26 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…
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…
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…
Danilo Bzdok, Denis Engemann, Olivier Grisel, Gaël Varoquaux + 1 more
In the 20^th^ century many advances in biological knowledge and evidence-based medicine were supported by p-values and accompanying methods. In the beginning 21^st^ century, ambitions towards precision medicine put a premium on detailed predictions for single individuals. The shift causes tension between traditional…
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…
Skyler Cranmer, Bruce Desmarais
The large majority of inferences drawn in empirical political research follow from model-based associations (e.g. regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that…
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…
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…
MA Nang Laik
One of the service providers in the financial service sector, who provide premium service to the customers, wanted to harness the power of data analytics as data mining can uncover valuable insights for better decision making. Therefore, the author aimed to use predictive analytics to discover crucial factors that will…
Andrew C. Miller, Nicholas J. Foti, Emily B. Fox
Breiman's classic paper casts data analysis as a choice between two cultures: data modelers and algorithmic modelers. Stated broadly, data modelers use simple, interpretable models with well-understood theoretical properties to analyze data. Algorithmic modelers prioritize predictive accuracy and use more flexible…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
Marjolein Fokkema, Carolin Strobl
The authors would like to thank Benjamin Christoffersen for his contributions to the development of package pre. The authors would like to thank Susan Niessen for granting access to the dataset from Example 2 (Predicting Academic Achievement). Example 3 (Predicting Substance Use) results from secondary analyses of data…
Authors not listed
Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
Casey C. Bennett, Thomas W. Doub, Rebecca Selove
- EHRs are increasingly likely to contain data and functionality that can support computational approaches to healthcare. - Predictive modeling of EHR data has achieved 70-72% accuracy in predicting individualized treatment response at baseline. - Clinical decision support can be conceptualized as a form of artificial…
Matthew W. Self, Peter Cheeseman
This paper shows that the common method used for making predictions under uncertainty in AI and science is in error. This method is to use currently available data to select the best model from a given class of models-this process is called abduction-and then to use this model to make predictions about future data. The…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…
Gonçalo Mateus, Cláudia Soares, João Leitão, António Rodrigues
The use of machine learning for time series prediction has become increasingly popular across various industries thanks to the availability of time series data and advancements in machine learning algorithms. However, traditional methods for time series forecasting rely on pre-optimized models that are ill-equipped to…
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…
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…
Shreeya Banerji
Diabetes mellitus is a growing problem, especially in developing countries. People suffering from diabetes have an increased risk of developing a number of serious health problems. Consistently high blood glucose levels can lead to serious diseases affecting the heart and blood vessels, eyes, kidney, etc. In addition…
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…
Tilda Herrgårdh, Elizabeth Hunter, Kajsa Tunedal, Håkan Örman + 5 more
One of the more interesting ideas for achieving personalized, preventive, and participatory medicine is the concept of a digital twin. A digital twin is a personalized computer model of a patient. So far, digital twins have been constructed using either mechanistic models, which can simulate the trajectory of…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
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…