UCLA

The escalating cost of healthcare and the growing prevalence of chronic diseases have created an urgent need for new solutions. Machine learning has the potential to revolutionize healthcare by providing more personalized and efficient care. However, there are unique challenges associated with applying machine learning in healthcare. Privacy concerns prevent data sharing across institutions, which limits available training data, and collecting individual features for patients may be invasive or expensive, as they may involve lab tests or medical imaging. In addition, machine learning models must be explainable so that medical professionals can understand how they arrive at a certain diagnosis. Despite these challenges, machine learning presents new opportunities in healthcare, both in hospitals and in remote health monitoring. In hospitals, machine learning can improve efficiency by assisting medical professionals with patient diagnoses, while in remote health monitoring, the vast quantities of data from personal and wearable devices open new opportunities for preventative care. However, processing and extracting meaningful insights from healthcare data require novel techniques. This dissertation investigates solutions for personalized healthcare in both hospital and remote healthcare settings, including adaptive data acquisition, unsupervised medical image segmentation, and remote health monitoring algorithms and applications. Overall, these solutions have the potential to improve patient outcomes and reduce healthcare costs.