Name
Suicide Prevention – Atlas Model - data-driven approaches in clinical practice
Speakers
Executive Advisory Board (EAB) or Platinum Sponsor
EAB - DLH
Content Presented on Behalf of
Executive Advisory Board (EAB)
Services/Agencies represented
Executive Advisory Board (EAB)
Session Type
Breakout
Date
Thursday, March 6, 2025
Start Time
10:15 AM
End Time
11:15 AM
Room#/Location
Woodrow Wilson C
Focus Areas/Topics
Behavioral and Mental Health, Medical Technology, Trending/Hot Topics or Other not listed
Learning Outcomes
Following this session, the attendee will be able to summarize:
- Improved Risk Identification: Advanced data models, such as machine learning algorithms, have shown higher accuracy in identifying individuals at risk of suicide compared to traditional methods.
- Predictive Analytics: Models using predictive analytics can forecast potential risk for suicide attempts, allowing for timely interventions and support.
- Enhanced Data Utilization: Utilizing a wide range of data sources to detect warning signs and risk factors.
- Improved Risk Identification: Advanced data models, such as machine learning algorithms, have shown higher accuracy in identifying individuals at risk of suicide compared to traditional methods.
- Predictive Analytics: Models using predictive analytics can forecast potential risk for suicide attempts, allowing for timely interventions and support.
- Enhanced Data Utilization: Utilizing a wide range of data sources to detect warning signs and risk factors.
CE/CME Session
Non-CE Session
Session Currently Live