利用无监督机器学习识别脊髓损伤后的膀胱表型：检查泌尿系统症状和生活质量的新方法。

The Journal of Urology Pub Date : 2024-04-16 DOI:10.1097/ju.0000000000003984

Blayne Welk, Tianyue Zhong, Jeremy Myers, John Stoffel, Sean Elliot, Sara M Lenherr, Daniel Lizotte

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引用次数: 0

摘要

脊髓损伤（SCI）患者的泌尿症状和生活质量参差不齐。我们的目标是利用机器学习来识别脊髓损伤后的膀胱相关表型，并评估它们与泌尿症状和 QOL 的关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Identifying Bladder Phenotypes After Spinal Cord Injury With Unsupervised Machine Learning: A New Way to Examine Urinary Symptoms and Quality of Life.

Patients with spinal cord injuries (SCI) experience variable urinary symptoms and QOL. Our objective was to use machine learning to identify bladder-relevant phenotypes after SCI and assess their association with urinary symptoms and QOL.

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The Journal of Urology

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