Develop cross-sectional AI models of relationships between diabetes severity, cognitive function, and presence of biomarkers

ID: B2AI_USECASE:28

Name: Develop cross-sectional AI models of relationships between diabetes severity, cognitive function, and presence of biomarkers.

Description: As presented by the AI-READI GC, this use case develops models capable of interpreting relationships between clinical observations of diabetes patients and their features, with a focus on cognitive function. This case does not depend upon availability of pseudotime models, unlike B2AI_USECASE:29.

Category: application

Involved in: Experimental Design, Metadata Management, Quality Control

Data Topics:

• B2AI_TOPIC:4 (Clinical Observations)

• B2AI_TOPIC:10 (EKG)

• B2AI_TOPIC:13 (Genome)

• B2AI_TOPIC:18 (mHealth)

• B2AI_TOPIC:24 (Ophthalmic Imaging)

• B2AI_TOPIC:29 (SDoH)

Enabled by:

• B2AI_USECASE:6 (Obtain patient data from wearable devices.)

• B2AI_USECASE:7 (Obtain genomics data from patients.)

• B2AI_USECASE:9 (Obtain social determinants of health data from patients.)

• B2AI_USECASE:17 (Standardize clinical record data collected from multiple sites and sources.)

Relevant to GCs:

• B2AI_ORG:114

Contributor: Harry Caufield (ORCID:0000-0001-5705-7831)