Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands.
Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands.
Sensors (Basel). 2023 May 31;23(11):5243. doi: 10.3390/s23115243.
Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity.
This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers.
This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed.
This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials.
mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
中枢神经系统（CNS）疾病受益于持续监测，以评估疾病进展和治疗效果。移动医疗（mHealth）技术为患者的远程和连续症状监测提供了一种手段。机器学习（ML）技术可以将 mHealth 数据处理和转化为疾病活动的精确和多维生物标志物。
本叙述性文献综述旨在概述当前使用 mHealth 技术和 ML 开发生物标志物的现状。此外，还提出了确保这些生物标志物准确性、可靠性和可解释性的建议。
本综述从 PubMed、IEEE 和 CTTI 等数据库中提取了相关文献。然后提取、汇总并回顾了所选出版物中使用的 ML 方法。
本综述综合并呈现了 66 篇解决使用 ML 基于 mHealth 生物标志物的不同方法的出版物。这些已审查的出版物为有效的生物标志物开发提供了基础，并为未来临床试验中创建具有代表性、可重现性和可解释性的生物标志物提供了建议。
基于 mHealth 的和基于 ML 的生物标志物在 CNS 疾病的远程监测中有很大的潜力。然而，需要进一步的研究和研究设计的标准化来推进这一领域。随着持续的创新，基于 mHealth 的生物标志物有望改善 CNS 疾病的监测。
