The integration of artificial intelligence in healthcare has created a new era of advancements, reshaping patient care and revolutionizing public health interventions. Through artificial intelligence, healthcare providers and public health authorities can optimize interventions, leading to more precise and efficient responses that enhance patient outcomes and address public health challenges effectively. The past decade has witnessed a rapid digital transformation across industries, and healthcare is no exception. This evolution is evident in the widespread adoption of electronic health records and healthcare information systems and the integration of diverse technologies, including handheld, wearable, and smart devices.

A central challenge in this digital shift lies in representing data from multiple sources and modalities for downstream machine learning tasks. This complexity stems from the varied longitudinal or contextual events in patients' historical records, encompassing lab tests, vital signs, diagnoses, and drug administration. Additionally, the challenge extends to predictive modeling and constructing robust models that accurately classify future health events, taking into consideration heterogeneous health-related data. Electronic phenotyping, crucial for identifying fine-grained disease/patient clusters, is also a central problem when utilizing multisource and multimodal information effectively to create meaningful patient profiles. In the context of public health interventions, exemplified by crises like the COVID-19 pandemic, decision-making requires a delicate balance between optimizing intervention effectiveness and considering economic and societal well-being.

This Ph.D. thesis seeks to unravel the potential of multisource and multimodal health observational data in generating patient phenotypes and predictions for both individual health and public health surveillance. It addresses the following central question: How can multisource and multimodal observational health data be effectively harnessed, using machine learning, to enhance patient and public health? Comprising five studies, the thesis confronts challenges posed by diverse data sources and modalities, exploring strategies for creating comprehensive patient profiles, developing robust classification models, and employing clustering methods tailored to observational health data. The research seeks to provide valuable insights into integrating AI in healthcare, with a specific emphasis on the complexities of multisource and multimodal data integration. It underscores the importance of exploring heterogeneous health observational data to deepen our understanding of patient health and optimize machine learning applications. Emphasizing the intricate nature of health data, the thesis discusses careful data handling and innovative methodologies to maximize its potential impact on improving patient outcomes and informing public health strategies. The effective management of heterogeneous observational health data requires thoughtful consideration due to their varied sources and inherent complexities.