Koo Youngwook, Kim Minki, Lee Woong-Woo
College of Business, Korea Advanced Institute of Science and Technology, Seoul, Korea.
Department of Neurology, Nowon Eulji Medical Center, Eulji University, Seoul, Korea.
J Clin Neurol. 2025 Jan;21(1):21-30. doi: 10.3988/jcn.2024.0175.
Parkinson's disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information.
We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods.
During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931-0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922.
A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.
帕金森病（PD）具有多种前驱症状，且这些症状大多是通过回顾性研究来调查的。虽然诸如快速眼动睡眠行为障碍等一些症状具有高度特异性，但其他症状则较为常见。这使得仅基于特异性较低的前驱症状来预测帕金森病高危人群具有挑战性。通过使用先进的深度学习技术分析大量可用信息，可以提高仅使用特异性较低症状时的预测准确性。本研究旨在利用包括处方信息在内的医保数据，提高基于深度学习的前驱期帕金森病筛查的性能。
我们从韩国国民健康保险队列数据中抽取了820例帕金森病患者和8200例年龄及性别匹配的非帕金森病对照。利用诊断代码、用药代码和前驱期的各种组合开发了一种深度学习算法。
在从第 -3年到第0年的前驱期，仅使用诊断代码预测帕金森病的准确率高达0.937。同期添加用药代码并未提高准确率（0.931 - 0.935）。对于更早的前驱期（第 -6年到第 -3年），仅使用诊断代码时帕金森病预测的准确率降至0.890。纳入所有用药代码数据后，准确率显著提高至0.922。
使用前驱期诊断代码和用药代码的深度学习算法在筛查帕金森病方面是有效的。为帕金森病高危人群开发一个利用自动收集的医保数据的监测系统可能具有成本效益。这种方法可以通过关注最适合纳入准确诊断测试的候选人群来简化疾病修饰药物的研发过程。
