27 papers · ranked by Valyu relevance
Riccardo Galanti, Massimiliano de Leoni, Merylin Monaro, Nicolò Navarin + 3 more
'Nicolò Navarin' 'Alan Marazzi' 'Brigida Di Stasi' 'Stéphanie Maldera'] Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution…
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…
J. Randall Moorman
In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made…
Ritwik Raj Saxena
– Concerns associated with occupational health and safety (OHS) remain critical and often underaddressed aspects of workforce management. This is especially true for high-risk industries such as manufacturing, construction, and mining. Such industries dominate the economic landscape of India which is a developing…
Edward Mensah, Sharon Levy, Chenghao Duan, Euan Anderson + 7 more
'Marilyn Lennon' 'Kimberley Kavanagh' 'Natalie Weir' 'David Kernaghan' 'Marc Roper' 'Emma Dunlop' 'Linda Lapp'] Background Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues.…
Alexander Muacevic, John R Adler, Qisthi A Hidayaturrohman, Eisuke Hanada
'Eisuke Hanada'] Heart failure is a leading cause of death among people worldwide. The cost of treatment can be prohibitive, and early prediction of heart failure would reduce treatment costs to patients and hospitals. Improved readmission prediction would also greatly help hospitals, allowing them to manage their…
Massimiliano de Leoni, Alessandro Padella
Learning (Extended Version) Authors: ['Massimiliano de Leoni' 'Alessandro Padella'] Abstract. Predictive business process analytics has become important for organizations, offering real-time operational support for their processes. However, these algorithms often perform unfair predictions because they are based on…
Sarah Di Grande, Thamires de Souza Oliveira, David Pagano, Salvatore Cavalieri + 1 more
Highlights What are the main findings?1. Machine Learning-based predictive analytics in Demand-Responsive Transport is emerging as a multi-level decision-support tool, supporting not only demand forecasting, but also operational reliability, service management, and planning-oriented decisions. 2. The literature has…
Muhammad Hassan Danish
Detection and Response Authors: ['Muhammad Hassan Danish'] Abstract—This research paper aims to examine the applicability of predictive analytics to improve the real-time identification and response to cyber-attacks. Today, threats in cyberspace have evolved to a level where conventional methods of defense are usually…
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…
Timo Schulte, Sabine Bohnet-Joschko
Introduction: Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Chathurani Ranathunge, Sagar S. Patel, Lubna Pinky, Vanessa L. Correll + 4 more
We present promor, a comprehensive, user-friendly R package that streamlines label-free (LFQ) proteomics data analysis and building machine learning-based predictive models with top protein candidates. promor is freely available as an open source R package on the Comprehensive R Archive Network…
Matthias Stierle, Karsten Kraume, Martin Matzner
Data-driven analysis of business processes has a long tradition in research. However, recently the term of process mining is mostly used when referring to data-driven process analysis. As a consequence, awareness for the many facets of process analysis is decreasing. In particular, while an increasing focus is put onto…
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…
Ahmad Nasimian, Saleena Younus, Emma U. Hammarlund, Kenneth J. Pienta + 2 more
Therapeutic resistance continues to impede overall survival rates for those affected by cancer. Although driver genes are associated with diverse cancer types, a scarcity of instrumental methods for predicting therapy response or resistance persists. Therefore, the impetus for designing predictive tools for therapeutic…
Banan Jamil Awrahman, Chia Aziz Fatah, Mzhda Yasin Hamaamin
Healthcare has evolved with the development of technology to improve the quality of life and save lives. Today, big data is considered as one of the most essential and promising future technology areas and has been attracting the medical community's attention. As a result of big data, we can improve patient outcomes…
André Patrício, Rafael S. Costa, Rui Henriques
The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring…
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…
Ronit Sharma, Nikolaos Meimetis, Arjana Begzati, Shashwat Depali Nagar + 2 more
Cancer metastasis, a process in which cancer cells migrate to secondary sites, accounts for 90% of cancer deaths. While machine learning has been used to predict metastasis in a variety of ways, they tend to be specific to tumor types or classification tasks. Here, we provide a pan-cancer machine learning model that is…
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…
Jules Schleinitz, Maxime Langevin, Yanis Smail, Benjamin Wehnert + 2 more
Synthetic yield prediction using machine learning is intensively studied. Previous work focused on two categories of datasets: High-Throughput Experimentation data, as an ideal case study and datasets extracted from proprietary databases, which are known to have a strong reporting bias towards high yields. However…
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…
Tobias Rehfeldt, Ralf Gabriels, Robbin Bouwmeester, Siegfried Gessulat + 6 more
Dataset acquisition and curation are often the hardest and most time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based LC-IM-MS datasets, due to the high-throughput data structure with high levels of noise and complexity between raw and machine learning-ready formats. While…
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)…
Jaka Kokošar, Cagatay Turkay, Luka Avsec, Miha Štajdohar + 1 more
We introduce a visual analytics methodology for survival analysis, and propose a framework that defines a reusable set of visualization and modeling components to support exploratory and hypothesis-driven biomarker discovery. Survival analysis—essential in biomedicine—evaluates patients’ survival rates and the onset of…
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…