e0304133. 1 indexed citations
5.
Fleuren, Lucas M., Armand R. J. Girbes, Mark Hoogendoorn, et al.. (2023). Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis. Journal of Intensive Care Medicine. 38(7). 612–629. 8 indexed citations
6.
Laxar, Daniel, Mathias Maleczek, Sebastian Zeiner, et al.. (2023). Development of a Reinforcement Learning Algorithm to Optimize Corticosteroid Therapy in Critically Ill Patients with Sepsis. Journal of Clinical Medicine. 12(4). 1513–1513. 13 indexed citations
7.
Yarnell, Christopher J., Federico Angriman, Bruno L. Ferreyro, et al.. (2023). Oxygenation thresholds for invasive ventilation in hypoxemic respiratory failure: a target trial emulation in two cohorts. Critical Care. 27(1). 67–67. 11 indexed citations
8.
Hawchar, Fatime, Alessandra Angelucci, Ari Ercole, et al.. (2023). Diagnosing acute kidney injury ahead of time in critically ill septic patients using kinetic estimated glomerular filtration rate. Journal of Critical Care. 75. 154276–154276. 1 indexed citations
9.
Liu, Xiaoli, Pan Hu, Zhongheng Zhang, et al.. (2023). Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation. The Lancet Digital Health. 5(10). e657–e667. 25 indexed citations
10.
Hoz, Miguel Ángel Armengol de la, Patrick Thoral, Paul Elbers, et al.. (2022). A novel Vascular Leak Index identifies sepsis patients with a higher risk for in-hospital death and fluid accumulation. Critical Care. 26(1). 103–103. 7 indexed citations
11.
12.
Hond, Anne de, Ilse Kant, Paul Elbers, et al.. (2022). Predicting Readmission or Death After Discharge From the ICU: External Validation and Retraining of a Machine Learning Model. Critical Care Medicine. 51(2). 291–300. 26 indexed citations
13.
Plagwitz, Lucas, Patrick Thoral, Harm‐Jan de Grooth, et al.. (2022). Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database. Studies in health technology and informatics. 294. 139–140. 2 indexed citations
14.
Thoral, Patrick, et al.. (2021). The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge. Value in Health. 25(3). 359–367. 19 indexed citations
15.
16.
Fleuren, Lucas M., et al.. (2020). Machine learning in intensive care medicine: ready for take-off?. Intensive Care Medicine. 46(7). 1486–1488. 47 indexed citations
17.
Roggeveen, Luca F., Tingjie Guo, Ronald H. Driessen, et al.. (2020). Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients. Frontiers in Pharmacology. 11. 646–646. 21 indexed citations
18.
Fleuren, Lucas M., Thomas Klausch, Charlotte Zwager, et al.. (2020). Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Medicine. 46(3). 383–400. 414 indexed citations breakdown →
19.
Stapel, Sandra N., et al.. (2019). Amino Acid Loss during Continuous Venovenous Hemofiltration in Critically Ill Patients. Blood Purification. 48(4). 321–329. 13 indexed citations
20.
Hartemink, Koen J., et al.. (2014). Pneumomediastinum and (bilateral) pneumothorax after high energy trauma: Indications for emergency bronchoscopy. Respiratory Medicine Case Reports. 13. 9–11. 1 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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