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Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.
Sci Rep
December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
View Article and Find Full Text PDFProbabilistic prediction of Phosphate ion Adsorption onto Biochar Materials Using a Large Dataset and Online Deployment.
Chemosphere
December 2024
Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA. Electronic address:
Phosphate (PO(III)) contamination in water bodies poses significant environmental challenges, necessitating efficient and accurate methods to predict and optimize its removal. The current study addresses this issue by predicting the adsorption capacity of PO(III) ions onto biochar-based materials using five probabilistic machine learning models: eXtreme Gradient Boosting LSS (XGBoostLSS), Natural Gradient Boosting, Bayesian Neural Networks (NN), Probabilistic NN, and Monte-Carlo Dropout NN. Utilizing a dataset of 2952 data points with 16 inputs, XGBoostLSS demonstrated the highest R (0.
View Article and Find Full Text PDFMachine learning-based prediction of compost maturity and identification of key parameters during manure composting.
Bioresour Technol
December 2024
School of Resources and Environment, Northeast Agricultural University, Harbin 150030, China. Electronic address:
Evaluating compost maturity, e.g. via manual seed germination index (GI) measurement, is both time-consuming and costly during composting.
View Article and Find Full Text PDFExplaining persistent physical symptoms to patients in general practice: can tests to measure central sensitisation add value? A mixed-methods study.
BMC Prim Care
December 2024
Department of General Practice, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands.
Introduction: General practitioners (GPs) often face challenges in explaining to patients with persistent physical symptoms (PPS) why their symptoms persist. Providing an explanation of the central sensitisation (CS) mechanism to patients could be helpful, yet GPs do not routinely test for signs of CS in these patients. The aim of this study was to explore the value of applying a test to assess CS in enhancing explanations provided to patients.
View Article and Find Full Text PDFExtreme learning machine for identifying soil-dwelling microorganisms cultivated on agar media.
Sci Rep
December 2024
The National Institute of Horticultural Research, ul. Pomologiczna 18, 96-100, Skierniewice, Poland.
The aim of this research is to create an automated system for identifying soil microorganisms at the genera level based on raw microscopic images of monocultural colonies grown in laboratory environment. The examined genera are: Fusarium, Trichoderma, Verticillium, Purpureolicillium and Phytophthora. The proposed pipeline deals with unprocessed microscopic images, avoiding additional sample marking or coloration.
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