Selective Partitioned Regression for Accurate Kidney Health Monitoring
Selective Partitioned Regression for Accurate Kidney Health Monitoring
复制标题
用于准确肾脏健康监测的选择性分区回归
DOI：
10.1007/s10439-024-03470-8
复制
发表时间：
2024
影响因子：
3.8
通讯作者：
Anastasiu, David C.
中科院分区：
文献类型：
--
作者：
Whelan, Alex;Elsayed, Ragwa;Bellofiore, Alessandro;Anastasiu, David C.
被诊断为肾病晚期的人数每年都在上升。早期发现和持续监测是预防严重肾损害或肾功能衰竭的唯一微创手段。我们提出了一种经济高效的基于机器学习的测试系统，该系统可以促进廉价而准确的肾脏健康检查。我们提出的框架被开发成iPhone应用程序，使用基于摄像头的生物传感器和最先进的经典机器学习和深度学习技术，根据试纸中的比色变化预测样本中肌酐的浓度。预测的肌酐浓度然后被用来将肾脏疾病的严重程度分类为健康、中等或严重。在这篇文章中，我们关注的是机器学习模型将比色反应转化为肾脏健康预测的有效性。在这种情况下，我们彻底评估了我们提出的新模型相对于最先进的经典机器学习和深度学习方法的有效性。此外，我们还进行了一些消融研究，以测量使用不同元参数选择进行训练时模型的性能。我们的评估结果表明，我们的选择性分割回归模型(SPR)使用基于颜色的直方图特征和直方图梯度增强树基础估计器，与最先进的方法相比，具有更好的整体预测性能。我们的初步研究表明，SPR可以利用廉价的横向流动检测试纸和基于智能手机的应用程序，成为检测肾脏疾病严重程度的有效工具。还需要额外的工作来验证模型在各种设置下的性能。
The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.
DOI：
10.1053/j.ajkd.2013.12.006
发表时间：
2014-05
期刊：
American journal of kidney diseases : the official journal of the National Kidney Foundation
影响因子：
--
作者：
Levey AS;Inker LA;Coresh J
通讯作者：
Coresh J
影响因子：
9.3
作者：
Levey, Andrew S.;Coresh, Josef;Van Lente, Frederick
通讯作者：
Van Lente, Frederick
自动化侧流检测识别框架：探索移动应用中可穿戴侧流检测的挑战
影响因子：
8.5
作者：
Jayanth Sivakumar;Ji Hyun Yang;M. Kelly;Ahyeon Koh;Daehan Won
通讯作者：
Daehan Won