16 papers · ranked by Valyu relevance
Ricardo J. Pais, Pietro Pinoli, Anna Bernasconi
Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes…
Shabbar I. Ranapurwala, Joseph E. Cavanaugh, Tracy Young, Hongqian Wu + 2 more
'Hongqian Wu' 'Corinne Peek-Asa' 'Marizen R. Ramirez'] Background The goal of predictive modelling is to identify the likelihood of future events, such as the predictive modelling used in climate science to forecast weather patterns and significant weather occurrences. In public health, increasingly sophisticated…
Gopi Battineni, Getu Gamo Sagaro, Nalini Chinatalapudi, Francesco Amenta
'Francesco Amenta'] This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are…
Bo Cao, Russell Greiner, Andrew Greenshaw, Jie Sui + 1 more
'Amaryllis Mavragani'] Title: Abstract Recent applications of artificial intelligence (AI) and machine learning in medicine, psychology, and social sciences have led to common terminological confusions. In this paper, we review emerging evidence from systematic reviews documenting widespread misuse of key terms…
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…
Roemer J Janse, Ameen Abu-Hanna, Iacopo Vagliano, Vianda S Stel + 5 more
'Kitty J Jager' 'Giovanni Tripepi' 'Carmine Zoccali' 'Friedo W Dekker' 'Merel van Diepen'] Title: ABSTRACT An artificial intelligence boom is currently ongoing, mainly due to large language models, leading to significant interest in artificial intelligence and subsequently also in machine learning (ML). One area where…
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…
Philip Greulich
Purpose of Review This article gives a broad overview of quantitative modelling approaches in biology and provides guidance on how to employ them to boost stem cell research, by helping to answer biological questions and to predict the outcome of biological processes. Recent Findings The twenty-first century has seen a…
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…
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…
Michael Onyema Edeh, Surjeet Dalal, Imed Ben Dhaou, Charles Chuka Agubosim + 3 more
Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8…
Christian Lovis, Sai Veeranki, Felix Agakov, Caitlin Doogan + 6 more
Background Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records data for identifying such patients…
Lars Holmberg, Andrew Vickers
Lars Holmberg and Andrew Vickers discuss the importance of ensuring prediction models lead to better decision making in light of new research into breast, endometrial, and ovarian cancer risk by Ruth Pfeiffer and colleagues. Please see later in the article for the Editors' Summary
Graziella Orrù, Merylin Monaro, Ciro Conversano, Angelo Gemignani + 1 more
'Giuseppe Sartori'] Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of…
Muni Lakshmi G K, Mokesh Rayalu G
Air pollution, especially elevated particulate matter concentrations, presents a substantial risk to public health and environmental sustainability in urban regions. By employing machine learning and hybrid ensemble models, this study develops a robust frame work for predicting the Air Quality Index (AQI). A multi-step…
Jaron Arbet, Cole Brokamp, Jareen Meinzen-Derr, Katy E. Trinkley + 1 more
Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met…