Proactive fall prevention in care homes is a major healthcare challenge, currently limited by the difficulty of modeling complex real-world data. This study leverages a unique, large-scale datasbase collected via a mobile care monitoring application, capturing the daily living activities of over 140,000 residents in multiple UK care homes. To utilize these irregular and heterogeneous event streams effectively, we propose a continuous prediction framework that transforms raw records into symbolic time intervals using temporal abstraction and mines frequent Time Interval Related Patterns (TIRPs). The Fully Continuous Prediction Model (FCPM) models TIRPs that end with a fall, so in real time based on the unfolding patterns it estimates fall probability the completion of these patterns, meaning that the fall will occur. In this study we propose two enhancements for the FCPM, the use of three general temporal relations, and a supervised state abstraction method. A rigorous evaluation on a large real life database, shows that the use of the three temporal reletions perfoms significnatly better than Allen's seven relations, and the use of the TD4C abstraction performs better than EWD, EFD, and SAX. Finally, the FCPM performs better in comparison to the baseline models, including the deep learning sequence based models (LSTM-FCN, ResNet, TFT) and the feature-based classifiers (XGBoost, ROCKET).