World's Best Scientists 2026 revealed!
B. Van Calster

B. Van Calster

D-Index & Metrics

Medicine

D-Index
82
Citations
27816
World Ranking
15995
National Ranking
200

Overview

B. Van Calster is affiliated with KU Leuven in Belgium, where their research primarily focuses on medicine with an emphasis on artificial intelligence applications, statistics, obstetrics, gynecology, reproductive medicine, and surgery. Their work spans several interconnected fields, reflecting a multidisciplinary approach.

The scientist's recent publications illustrate a concentration on clinical prediction models and artificial intelligence in healthcare. Key papers include:

  • Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal, 2020, BMJ
  • TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, 2024, BMJ
  • Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence, 2021, BMJ Open
  • Interpreting area under the receiver operating characteristic curve, 2022, The Lancet Digital Health
  • Randomized Trial of Fetal Surgery for Severe Left Diaphragmatic Hernia, 2021, New England Journal of Medicine

B. Van Calster frequently collaborates with several coauthors, highlighting the interconnected nature of their research projects. Frequent collaborators include:

  • D. Timmerman
  • Laure Wynants
  • Gary S. Collins
  • Maarten van Smeden
  • T. Bourne

The scientist has a notable publication record in specific venues, indicating preferred platforms for disseminating research findings. These include:

  • Ultrasound in Obstetrics and Gynecology
  • Journal of Clinical Epidemiology
  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • BMJ

B. Van Calster's subfields reflect the diverse aspects of their research focus, covering:

  • Artificial Intelligence
  • Statistics and Probability
  • Obstetrics and Gynecology
  • Reproductive Medicine
  • Surgery

Their main research topics provide further detail on the specific themes explored within their work, including:

  • Machine Learning in Healthcare
  • Ovarian cancer diagnosis and treatment
  • Endometrial and Cervical Cancer Treatments
  • Artificial Intelligence in Healthcare and Education
  • Meta-analysis and systematic reviews
  • Sepsis Diagnosis and Treatment
  • Statistical Methods in Clinical Trials

Best Publications

  • A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

    Evangelia Christodoulou;Jie Ma;Gary S. Collins;Ewout W. Steyerberg

  • Calibration: the Achilles heel of predictive analytics

    Ben Van Calster;Ben Van Calster;David J. McLernon;Maarten van Smeden;Laure Wynants;Laure Wynants

  • Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests.

    Andrew J Vickers;Ben Van Calster;Ben Van Calster;Ewout W Steyerberg

  • Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators.

    Ben Van Calster;Ben Van Calster;Laure Wynants;Jan F.M. Verbeek;Jan Y. Verbakel;Jan Y. Verbakel

  • A simple, step-by-step guide to interpreting decision curve analysis.

    Andrew J. Vickers;Ben van Calster;Ben van Calster;Ewout W. Steyerberg

  • Simple ultrasound-based rules for the diagnosis of ovarian cancer

    D Timmerman;Antonia Carla Testa;T Bourne;L Ameye

  • A calibration hierarchy for risk models was defined: from utopia to empirical data.

    Ben Van Calster;Ben Van Calster;Daan Nieboer;Yvonne Vergouwe;Bavo De Cock

  • Logistic Regression Model to Distinguish Between the Benign and Malignant Adnexal Mass Before Surgery: A Multicenter Study by the International Ovarian Tumor Analysis Group

    Dirk Timmerman;Antonia Carla Testa;Tom Bourne;Enrico Ferrazzi

  • Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.

    Gary S Collins;Gary S Collins;Paula Dhiman;Paula Dhiman;Constanza L Andaur Navarro;Jie Ma

  • Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study.

    Ben Van Calster;Kirsten Van Hoorde;Lil Valentin;Antonia C Testa

  • Pregnancy of unknown location: a consensus statement of nomenclature, definitions, and outcome

    Kurt Barnhart;Norah M. van Mello;Tom Bourne;Tom Bourne;Emma Kirk

  • Long-term cognitive and cardiac outcomes after prenatal exposure to chemotherapy in children aged 18 months or older: an observational study

    Frédéric Amant;Kristel Van Calsteren;Michael J Halaska;Mina Mhallem Gziri

  • Prognosis of Women With Primary Breast Cancer Diagnosed During Pregnancy: Results From an International Collaborative Study

    Frédéric Amant;Gunter von Minckwitz;Sileny N. Han;Marijke Bontenbal

  • Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group

    Dirk Timmerman;Ben Van Calster;Antonia Testa;Luca Savelli

  • Calibration of Risk Prediction Models: Impact on Decision-Analytic Performance

    Ben Van Calster;Andrew J. Vickers

  • Treatment of breast cancer during pregnancy: an observational study

    Sibylle Loibl;Sileny N Han;Gunter von Minckwitz;Marijke Bontenbal

  • Oncological management and obstetric and neonatal outcomes for women diagnosed with cancer during pregnancy: a 20-year international cohort study of 1170 patients

    Jorine de Haan;Jorine de Haan;Magali Verheecke;Kristel Van Calsteren;Ben Van Calster

  • Endometriomas: their ultrasound characteristics

    C. Van Holsbeke;B. Van Calster;S. Guerriero;L. Savelli

  • Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis

    Jeroen Kaijser;Ahmad Sayasneh;Kirsten Van Hoorde;Sadaf Ghaem-Maghami

  • Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

    L Wynants;B Van Calster;Bonten Mmj.;G Collins

Frequent Co-Authors

Tom Bourne
Tom Bourne Imperial College London
Lil Valentin
Lil Valentin Lund University
Patrick Neven
Patrick Neven KU Leuven
Hans Wildiers
Hans Wildiers KU Leuven
Davor Jurkovic
Davor Jurkovic University College London
Thomas D'Hooghe
Thomas D'Hooghe Yale University
Giovanni Scambia
Giovanni Scambia Catholic University of the Sacred Heart

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