Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study by Li Shen, Li Shen, Jiaqiang Wu, Jianger Lan, Chao Chen, Yi Wang, Zhiping Li

Subjects: “…machine learning…”
Get full text

Yoga practices effect on VCSS-based classification of patients with chronic venous insufficiency based on hybrid machine learning algorithms by Changhong Pan, Lu Qi, Lili Zhao, Yijun Wei

Subjects: Get full text

Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

Subjects: Get full text

Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance by Pratyush Kumar MAHARANA, Tapan Kumar BEHERA, Pradeep Kumar NAIK

Subjects: Get full text

Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma by Qianru Li, Qianru Li, Heli Li

Subjects: Get full text

Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. by Yulin Lai, Peiyuan Huang

“…<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. …”
Get full text

The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis by Tingting Lin, Tingting Lin, Huimin Wan, Jie Ming, Yifei Liang, Linxin Ran, Jingjing Lu

Subjects: Get full text

MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

Subjects: “…Machine learning techniques…”
Get full text

Inteligencia Artificial: Machine Learning, para Detección Temprana de Plagas y Enfermedades de Cultivos Básicos, Nicaragua by Saira María Urbina Cienfuegos, Jazcar Josué Bravo Rivas

Subjects: “…machine learning…”
Get full text

Classifying Food Items During an Eating Occasion: A Machine Learning Approach with Slope Dynamics for Windowed Kinetic Data by Ileana Baldi, Corrado Lanera, Mohammad Junayed Bhuyan, Paola Berchialla, Luca Vedovelli, Dario Gregori

“…Conclusions: Monitoring food items using simple wristband-mounted wearable devices is feasible and accurate for some foods while unsatisfactory for others. Machine learning tools are necessary to deal with the complexity of signals gathered by the devices, and research is ongoing to improve accuracy further and work on large-scale and real-time implementation and testing.…”
Get full text

Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning by Alejandro Casallas-Lagos, Javier M. Antelis, Claudia Moreno, Ramiro Franco-Hernández

Subjects: Get full text

Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study by Soonil Kwon, SooMin Chung, So-Ryoung Lee, Kwangsoo Kim, Junmo Kim, Dahyeon Baek, Hyun-Lim Yang, Eue-Keun Choi, Seil Oh

“…Conclusions Among the patients with AF/AFL, machine learning may predict reduced LVEF with 12-lead ECGs of AF/AFL.…”
Get full text

Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients by Thomas Meredith, Thomas Meredith, Thomas Meredith, Farhan Mohammed, Amy Pomeroy, Sebastiano Barbieri, Sebastiano Barbieri, Erik Meijering, Louisa Jorm, David Roy, Jason Kovacic, Jason Kovacic, Jason Kovacic, Jason Kovacic, Michael Feneley, Michael Feneley, Michael Feneley, Christopher Hayward, Christopher Hayward, Christopher Hayward, David Muller, David Muller, David Muller, Mayooran Namasivayam, Mayooran Namasivayam, Mayooran Namasivayam

Subjects: Get full text

Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. by Fiona Leonard, Dympna O'Sullivan, John Gilligan, Nicola O'Shea, Michael J Barrett

“…<h4>Introduction</h4>Machine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. …”
Get full text

On geometry of fixed figures via φ−interpolative contractions and application of activation functions in neural networks and machine learning models by Khairul Habib Alam, Yumnam Rohen, Anita Tomar, Mohammad Sajid

“…Additionally, we explore the concept of fixed figures in the context of Rectified Linear Unit (ReLU), a widely-used activation function in neural networks and machine learning models. Our exploration of fixed figures in the context of Rectified Linear Units (ReLU) further deepens our understanding of nonlinear systems and their relationship to neural network behavior.…”
Get full text

Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study by Jiexin Chen, Qiongbing Zheng, Youmian Lan, Meijing Li, Ling Lin

Subjects: Get full text

Advancements and challenges in agriculture: a comprehensive review of machine learning and IoT applications in vertical farming and controlled environment agriculture by Binoy Sasmal, Gobinda Das, Preeti Mallick, Sneha Dey, Suman Ghorai, Subrata Jana, Chiranjibe Jana

“…Implementing VF will solve many challenges by utilizing modern machine learning, IoT, and AI techniques, increasing VF productivity and quality. …”
Get full text

A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models by Fahmida Kousar, Arshiya Sultana, Marwan Ali Albahar, Manoj Shamkuwar, Md Belal Bin Heyat, Mohd Ammar Bin Hayat, Saba Parveen, John Irish G. Lira, John Irish G. Lira, Khaleequr Rahman, Abdullah Alammari, Eram Sayeed

Subjects: Get full text

A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles by Joost Groen, Bas M. de Haan, Ruben J. Overduin, Andrea B. Haijer-Schreuder, Terry GJ Derks, M. Rebecca Heiner-Fokkema

Subjects: Get full text

Bond behavior of galvanized iron fiber reinforced concrete with recycled coarse aggregate and model prediction using machine learning by Md. Jahidul Islam, Ummul Wara Labiba, Tasfiah Faisal Chowdhury, Md. Shahjalal, Tanvir Mustafy, Tanvir Hassan Tusher

Subjects: Get full text