24 papers · ranked by Valyu relevance
Mohammad Azizmalayeri, Ameen Abu-Hanna, Saskia Houterman, Marije M. Vis + 1 more
Background: External validation is essential for assessing the transportability of predictive models. However, its interpretation is often confounded by differences between external and development populations. This study introduces a framework to distinguish model deficiencies from case-mix effects. Method: We propose…
Matthew Sperrin, Richard D. Riley, Gary S. Collins, Glen P. Martin
Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common for validation studies to take place with arbitrary datasets, chosen for convenience rather than relevance. We…
M. A. E. Binuya, E. G. Engelhardt, W. Schats, M. K. Schmidt + 1 more
'E. W. Steyerberg'] Background Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of…
Matthew Rosenblatt, Link Tejavibulya, Chris C. Camp, Rongtao Jiang + 3 more
Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the…
Alex Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu + 2 more
'David A. Clifton' 'Nigam H. Shah'] Stanford Bioengineering Department, Stanford University, Stanford, CA, USA Department of Engineering Science, University of Oxford, Oxford, UK Duke University School of Medicine, Durham, NC, USA Oxford-Suzhou Centre for Advanced Research, Suzhou, China Center for Biomedical…
Linda Nevin
An ideal scenario for development and validation of prediction models, best suited to multisite studies, is one in which, first, data from the development sample are partitioned non-randomly-e.g., by site, department, geography, or time-and each subset is held out in turn to test the performance of models developed on…
Giuseppe Gallitto, Robert Englert, Balint Kincses, Raviteja Kotikalapudi + 4 more
Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data pre-processing and feature engineering pipelines can…
Jong-Wook Ban, Lucy Abel, Richard Stevens, Rafael Perera + 1 more
External validation is a stage of clinical prediction rule (CPR) development where a CPR derived in a population is evaluated in different populations with a comparable health condition to the health condition of the derivation population (e.g., in another country) . An external validation study can generate evidence…
Mohsen Sadatsafavi, Tae Yoon Lee, Laure Wynants, Andrew J. Vickers + 1 more
'Paul Gustafson'] - 1. Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada - 2. Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands - 3. Department of…
Ulrich Dirnagl, Alexandra Bannach‐Brown, Sarah McCann
A spectre is haunting biomedical research: It appears that a substantial fraction of published research results cannot be reproduced, while spectacularly successful novel treatments developed in experimental models of disease too often fail in clinical trials. A reproducibility crisis has been proclaimed, and…
Tieu-Long Phan, Hoang-Son Lai Le, Gia-Bao Truong, The-Chuong Trinh + 4 more
HIV-1 (Human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host. Left untreated, it can lead to AIDS (acquired immunodeficiency syndrome), where death is inevitable due to opportunistic diseases. Therefore, discovering new antiviral drugs against HIV-1 is crucial.…
Zhina Mohamadi, Erfan Abtahi, Zahra sadat Shayegh, Mehrafrin Ataei Kachouei + 8 more
Cancer is a major source of mortality and morbidity all over the world that has caused more than 19 million new cases and nearly 10 million deaths in 2020. Although there are so many advances in cancer diagnosis, previous methods such as imaging and serum biomarkers more often lack the necessary sensitivity and…
Zexiang Li, Donna Spiegelman, Molin Wang, Zuoheng Wang + 1 more
In epidemiology, obtaining accurate individual exposure measurements can be costly and challenging. Thus, these measurements are often subject to error. Regression calibration with a validation study is widely employed as a study design and analysis method to correct for measurement error in the main study due to its…
Lucas Gren
Most reviewers and authors in the behavioral soware engineering research eld, base their concept of validity on two publications, namely Wohlin et al. [20] and Runeson and Host ¨ [19]. While I highly appreciate the authors' work in these publications, I am afraid the four categories of validity threats promoted for…
David A. Cook, Rose Hatala
Background Simulation plays a vital role in health professions assessment. This review provides a primer on assessment validation for educators and education researchers. We focus on simulation-based assessment of health professionals, but the principles apply broadly to other assessment approaches and topics. Key…
Roy Eagleson, Leo Joskowicz, Nassir Navab, Philipp Fürnstahl + 3 more
'Mazda Farshad' 'Hooman Esfandiari' 'Matthias Seibold'] This paper presents a discussion about the fundamental principles of Analysis of Augmented and Virtual Reality (AR/VR) Systems for Medical Imaging and Computer-Assisted Interventions. The three key concepts of Analysis (Verification, Evaluation, and Validation)…
Brittany Haas, Melissa Hardy, Shree Sowndarya S. V., Keir Adams + 3 more
Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering…
Yanlin Zhang, Jing Zhao
We formalize scientific methodology—the end-to-end process from question formulation to evidence-grounded writing—as a phase-gated research protocol with explicit return paths and persistent constraints, and instantiate it for general-purpose language models as executable protocol specifications. The formalization…
Eric W. Deutsch, Roger Kramer, Joseph Ames, Andrew Bauman + 21 more
Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities.…
Richard C. Gerkin, Justas Birgiolas, Russell J. Jarvis, Cyrus Omar + 1 more
Validating a quantitative scientific model requires comparing its predictions against many experimental observations, ideally from many labs, using transparent, robust, statistical comparisons. Unfortunately, in rapidly-growing fields like neuroscience, this is becoming increasingly untenable, even for the most…
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Authors not listed
Fullerenes, carbon-based nanomaterials with sp2-hybridized carbon atoms arranged in polyhedral cages, exhibit diverse isomeric structures with promising applications in optoelectronics, solar cells, and medicine. However, the vast number of possible fullerene isomers complicates efficient property prediction. In this…
Authors not listed
Computational toxicology plays a pivotal role in modern drug discovery and environmental risk assessment; however, the reliability of predictive models on unseen chemical scaffolds remains a critical bottleneck. Deep learning architectures, despite their prevalence, are susceptible to ’silent failures’—yielding…