MiX Phenotyping
Description
Syndromic surveillance
Detailed example
The main AI system outputs will include anomaly detection for emerging trends in clinical record patterns. AI/ML outputs will also be used to find these clinical patterns by clustering, classification and topic modeling. These clinical patterns will finally be output as linear reference models that are simple enough to be interpreted and guided by human clinicians using human-in-the-loop collaboration.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Electronic medical records can provide key data for health security insights. AI/ML makes it possible to translate these records into machine-readable data, which is the first step to finding these health security insights. AI/ML will then be used to find clinical patterns across the data, such as patterns in symptoms over time and location. AI/ML will also be used to automate the process for detecting new trends (anomalies) in these clinical patterns. These health security insights can alert about potential threats, inform messaging, and provide decision support to medical and public health partners.
Controls / human review
ATO: Not reported; PIA: Not published