This K99/R00 award will position the candidate to become an independent clinical researcher with expertise in individualized novel phenotyping and prediction of risk and resilience to psychosis and bipolar spectrum disorders (PBSD). Background. PBSD are among the most disabling conditions worldwide, evidenced by poor quality of life and premature mortality. These disorders demonstrate pluripotentiality and heterotypic continuity across clinical, cognitive, and neural phenotypes. The ability to predict transdiagnostic functional outcomes is critical for implementing precision-based interventions. Despite advances in identifying shared risk factors and pathophysiological mechanisms, translating research findings into clinical practice remains a challenge. Specific Aims. This project synthesizes data from NIMH-sponsored clinical high-risk (CHR) cohort, the North American Prodrome Longitudinal Study 2 and 3 (NAPLS-2, NAPLS-3) and Accelerating Medical Partnerships – Schizophrenia (AMP-SCZ), and translates empirical findings to electronic health records (EHR). Aim 1.1 will leverage the NAPLS cohorts to identify novel combinations of demographic, social determinants of health (SDOH), clinical, cognitive, and biological factors associated with risk, remission, and resilience using machine learning. Aim 1.2 will externally validate these models in AMP-SCZ and investigate the predictive power of digital phenotyping measures. Aim 2.1 will apply temporal deep learning and explainable artificial intelligence (XAI) to test these predictive models and align CHR variables with unique XAI-derived common data elements in the demographically-diverse Epic EHR using a longitudinal retrospective design. Aim 2.2 will design a clinician-facing nomogram for future deployment as an automated real-time predictor of PBSD as preparation for an R01 application. Training. The candidate will achieve these goals through a resource-rich institutional environment and cohesive training plan in: (1) PB