Ziegenbein Charles B, Ortiz Bengie L, Gupta Vibhuti, Choi Sung Won
Department of Pediatrics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Autonomous Systems Research Department, Peraton Labs, Basking Ridge, NJ, United States.
Proc IEEE Int Conf Big Data. 2024 Dec;2024:4996-5005. doi: 10.1109/bigdata62323.2024.10825132.
Hematopoietic stem cell transplantation (HCT) is a potentially life-saving treatment that uses healthy blood-forming cells from donors to replace dysfunctional or damaged hematopoietic cells in patients with various blood disorders. This procedure is often employed to treat conditions such as hematological malignancies (e.g., leukemia, lymphoma, myeloma) and other severe blood or immune system diseases. Monitoring post-transplant complications is essential for tracking physiological effects and aiding in clinical decision-making. Biobehavioral aspects of care partners (i.e., unpaid caregivers) can also be influenced during the post-transplant stage of HCT. Wearable devices offer a non-invasive way to continuously track physiological parameters, making them a valuable resource for health monitoring. However, the physiological data collected from wearables is highly unstructured, often containing missing values, outliers, redundant features, and erroneous measurements leading to false conclusions/prediction. Therefore, enhancing data quality is essential for deriving meaningful insights. This paper introduces novel pre-processing methods to build a high quality, comprehensive, standardized, AI/ML ready, and clinically meaningful wearable dataset of HCT patients and caregivers. To test our data cleaning implementation, our cleaned, high-quality dataset is utilized to predict mood and compliance in HCT patients and their caregivers using machine learning algorithms. The paper illustrates our proposed approach and presents experimental results conducted on the data collected from Michigan Medicine for HCT patients and caregivers. Our preliminary experimental results are promising, demonstrating the effectiveness of the proposed methods and the high-quality dataset in predicting mood and compliance for the participants.
造血干细胞移植（HCT）是一种可能挽救生命的治疗方法，它使用供体的健康造血细胞来替代患有各种血液疾病患者体内功能失调或受损的造血细胞。该程序常用于治疗血液系统恶性肿瘤（如白血病、淋巴瘤、骨髓瘤）等疾病以及其他严重的血液或免疫系统疾病。监测移植后并发症对于追踪生理效应和辅助临床决策至关重要。在造血干细胞移植的移植后阶段，护理伙伴（即无偿护理人员）的生物行为方面也会受到影响。可穿戴设备提供了一种非侵入性的方式来持续追踪生理参数，使其成为健康监测的宝贵资源。然而，从可穿戴设备收集的生理数据高度无结构，通常包含缺失值、异常值、冗余特征和错误测量，从而导致错误的结论/预测。因此，提高数据质量对于获得有意义的见解至关重要。本文介绍了新颖的预处理方法，以构建一个高质量、全面、标准化、适用于人工智能/机器学习且具有临床意义的造血干细胞移植患者及护理人员可穿戴数据集。为了测试我们的数据清理实施情况，我们使用清理后的高质量数据集，通过机器学习算法预测造血干细胞移植患者及其护理人员的情绪和依从性。本文阐述了我们提出的方法，并展示了对从密歇根医学中心收集的造血干细胞移植患者及护理人员数据进行的实验结果。我们的初步实验结果很有前景，证明了所提出的方法和高质量数据集在预测参与者情绪和依从性方面的有效性。
