Preparing Wearable Data for AI-Powered Mood and Compliance Prediction in HCT Patients and Caregivers

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.