TY  - CONF
AB  - Mobile health applications have a high potential to effectively support patients with heart failure throughout their therapy by offering continuous support and personalized care. Since these applications are intended for long-term use, an important factor for their effectiveness is sustaining patient motivation. While current research has identified relevant motivational factors such as perceived usefulness and ease of use, there remains a need to deepen the understanding of what sustains long-term engagement. To address this, we conducted semi-structured interviews with patients (n = 9) and medical experts (n = 5), all of whom have extensive experience with a medically approved mobile health application. This focus on experienced users allowed us to gather detailed feedback on the app’s impact over time. From a thematic analysis, we derived the five motivational factors: Personalisation, Support, Simplicity, Overview, and Habit. Participant statements also showed that it was important for patients to have a feeling of safety, control, and usefulness while interacting with mobile health applications. The results from this study give detailed insights into relevant factors in the everyday use of patients with heart failure and provide grounds for future steps regarding the motivational design of mobile health applications.
AU  - Flaucher, M.*
AU  - Pruemer, F.*
AU  - Jaeger, K.M.*
AU  - Rolny, J.*
AU  - Trissler, P.*
AU  - Eckl, S.*
AU  - Eskofier, B.M.
AU  - Leutheuser, H.*
C1  - 72136
C2  - 56385
CY  - 1601 Broadway, 10th Floor, New York, Ny, United States
TI  - Motivational factors for experienced users of mobile health applications in heart failure management.
JO  - ACM International Conference Proceeding Series
PB  - Assoc Computing Machinery
PY  - 2024
ER  - 

TY  - CONF
AB  - Obtaining high-quality data for collaborative training of machine learning models can be a challenging task due to A) regulatory concerns and B) a lack of data owner incentives to participate. The first issue can be addressed through the combination of distributed machine learning techniques (e.g. federated learning) and privacy enhancing technologies (PET), such as the differentially private (DP) model training. The second challenge can be addressed by rewarding the participants for giving access to data which is beneficial to the training model, which is of particular importance in federated settings, where the data is unevenly distributed. However, DP noise can adversely affect the underrepresented and the atypical (yet often informative) data samples, making it difficult to assess their usefulness. In this work, we investigate how to leverage gradient information to permit the participants of private training settings to select the data most beneficial for the jointly trained model. We assess two such methods, namely variance of gradients (VoG) and the privacy loss-input susceptibility score (PLIS). We show that these techniques can provide the federated clients with tools for principled data selection even in stricter privacy settings.
AU  - Usynin, D.*
AU  - Rueckert, D.*
AU  - Kaissis, G.
C1  - 70896
C2  - 55798
SP  - 179-185
TI  - Incentivising the federation: Gradient-based metrics for data selection and valuation in private decentralised training.
JO  - ACM International Conference Proceeding Series
PY  - 2024
ER  - 

TY  - CONF
AB  - While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exacerbate entrenched biases and existing health disparities in society. The area of fairness in machine learning seeks to address these issues of equity; however, appropriate approaches are context-dependent, necessitating domain-specific consideration. We focus on clinical trials, i.e., research studies conducted on humans to evaluate medical treatments. Clinical trials are a relatively under-explored application in machine learning for healthcare, in part due to complex ethical, legal, and regulatory requirements and high costs. Our aim is to provide a multi-disciplinary assessment of how fairness for machine learning fits into the context of clinical trials research and practice. We start by reviewing the current ethical considerations and guidelines for clinical trials and examine their relationship with common definitions of fairness in machine learning. We examine potential sources of unfairness in clinical trials, providing concrete examples, and discuss the role machine learning might play in either mitigating potential biases or exacerbating them when applied without care. Particular focus is given to adaptive clinical trials, which may employ machine learning. Finally, we highlight concepts that require further investigation and development, and emphasize new approaches to fairness that may be relevant to the design of clinical trials.
AU  - Chien, I.*
AU  - Deliu, N.*
AU  - Turner, R.*
AU  - Weller, A.*
AU  - Villar, S.*
AU  - Kilbertus, N.
C1  - 65620
C2  - 52745
SP  - 906-924
TI  - Multi-disciplinary fairness considerations in machine learning for clinical trials.
JO  - ACM International Conference Proceeding Series
PY  - 2022
ER  -