Informal caregivers (e.g.,family members or friends) of people living with Alzheimers Disease and Related Dementias (ADRD) face substantial challenges and often seek informational or emotional support through online communities. Understanding the factors that drive engagement within these platforms is crucial, as it can enhance their long-term value for caregivers by ensuring that these communities effectively meet their needs. This study investigated the user interaction dynamics within two large, popular ADRD communities, TalkingPoint and ALZConnected, focusing on topic initiator engagement, initial post content, and the linguistic patterns of comments at the thread level. Using analytical methods such as propensity score matching, topic modeling, and predictive modeling, we found that active topic initiator engagement drives higher comment volumes, and reciprocal replies from topic initiators encourage further commentor engagement at the community level. Practical caregiving topics prompt more re-engagement of topic initiators, while emotional support topics attract more comments from other commentors. Additionally, the linguistic complexity and emotional tone of a comment influence its likelihood of receiving replies from topic initiators. These findings highlight the importance of fostering active and reciprocal engagement and providing effective strategies to enhance sustainability in ADRD caregiving and broader health-related online communities.

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.

User-generated content in social media is increasingly acknowledged as a rich resource for research into health problems. One particular area of interest is in the semantics individuals’ evoke because they can influence when health-related information is disclosed. While there have been multiple investigations into why self-disclose occurs, much less is known about when individuals choose to disclose information about other people (e.g., a relative), which is a significant privacy concern. In this paper, we introduce a novel framework to investigate how semantics influence disclosure routines for 34 health issues. This framework begins with a supervised classification model to distinguish tweets that communicate personal health issues from confounding concepts (e.g., metaphorical statements that include a health-related keyword). Next, we annotate tweets for each health issue with linguistic and psychological categories (e.g. social processes, affective processes and personal concerns). Then, we apply a non-negative matrix factorization over a health issue-by-language category space. Finally, the factorized basis space is leveraged to group health issues into natural aggregations based around how they are discussed. We evaluate this framework with four months of tweets (over 200 million) and show that certain semantics correspond with whom a health mention pertains to. Our findings show that health issues related with family members, high medical cost and social support (e.g., Alzheimer's Disease, cancer, and Down syndrome) lead to tweets that are more likely to disclose another individual's health status, while tweets with more benign health issues (e.g., allergy, arthritis, and bronchitis) with biological processes (e.g., health and ingestion) and negative emotions are more likely to contain self-disclosures.