25 papers · ranked by Valyu relevance
Daniel S. Hain, Roman Jurowetzki
Recent years have seen a substantial development of quantitative methods, mostly led by the computer science community with the goal to develop better machine learning application, mainly focused on predictive modeling. However, economic, management, and technology forecasting research has up to now been hesitant to…
Giacomo Welsch, Peter Kowalczyk
Prediction-oriented machine learning is becoming increasingly valuable to organizations, as it may drive applications in crucial business areas. However, decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms. We ascribe…
Christian Lovis, Ghasem Ahmadi, Tjeerd van der Ploeg, Robbert Gobbens
Background Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods We performed a longitudinal study with…
Joel Martínez-Salazar, Filiberto Toledano-Toledano, Vesna Zadnik
Simple Summary Statistical predictive models using one of the most important strategies, known as results-based management (RBM), are relevant for improving the quality of medical services and could be used with cancer data statistics to monitor and evaluate children with cancer. We provided a comparative analysis of…
Alicja Wolny–Dominiak, Tomasz Żądło
strategy Authors: ['Alicja Wolny–Dominiak' 'Tomasz Żądło'] This paper addresses the topic of choosing a prediction strategy when using parametric or nonparametric regression models. It emphasizes the importance of ex ante prediction accuracy, ensemble approaches, and forecasting not only the values of the dependent…
Michael Brimacombe, Kwang Woo Ahn
Data flow-based strategies that seek to improve the understanding of A.I.-based results are examined here by carefully curating and monitoring the flow of data into, for example, artificial neural networks and random forest supervised models. While these models possess structures and related fitting procedures that are…
Joshua P. Jahner, C. Alex Buerkle, Dustin G. Gannon, Eliza M. Grames + 11 more
The proliferation of biological data with large numbers of samples and many dimensions is kindling hope that life scientists will be able to fit statistical and machine learning models that are highly predictive and interpretable. However, large biological data sets are commonly burdened with an inherent trade-off…
Anja K. Leist, Matthias Klee, Jung Hyun Kim, David H. Rehkopf + 3 more
'Stéphane P. A. Bordas' 'Graciela Muniz-Terrera' 'Sara Wade'] Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the…
Lilli Heinen, Robert L. Larson, Brad J. White, Sofia Alves-Pimenta
Title: Simple Summary Predictive models use historical data to make future predictions. These tools have become more common in the cattle industry in recent years. Their applications are broad but they are especially useful in the prediction of disease. This review explores published studies that use predictive models…
Alexandria Volkening
Traffic jams on roadways, echo chambers on social media, crowds of moving pedestrians, and opinion dynamics during elections are all complex social systems. These applications may seem disparate, but some of the questions that they motivate are similar from a mathematical perspective. Across these examples, researchers…
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…
Alin Adrian Alecu, Yunfeng Wu, Sundeep Singh
Biomedical prediction increasingly requires machine learning methods capable of integrating heterogeneous data spanning molecular, physiological, clinical, behavioral, and environmental representations. Yet progress in this area has often been framed primarily in terms of predictive algorithms, with less attention…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Authors not listed
Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown…
John Mark Agosta, Robert F. Horton
With the explosion of applications of Data Science, the field is has come loose from its foundations. This article argues for a new program of applied research in areas familiar to researchers in Bayesian methods in AI that are needed to ground the practice of Data Science by borrowing from AI techniques for model…
Raquel Costa, Bruno de Sousa, Thomas Kneib, Rui Martins + 1 more
Clinical prediction models play a crucial role in advancing personalized care for mental health disorders, providing essential insights for diagnosis, prognosis and intervention planning. This work examines the current methodological approaches used to develop such models, emphasizing their application to mental health…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
Michal Bozděch
Not only in sports is a neural network the most used type of artificial intelligence. With software development, anyone can create a neural network model, but little is known about how to prepare the data and how to set up the model algorithms to their maximum performance. For these reasons, this study aims to…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Saumyadipta Pyne, Deep Ray, Meghana S. Ray
With a general increase in human lifespan, the need for technological advances to develop strategies for healthy aging has assumed great importance. In the present study, our goal is to predict the progression of selected aging phenotypes in a given healthy individual as one continues aging past 65 years. Therefore, we…
Koichi Handa, Saki Yoshimura, Michiharu Kageyama, Takeshi Iijima
AI is expected to help identify excellent candidates in drug discovery. However, we face a lack of data as it is time consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel QSAR method to predict a parameter more precisely from an incomplete dataset via optimizing…
Husam Abdulnabi, J. Timothy Westwood
Machine Learning (ML) models may perform inconsistently on individual classes on nominal outputs or ranges on continuous outputs, collectively referred to here as bins. Models should be assessed through metrics that consider each bin individually, called bin metrics. Inconsistent model performance is often due to model…
Alexander B. Brummer, Agata Xella, Ryan Woodall, Vikram Adhikarla + 4 more
In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
Luis F. Arias-Giraldo, Marlon E. Cobos
Here, we present the new R package “enmpa,” which includes a range of tools for modeling ecological niches using presence-absence data via logistic generalized linear models. The package allows users to calibrate, select, project, and evaluate models using independent data. We have emphasized a comprehensive search for…