27 papers · ranked by Valyu relevance
Laurie Miles PhD
This presentation aims to go over a number of things. Firstly it will welcome the attendees to SAS UK HQ at Wittington House and give the audience a brief introduction into SAS as an organisation. This will include a short history of the company and an overview of what SAS does. This focuses on predictive analytics…
Frederic Michard, Jean Louis Teboul
Electronic medical records and physiologic monitors produce unprecedented amounts of clinical data, which increasingly powerful computers may turn into novel insights through machine learning and predictive algorithms. Predictive analytics are statistical methods (e.g., random forest models and neural networks)…
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.…
Md Saiful Islam, Md Mahmudul Hasan, Xiaoyi Wang, Hayley D. Germack + 1 more
'Md Noor-E-Alam'] The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage-attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of…
Lu Hong, PJ Lamberson, Scott E Page
An increasing proportion of decisions, design choices, and predictions are being made by hybrid groups consisting of humans and artificial intelligence (AI). In this paper, we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks, the primary use of AI. Our analysis…
Reyes-González Juan Pablo, Díaz-Peregrino Roberto, Soto-Ulloa Victor, Galvan-Remigio Isabel + 2 more
In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze the data in order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits…
Quoc Duy Vo, Jaya Thomas, Shinyoung Cho, Pradipta De + 2 more
'Lee Sael'] Abstract—Business Intelligence and Analytics (BI&A) is the process of extracting and predicting business-critical insights from data. Traditional BI focused on data collection, extraction, and organization to enable efficient query processing for deriving insights from historical data. With the rise of big…
Ario Santoso
Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of…
Chiara Di Francescomarino, Marlon Dumas, Fabrizio Maria Maggi, Irene Teinemaa
'Irene Teinemaa'] Business process enactment is generally supported by information systems that record data about process executions, which can be extracted as event logs. Predictive process monitoring is concerned with exploiting such event logs to predict how running (uncompleted) cases will unfold up to their…
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…
Jose Benuzillo, Lucy A. Savitz, Scott Evans
Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Recent applications of AI to common health care scenarios, such as screening and diagnosing, have fueled optimism about the use of advanced analytics to improve care. Careful and…
Alessandro Bolli, Paolo Di Domenico, Giordano Bottà
In the last decade the scientific community witnessed a large increase in Genome-Wide Association Study sample size, in the availability of large Biobanks and in the improvements of statistical methods to model genomes features. This have paved the way for the development of new prediction medicine tools that use…
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…
Ramyaa Ramyaa, Omid Hosseini, Giri P Krishnan, Sridevi Krishnan
Nutritional phenotyping is a promising approach to achieve personalized nutrition. While conventional statistical approaches haven’t enabled personalizing well yet, machine-learning tools may offer solutions that haven’t been evaluated yet. The primary aim of this study was to use energy balance components – input…
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…
Chia‐Yen Lee, Chen‐Fu Chien
The practical application of machine learning and data science (ML/DS) techniques present a range of procedural issues to be examined and resolve including those relating to the data issues, methodologies, assumptions, and applicable conditions. Each of these issues can present difficulties in practice; particularly…
Kwetishe Joro Danjuma
The nature of clinical data makes it difficult to quickly select, tune and apply machine learning algorithms to clinical prognosis. As a result, a lot of time is spent searching for the most appropriate machine learning algorithms applicable in clinical prognosis that contains either binary-valued or multi-valued…
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…
Tan Shiang-Yen, Taizan Chan
Despite actionable insight being widely recognized as the outcome of data analytics, there is a lack of a systematic and commonly-agreed definition for the term. More importantly, existing definitions are generally too abstract for informing the design of data analytics systems. This study proposes a definition of…
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…
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…
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…