Lotty Hooft
Statistics and Probability · 2015–2025
A researcher in the Science corpus: 13 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 120 citations into it
- Artificial Intelligence — 52 citations into it
- Economics and Econometrics — 49 citations into it
- Epidemiology — 29 citations into it
- Cardiology and Cardiovascular Medicine — 27 citations into it
Whom Lotty Hooft trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Lotty Hooft's perspective
Ordered by how strongly the researchers Lotty Hooft trusts endorse each paper. Same corpus, a different order for every researcher.
- Key challenges for delivering clinical impact with artificial intelligence
- Sources of Variation and Bias in Studies of Diagnostic Accuracy (Annals of Internal Medicine)
- Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
- Interpretation of random effects meta-analyses (British medical journal)
- Prognosis and prognostic research: application and impact of prognostic models in clinical practice (British medical journal)
- Datasheets for datasets
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
- Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. (Radiology)
- National Institute for Health and Care Excellence. (European Heart Journal)
- The Ethics of AI Ethics: An Evaluation of Guidelines (Minds and Machines)
Their papers
- Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement (2018) — 2,772 citations
- STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies (2015) — 3,489 citations
- STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration (2016) — 2,218 citations
- 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) — 676 citations
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods (2025) — 435 citations
- Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) — 377 citations
- Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review (2021) — 338 citations
- Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist (2020) — 638 citations
- Antibody tests for identification of current and past infection with SARS‐CoV‐2 (2020) — 807 citations
- Rapid, point‐of‐care antigen and molecular‐based tests for diagnosis of SARS‐CoV‐2 infection (2020) — 1,048 citations
- A guide to systematic review and meta-analysis of prognostic factor studies (2019) — 656 citations
- Prediction models for cardiovascular disease risk in the general population: systematic review (2016) — 842 citations
- STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. (2015) — 654 citations