21 papers · ranked by Valyu relevance
Gary S Collins, Paula Dhiman, Jie Ma, Michael M Schlussel + 9 more
Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and…
Richard D Riley, Lucinda Archer, Kym I E Snell, Joie Ensor + 4 more
External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to…
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…
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…
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…
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…
Patrick Rockenschaub, Ela Marie Akay, Benjamin Gregory Carlisle, Adam Hilbert + 5 more
'Adam Hilbert' 'Joshua Wendland' 'Falk Meyer-Eschenbach' 'Anatol-Fiete Näher' 'Dietmar Frey' 'Vince Istvan Madai'] Background Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit…
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…
Maartje BELT, Katrijn SMULDERS, B Willem SCHREURS, Gerjon HANNINK
Several prediction models have been developed for hip and knee arthroplasty, aiming to predict the probability of an outcome after surgery . These predicted probabilities can provide valuable information to patients and clinicians as an aid in clinical decision-making and expectation management. However, existing…
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…
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…
Hirotaka Suetake, Tsukasa Fukusato, Takeo Igarashi, Tazro Ohta
Reproducibility of data analysis workflow is a key issue in the field of bioinformatics. Recent computing technologies, such as virtualization, have made it possible to reproduce workflow execution with ease. However, the reproducibility of results is not well discussed; that is, there is no standard way to verify…
David Higgins, Christian Johner
The introduction of artificial intelligence / machine learning (AI/ML) products to the regulated fields of pharmaceutical research and development (R&D) and drug manufacture, and medical devices (MD) and in-vitro diagnostics (IVD), poses new regulatory problems: a lack of a common terminology and understanding leads to…
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…