基于机器学习的尿路结石复发预测模型的开发与验证 - Suppr

Development and validation of a machine learning-based prediction model for urinary calculi recurrence.

作者信息

Guo Jiangkun, Zhang Jinhang, Zhang Jinxiao, Xu Changbao, Wang Xikun, Liu Changwei

机构信息

Department of Urology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China.

The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.

出版信息

Urolithiasis. 2025 Jun 19;53(1):120. doi: 10.1007/s00240-025-01792-3.

DOI:10.1007/s00240-025-01792-3

PMID:40536670

Abstract

摘要

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Development and validation of a machine learning-based prediction model for urinary calculi recurrence.

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J Endourol. 2024 Aug;38(8):809-816. doi: 10.1089/end.2023.0457.

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BJU Int. 2024 Jan;133(1):34-43. doi: 10.1111/bju.16179. Epub 2023 Sep 27.

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Development and validation of a machine learning-based prediction model for urinary calculi recurrence.

作者信息

Guo Jiangkun, Zhang Jinhang, Zhang Jinxiao, Xu Changbao, Wang Xikun, Liu Changwei

机构信息

Department of Urology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China.

The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.

出版信息

Urolithiasis. 2025 Jun 19;53(1):120. doi: 10.1007/s00240-025-01792-3.

DOI:10.1007/s00240-025-01792-3

PMID:40536670

Abstract

摘要

相似文献

Development and validation of a machine learning-based prediction model for urinary calculi recurrence.

Urolithiasis. 2025 Jun 19;53(1):120. doi: 10.1007/s00240-025-01792-3.

J Cancer Res Clin Oncol. 2025 Jun 17;151(6):190. doi: 10.1007/s00432-025-06241-7.

An explainable machine learning model for predicting the risk of distant metastasis in intrahepatic cholangiocarcinoma: a population-based cohort study.

Discov Oncol. 2025 Jun 18;16(1):1140. doi: 10.1007/s12672-025-02952-y.

Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation.

Front Endocrinol (Lausanne). 2025 Jun 5;16:1614657. doi: 10.3389/fendo.2025.1614657. eCollection 2025.

Identifying the key influencing factors of psychological birth trauma in primiparous women with interpretable machine learning.

Int J Nurs Sci. 2025 Apr 15;12(3):253-260. doi: 10.1016/j.ijnss.2025.04.008. eCollection 2025 May.

A Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions Through Comprehensive Feature Selection and Explainable Ensemble Learning.

Cancer Manag Res. 2025 May 8;17:917-932. doi: 10.2147/CMAR.S514693. eCollection 2025.

Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA.

Nat Sci Sleep. 2025 Jun 12;17:1271-1289. doi: 10.2147/NSS.S516912. eCollection 2025.

Machine learning-based prediction of 6-month functional recovery in hypertensive cerebral hemorrhage: insights from XGBoost and SHAP analysis.

Front Neurol. 2025 Jun 4;16:1608341. doi: 10.3389/fneur.2025.1608341. eCollection 2025.

Predicting all-cause mortality and premature death using interpretable machine learning among a middle-aged and elderly Chinese population.

Heliyon. 2024 Aug 28;10(17):e36878. doi: 10.1016/j.heliyon.2024.e36878. eCollection 2024 Sep 15.

Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.

BMC Oral Health. 2025 Jun 3;25(1):898. doi: 10.1186/s12903-025-06298-3.

本文引用的文献

Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.

Sci Rep. 2025 Feb 5;15(1):4327. doi: 10.1038/s41598-025-88704-y.

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Cardiovasc Diabetol. 2024 Nov 15;23(1):407. doi: 10.1186/s12933-024-02503-9.

A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.

J Endourol. 2024 Aug;38(8):809-816. doi: 10.1089/end.2023.0457.

Metabolic Evaluation and Recurrence Prevention for Urinary Stone Patients: An EAU Guidelines Update.

Eur Urol. 2024 Oct;86(4):343-363. doi: 10.1016/j.eururo.2024.05.029. Epub 2024 Jul 27.

Impact of low-oxalate diet on hyperoxaluria among patients suffering from nephrolithiasis.

Food Sci Nutr. 2024 Apr 5;12(6):4292-4298. doi: 10.1002/fsn3.4088. eCollection 2024 Jun.

Surgical procedure and recurrence of upper urinary tract stone: a national-wide study based on hospitalized patients.

World J Urol. 2024 Mar 13;42(1):132. doi: 10.1007/s00345-024-04831-7.

Routine Urinary Biochemistry Does Not Accurately Predict Stone Type Nor Recurrence in Kidney Stone Formers: A Multicentre, Multimodel, Externally Validated Machine-Learning Study.

J Endourol. 2023 Dec;37(12):1295-1304. doi: 10.1089/end.2023.0451. Epub 2023 Oct 31.

Ae index is an independent predictor of kidney stone recurrence in overweight and obese patients.

BMC Urol. 2023 Sep 23;23(1):151. doi: 10.1186/s12894-023-01321-7.

Prevalence of urolithiasis in China: a systematic review and meta-analysis.

BJU Int. 2024 Jan;133(1):34-43. doi: 10.1111/bju.16179. Epub 2023 Sep 27.

Establishment of a new predictive model for the recurrence of upper urinary tract stones.

Int Urol Nephrol. 2023 Oct;55(10):2411-2420. doi: 10.1007/s11255-023-03698-8. Epub 2023 Jul 12.

Development and validation of a machine learning-based prediction model for urinary calculi recurrence.

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Development and validation of a machine learning-based prediction model for urinary calculi recurrence.

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