通过早期预测治疗结果提高遗尿警报治疗的疗效：一种机器学习方法。
Improving the efficacy of enuresis alarm treatment through early prediction of treatment outcome: a machine learning approach.
作者信息
Jönsson Karl-Axel, Andersson Edvin, Nevéus Tryggve, Gärdenfors Torbjörn, Balkenius Christian
机构信息
Department of Biomedical Engineering, Lund University, Lund, Sweden.
Department of Women's and Children's Health Uppsala University, Uppsala, Sweden.
出版信息
Front Urol. 2023 Nov 24;3:1296349. doi: 10.3389/fruro.2023.1296349. eCollection 2023.
INTRODUCTION
Bedwetting, also known as enuresis, is the second most common chronic health problem among children and it affects their everyday life negatively. A first-line treatment option is the enuresis alarm. This method entails the child being awoken by a detector and alarm unit upon urination at night, thereby changing their arousal mechanisms and potentially curing them after 6-8 weeks of consistent therapy. The enuresis alarm treatment has a reported success rate above 50% but requires significant effort from the families involved. Additionally, there is a challenge in identifying early indicators of successful treatment.
METHODS
The alarm treatment has been further developed by the company Pjama AB, which, in addition to the alarm, offers a mobile application where users provides data about the patient and information regarding each night throughout the treatment. The wet and dry nights are recorded, in addition to the actual timing of the bedwetting incidents. We used the machine learning model random forest to see if predictions of treatment outcome could be made in early stages of treatment and shorten the evaluation time based on data from 611 patients. This was carried out by using and analyzing data from patients who had used the Pjama application. The patients were split into training and testing groups to evaluate to what extent the algorithm could make predictions every day about whether a patient's treatment would be successful, partially successful, or unsuccessful.
RESULTS
The results show that a large number of patient outcomes can already be predicted accurately in the early stages of treatment.
DISCUSSION
Accurate predictions enable the correct measures to be taken earlier in the treatment, including increasing motivation, adding pharmacotherapy, or terminating treatment. This has the potential to shorten the treatment in general, and to detect patients who will not respond to the treatment early on, which in turn can improve the lives of children suffering from enuresis. The results show great potential in making the treatment of enuresis more efficient.
引言
尿床，也称为遗尿症，是儿童中第二常见的慢性健康问题，对他们的日常生活产生负面影响。一线治疗选择是遗尿报警器。这种方法需要孩子在夜间排尿时被探测器和警报装置唤醒，从而改变他们的唤醒机制，并在持续治疗6至8周后有可能治愈。据报道，遗尿报警器治疗的成功率超过50%，但需要相关家庭付出巨大努力。此外，识别成功治疗的早期指标存在挑战。
方法
Pjama AB公司对报警器治疗进行了进一步开发，该公司除了提供报警器外，还提供一款移动应用程序，用户可在其中提供有关患者的数据以及整个治疗过程中每晚的信息。除了尿床事件的实际时间外，还记录了尿床和干爽的夜晚。我们使用机器学习模型随机森林，根据611名患者的数据，查看是否能在治疗早期对治疗结果进行预测，并缩短评估时间。这是通过使用和分析使用Pjama应用程序的患者数据来进行的。将患者分为训练组和测试组，以评估该算法每天能在多大程度上预测患者的治疗是否会成功、部分成功或失败。
结果
结果表明，在治疗早期就能准确预测大量患者的治疗结果。
讨论
准确的预测能够在治疗早期采取正确的措施，包括增强动力、增加药物治疗或终止治疗。这有可能总体上缩短治疗时间，并尽早发现对治疗无反应的患者，进而改善遗尿症患儿的生活。结果显示出在提高遗尿症治疗效率方面的巨大潜力。
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