23 papers · ranked by Valyu relevance
Gary S Collins, Joris A de Groot, Susan Dutton, Omar Omar + 7 more
'Milensu Shanyinde' 'Abdelouahid Tajar' 'Merryn Voysey' 'Rose Wharton' 'Ly-Mee Yu' 'Karel G Moons' 'Douglas G Altman'] Background Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the…
Chandra Sripada, Mike Angstadt, Saige Rutherford, Aman Taxali
Test-retest reliability is critical for individual differences research. Thus, recent reports that found low test-retest reliability in fMRI have raised concern among researchers who aim to use brain imaging to predict psychologically- and clinically-important differences across people. These previous studies, however…
K. Larsen, R. Lukyanenko, Roland M. Mueller, V. Storey + 3 more
Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a…
Niels Smits, L. Andries van der Ark, Judith M. Conijn
Background Two important goals when using questionnaires are (a) measurement: the questionnaire is constructed to assign numerical values that accurately represent the test taker’s attribute, and (b) prediction: the questionnaire is constructed to give an accurate forecast of an external criterion. Construction methods…
Jin Li, Qin Zhang
Assessing the accuracy of predictive models is critical because predictive models have been increasingly used across various disciplines and predictive accuracy determines the quality of resultant predictions. Pearson product-moment correlation coefficient (r) and the coefficient of determination (r2) are among the…
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…
Dario Boschiero, Andrea Gallotta, Francesca Ferrari, Konstantina Dragoumani + 3 more
Purpose/objective The field of predictive medicine focuses on assessing disease risk and implementing preventive strategies with a view to either preventing disease onset entirely or significantly minimizing its impact on affected individuals. An emerging subfield, predictive health, extends this approach by targeting…
Mohamed Khalifa, Farah Magrabi, Blanca Gallego
Background: When selecting predictive tools, for implementation in their clinical practice or for recommendation in clinical guidelines, clinicians are challenged with an overwhelming and ever-growing number of tools. Many of these have never been implemented or evaluated for comparative effectiveness. To overcome this…
Bertrand Clarke, Yuling Yao
This paper reviews the growing field of Bayesian prediction. Bayes point and interval prediction are defined and exemplified and situated in statistical prediction more generally. Then, four general approaches to Bayes prediction are defined and we turn to predictor selection. This can be done predictively or…
Owen L. Petchey, Mikael Pontarp, Thomas M. Massie, Sonia Kéfi + 14 more
Forecasts of how ecological systems respond to environmental change are increasingly important. Sufficiently inaccurate forecasts will be of little use, however. For example, weather forecasts are for about one week into the future; after that they are too unreliable to be useful (i.e., the forecast horizon is about…
Danilo Bzdok, Denis Engemann, Olivier Grisel, Gaël Varoquaux + 1 more
In the 20^th^ century many advances in biological knowledge and evidence-based medicine were supported by p-values and accompanying methods. In the beginning 21^st^ century, ambitions towards precision medicine put a premium on detailed predictions for single individuals. The shift causes tension between traditional…
William Borrelli, Joshua Schrier
Forward and retrosynthetic organic reaction prediction are challenging applications of artificial intelligence (AI) research in chemistry. IBM’s freely available RXN for Chemistry (https://rxn.res.ibm.com) treats reaction prediction as a translation problem, by using transformer-based machine learning models trained on…
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
Jong-Wook Ban, José Ignacio Emparanza, Iratxe Urreta, Amanda Burls + 1 more
Design shortcomings and insufficient descriptions of design characteristics were prevalent among validation studies of clinical prediction rule as presented in [pone.0145779.s005]. There were 53 (18.5%) validation studies meeting 0 or 1 design characteristic and 161 (56.1%) validation studies satisfying 2 or 3 design…
Amanda M. O’Brien, Toni A. May, Kristin L. K. Koskey, Lindsay Bungert + 9 more
'Lindsay Bungert' 'Annie Cardinaux' 'Jonathan Cannon' 'Isaac N. Treves' 'Anila M. D’Mello' 'Robert M. Joseph' 'Cindy Li' 'Sidney Diamond' 'John D. E. Gabrieli' 'Pawan Sinha'] Purpose Predictions are complex, multisensory, and dynamic processes involving real-time adjustments based on environmental inputs. Disruptions…
Anshul Kumar, Taylor DiJohnson, Roger Edwards, Lisa Walker
Purpose: When a learner fails to reach a milestone, educators often wonder if there had been any warning signs that could have allowed them to intervene sooner. Machine learning can predict which students are at risk of failing a high-stakes certification exam. If predictions can be made well in advance of the exam…
Nikolaus Kriegeskorte
Crossvalidation is a method for estimating predictive performance and adjudicating between multiple models. On each of k folds of the process, k-1 of k independent subsets of the data (training set) are used to fit the parameters of each model and the left-out subset (test set) is used to estimate predictive…
Jacques Balayla
The accuracy of binary classification systems is defined as the proportion of correct predictions - both positive and negative - made by a classification model or computational algorithm. A value between 0 (no accuracy) and 1 (perfect accuracy), the accuracy of a classification model is dependent on several factors…
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…
Martin Shepperd, Steve MacDonell
Context: Software engineering has a problem in that when we empirically evaluate competing prediction systems we obtain conflicting results. Objective: To reduce the inconsistency amongst validation study results and provide a more formal foundation to interpret results with a particular focus on continuous prediction…
Authors not listed
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) are two important targets in current drug discovery, mainly due to the COVID-19 pandemic and the MERS-CoV outbreaks in recent years. An important target of both SARS-CoV-2 and MERS-CoV is the main…
Authors not listed
Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
Authors not listed
Meteorological normalization is a key concept in studying anthropogenic effects on air pollutant concentrations and its temporal trends. While apparently successful in revealing anthropogenic effects and often used, there are downsides to the methods and limitations which should be taken into account when using it.…