Article
Multidisciplinary Sciences
Jee Soo Park, Dong Wook Kim, Dongu Lee, Taeju Lee, Kyo Chul Koo, Woong Kyu Han, Byung Ha Chung, Kwang Suk Lee
Summary: A prediction model of spontaneous ureteral stone passage (SSP) was developed using machine learning and logistic regression, with stone opacity, location, and whether it was the first ureteral stone episode identified as significant predictors. The performance of the models in identifying SSP for 5-10 mm ureteral stones was good.
Proceedings Paper
Computer Science, Information Systems
Nida Seraj, Rashid Ali
Summary: A prediction model for spontaneous stone passage (SSP) was developed using decision tree based machine learning methods. The study found that stratified k-fold cross-validation method gave the best results, which can assist in selecting suitable treatment or monitoring techniques for dealing with ureteral stones.
2022 5TH INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT)
(2022)
Article
Automation & Control Systems
Chunxiao Li, Cynthia Rudin, Tyler H. McCormick
Summary: Instrumental variables are widely used in social and health sciences for causal inference. This paper presents a framework that utilizes machine learning to validate assumptions in the IV model and provides empirical evidence. Prediction validity is the key idea, and one-stage and two-stage approaches for IV are developed.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Medicine, General & Internal
Ozgur Ekici, Abdullah Gul, Salim Zengin, Caglar Boyaci, Metin Kilic
Summary: This study evaluates the association between ureteral wall thickness and spontaneous passage of ureteral stones, as well as the development of ureteral stricture. The results indicate that ureteral wall thickness can be used to predict spontaneous passage of ureteral stones and long-term development of ureteral stricture after ureterorenoscopy.
JCPSP-JOURNAL OF THE COLLEGE OF PHYSICIANS AND SURGEONS PAKISTAN
(2023)
Article
Urology & Nephrology
Naveen Kachroo, Rajat Jain, Sarah Maskal, Luay Alshara, Sherif Armanyous, Jason Milk, Leonard Kahn, Manoj Monga, Sri Sivalingam
Summary: This study aimed to identify key predictors of successful spontaneous passage (SP) of ureteral stones and found that the maximal ureteral wall thickness (UWT) at the stone site was the most significant predictor. Further prospective studies are needed to accurately predict spontaneous SP.
JOURNAL OF ENDOUROLOGY
(2021)
Article
Chemistry, Analytical
Tae-Hoon Kim, Myung Kyu Choi, Hang Seok Choi
Summary: This study utilized data-based prediction algorithms to model a biomass fast pyrolyzer and predict the yields of major products. Eight data-based prediction models were compared, indicating better agreement with experimental results compared to traditional lumped process models. The study provides new guidelines for modeling fast pyrolysis reactions using data-based prediction methods.
JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS
(2022)
Article
Urology & Nephrology
Chuanyu Gao, Max Peters, Piet Kurver, Thineskrishna Anbarasan, Keerthanaa Jayaraajan, Todd Manning, Sophia Cashman, Arjun Nambiar, Marcus Cumberbatch, Benjamin W. Lamb, Robert Pickard, Paul Erotocritou, Daron Smith, Veeru Kasivisvanathan, Taimur T. Shah
Summary: This study developed a nomogram to predict spontaneous stone passage in patients with acute ureteric colic, providing a scientific basis for clinicians and patients to make decisions on whether to choose conservative treatment.
Article
Radiology, Nuclear Medicine & Medical Imaging
Samir A. Haroon, Ducksoo Kim
Summary: Spontaneous rupture of the ureter, although rare, requires prompt diagnosis and management. Treatment has shifted towards minimally invasive interventions and conservative approaches, but open surgical intervention is still important in certain situations.
Article
Mathematics
Abdullah Alqahtani, Shtwai Alsubai, Adel Binbusayyis, Mohemmed Sha, Abdu Gumaei, Yu-Dong Zhang
Summary: Globally, the incidence of kidney stones has increased, making early detection crucial for improving individuals' lives. Machine Learning has gained attention for its ability to continuously enhance and deal with multi-dimensional data. This study proposes a smart toilet model in an IoT-fog environment to detect kidney stones using suitable ML algorithms from real-time urinary data.
Article
Urology & Nephrology
Tommy Chiou, Margaret F. Meagher, Jonathan H. Berger, Tony T. Chen, Roger L. Sur, Seth K. Bechis
Summary: The purpose of this study was to evaluate whether computer program-estimated urolith stone volume (SV) was a better predictor of spontaneous passage (SP) compared with program-estimated stone diameter (PD) or manually measured stone diameter (MD), and whether utilizing SV and MD together provided additional value in SP prediction compared with MD alone. Through retrospective analysis, it was found that SV had higher accuracy in predicting spontaneous passage within 4 and 6 weeks compared with MD and PD, and the combined use of SV and MD could provide better prediction of spontaneous passage, especially for larger or more proximal stones.
JOURNAL OF ENDOUROLOGY
(2023)
Article
Biochemical Research Methods
Nada Al Taweraqi, Ross D. King
Summary: This research integrates mechanistic cell signalling models with machine learning methods, using similarity features computed from the cell signalling models to improve the accuracy of gene expression level prediction, while providing biological knowledge about the genes.
BMC BIOINFORMATICS
(2022)
Article
Urology & Nephrology
Cagdas Senel, Ibrahim Can Aykanat, Ahmet Asfuroglu, Tanju Keten, Melih Balci, Yilmaz Aslan, Altug Tuncel
Summary: This study aimed to investigate the role of inflammatory markers in predicting the spontaneous passage of ureteral stones. Retrospectively reviewing 279 patients, it was found that inflammatory markers could not determine the likelihood of spontaneous stone passage, while stone size, location, and presence of hydronephrosis were more significant predictors.
Article
Neurosciences
Fengping Zhu, Zhiguang Pan, Ying Tang, Pengfei Fu, Sijie Cheng, Wenzhong Hou, Qi Zhang, Hong Huang, Yirui Sun
Summary: This study constructed efficient coagulopathy prediction models using data mining and machine learning algorithms, identifying important clinical parameters for acute coagulopathy occurrence. The random forest algorithm showed better performance compared to the support vector machine in predicting coagulopathy.
CNS NEUROSCIENCE & THERAPEUTICS
(2021)
Article
Medicine, General & Internal
Ismaeel Aghaways, Rebaz Ibrahim, Rawa Bapir, Rawezh Q. Salih, Karzan M. Salih, Berwn A. Abdulla
Summary: The role of ureteral wall thickness and inflammatory markers in guiding the decision-making process of ureteral stone treatment was assessed in this study. Results showed that increased ureteral wall thickness and inflammatory markers were associated with failure of spontaneous stone passage.
ANNALS OF MEDICINE AND SURGERY
(2022)
Article
Environmental Sciences
Patricia Alocen, Miguel a. Fernandez-Centeno, Miguel A. Toledo
Summary: This research aims to improve the accuracy of dam behavior prediction by combining different models. Firstly, two methods of model error estimation were compared, and it was found that Blocked Cross Validation outperforms Random Cross Validation in robustness. Then, two combination strategies were tested, and the results suggest that Stacking provides better predictions than Blending.
