Sieberts Solveig K, Borzymowski Henryk, Guan Yuanfang, Huang Yidi, Matzner Ayala, Page Alex, Bar-Gad Izhar, Beaulieu-Jones Brett, El-Hanani Yuval, Goschenhofer Jann, Javidnia Monica, Keller Mark S, Li Yan-Chak, Saqib Mohammed, Smith Greta, Stanescu Ana, Venuto Charles S, Zielinski Robert, Jayaraman Arun, Evers Luc J W, Foschini Luca, Mariakakis Alex, Pandey Gaurav, Shawen Nicholas, Synder Phil, Omberg Larsson
Sage Bionetworks, Seattle, Washington, United States of America.
Independent researcher.
PLOS Digit Health. 2023 Mar 28;2(3):e0000208. doi: 10.1371/journal.pdig.0000208. eCollection 2023 Mar.
One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.
数字健康的一个充满前景的机遇在于，它有潜力通过与患者的日常生活互动以及收集大量真实世界数据，从而对疾病有更全面的理解。然而，鉴于现实世界中存在大量混杂因素，以及在家中收集真实数据面临的挑战，在家庭环境中验证和设定疾病严重程度指标是困难的。在此，我们利用从帕金森病患者收集的两个数据集，将连续的手腕佩戴式加速度计数据与家庭环境中频繁的症状报告相结合，以开发症状严重程度的数字生物标志物。利用这些数据，我们进行了一项公开的基准测试挑战，要求参与者构建针对三种症状（服药/未服药、异动症和震颤）的严重程度测量方法。42个团队参与其中，每个子挑战的表现都比基线模型有所提高。对提交的结果进行额外的集成建模进一步提高了性能，顶级模型在一组由训练有素的临床医生观察和评估症状的患者中得到了验证。
