Efficient utilization of longitudinal observations is a crucial component in proposing machine learning solutions to problems in healthcare. The temporal nature of numerous problems in this domain, such as understanding fluctuations in physiological signals through time pertinent to health status, renders this avenue of research particularly important for the intersection of Health Analytics and Artificial Intelligence (AI). In the healthcare domain, compared to other fields such as Computer Vision or Natural Language Processing, the data is often available in limited quantities. Additionally, reliable supervision signals for training inference pipelines are scarce. Furthermore, some data modalities and domains are critical to health applications which are, at the same time, considerably less investigated in machine learning research. These challenges are essential bottlenecks to address in improving the efficacy and usability of machine learning-based healthcare solutions.

In this dissertation, we investigate the role of longitudinal data in medical and health applications in various related domains. Namely, we consider the domains of 1) Physical Health: Representation learning for monitoring the physical health of an individual useful for in-patient and out-patient setups, with examples being physiological signals, activity data, and posture tracking. 2) Electronic Health Records: The multi-modal and temporal reports in different time resolutions on patients' health trajectories 3) Mental Health: Efficient multi-resolution monitoring of stress and anxiety as an example use-case with important applications, and 3) Public Health: Pandemic analytics and representation of population-level spatio-temporal health data. We suggest novel techniques to address the primary challenges in each task efficiently. In our solutions, we use approaches such as optimizing self-supervised contrastive objectives, knowledge transfer, and adversarial training so as to minimize the reliance on accurate and large-scale supervision signals. We discuss the empirical validation of our suggested solutions and shed light on some of the key future research directions.