Federated Transfer Learning
Automated Feature Selection
Mental Health Knowledge Network
CSRP Methods Core
Novel Implementation Strategies for Improved Suicide Prevention Efforts
Center for Suicide Risk Prevention and Research
The CSRP Methods Core (CSRP-MC) brings together an experienced team of biostatisticians, informaticians and app developers to leverage novel data elements, leading edge statistical and computational approaches, and innovative study designs to implement and evaluate pragmatic approaches to improve outcomes among patients at risk for suicide.
CSRP-MC Research
New opportunities for envisaging, implementing, and evaluating the efficacy and efficiencies of interventions by leveraging the vast array of data across different modalities, including patient and provider-level survey data, electronic health records (EHR) data, wearable device data and geocoded data.
Improved performance of innovative prediction modeling strategies for suicide risk and treatment response that can be used to both address the needs of the research projects herein and to optimize risk prediction for future studies.
Novel shared resources including a forum for exchanging ideas and approaches to address the most pressing analytic challenges and a commitment to disseminating open source software tools for the broader scientific research community.