The PRECICECAP study applies machine learning to multimodal, high-resolution data from out-of-hospital cardiac arrest patients to identify biomarker signatures predicting optimal therapeutic hypothermia duration and 90-day functional outcomes. Integrating data from the ICECAP trial, we collect detailed medication records, physiological waveforms, and imaging data. In collaboration with Moberg Analytics, we are developing a freely available software platform for standardized curation of ICU-acquired data, enabling efficient machine learning applications. Using autoencoder neural networks and supervised deep learning, we aim to refine patient-specific treatment strategies. PRECICECAP is actively enrolling and is expected to conclude in late 2025, contributing to personalized neurocritical care and broader hospital-based research applications.