DOE · DOE-351 · record R3293
Elucidating Genetic and Environmental Risk Factors for Antipsychotic-induced Metabolic Adverse Effects Using AI
Department of Energy · PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Overview
- Development stage
- DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Does not meet any requiremnts for high Impact use case
- Start date
- 2024-10-01 (day precision)Source: 2024-10-01T00:00:00
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Health and Medical
- Operational functions
- Forecasting and prediction Derived by keyword rules; see methodology
Problem the AI is intended to solve
Identifying individuals at higher risk for adverse metabolic effects from antipsychotic medications through predictive modeling of genetic and environmental data.
Expected benefits
Improves drug safety and reduces adverse health impacts in vulnerable populations.
System outputs
Produce high-quality, contextually relevant, and coherent responses or content based on user input.
Technology
- AI classification
- Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
- System name(s)
- R&D User
- Custom-developed code
- No
- Public source code
- Not reported
Sourcing
- How it was built
- Developed in-houseSource: b) Developed in-house
- Vendor (as reported)
- Not reported
- Vendors (standardized)
- None on the standard list
A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.
Data and privacy
- Involves PII
- No
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- Yes
- Demographic features
- k) None of the above
- Training and evaluation data
- No
- Federal Data Catalog
- No
Governance
0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Pre-deployment testingNo answer reported
AI impact assessmentNo answer reported
Independent reviewNo answer reported
Ongoing monitoringNo answer reported
Operator trainingNo answer reported
Fail-safeNo answer reported
Appeal processNo answer reported
User and public consultationNo answer reported
Potential impacts and how they were identified
Not reported