14 papers · ranked by Valyu relevance
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…
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…
Sabri Boughorbel, Rashid Al-Ali, Naser Elkum, Mansour Ebrahimi
We compared the performance of several prediction techniques for breast cancer prognosis, based on AU-ROC performance (Area Under ROC) for different prognosis periods. The analyzed dataset contained 1,981 patients and from an initial 25 variables, the 11 most common clinical predictors were retained. We compared eight…
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…
Bradley Malin, Khaled El Emam, Abraham Finny, Simon John Christoph Soerensen + 7 more
'Simon John Christoph Soerensen' 'Vishnu Kumar' 'Chanjung Lee' 'Brian Jo' 'Hyunki Woo' 'Yoori Im' 'Rae Woong Park' 'ChulHyoung Park'] Background Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining…
Amir Sorayaie Azar, Samin Babaei Rikan, Amin Naemi, Jamshid Bagherzadeh Mohasefi + 3 more
'Jamshid Bagherzadeh Mohasefi' 'Habibollah Pirnejad' 'Matin Bagherzadeh Mohasefi' 'Uffe Kock Wiil'] Background Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on…
Igor Odrobina, Constantinos Bakogiannis, Michel Noutsias
This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of…
Banne Nemeth, Mark J.R. Smeets, Suzanne C. Cannegieter, Maarten van Smeden
'Maarten van Smeden'] Clinical prediction modeling has become an increasingly popular domain of venous thromboembolism research in recent years. Prediction models can help healthcare providers make decisions regarding starting or withholding therapeutic interventions, or referrals for further diagnostic workup, and can…
Muffy Calder, Claire Craig, Dave Culley, Richard de Cani + 15 more
'Christl A. Donnelly' 'Rowan Douglas' 'Bruce Edmonds' 'Jonathon Gascoigne' 'Nigel Gilbert' 'Caroline Hargrove' 'Derwen Hinds' 'David C. Lane' 'Dervilla Mitchell' 'Giles Pavey' 'David Robertson' 'Bridget Rosewell' 'Spencer Sherwin' 'Mark Walport' 'Alan Wilson'] In order to deal with an increasingly complex world, we…
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…
Emanuela Barbini, Gabriele Cevenini, Sabino Scolletta, Bonizella Biagioli + 2 more
'Bonizella Biagioli' 'Pierpaolo Giomarelli' 'Paolo Barbini'] Background Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of…
Curtis E Kennedy, James P Turley
Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest.…
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…
Marco Podda, Davide Bacciu, Alessio Micheli, Roberto Bellù + 2 more
Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network…