This dissertation aimed to explore the use of machine learning (ML) models in in-patient clinical settings. A literature review was conducted to identify existing guidelines and frameworks for such models, and an in-depth study was conducted on ML-based models to understand their behavior and implications on patient outcomes. A conceptual framework was developed for the structure, components, steps, and considerations necessary for an ML-based model to make sense in a clinical setting, and an ontology-guided representation of the framework was discussed. Additionally, the dissertation discusses the performance evaluation of ML models in supervised learning using time-stamped clinical data and proposed a clinically relevant temporal evaluation method. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]