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This Subcontract opportunity from Department Of Health And Human Services was posted on August 24, 2026. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

Predictive Model Development for Sepsis or Antimicrobial Resistance

Closed
Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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NAICS: 541714
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DEADLINE

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NAICS: 541714
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NAICS: 541714
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Solicitation # RFA-AI-28-014
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National Institutes Of Health

POSTED

4 days ago

DEADLINE

in 3 months

AI Contract Overview

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This subcontract opportunity with the National Institutes of Health Olao under the Department of Health and Human Services focuses on the development and validation of predictive models for the early detection of sepsis or antimicrobial resistance. The project requires the use of longitudinal patient data derived from real-world sources to improve diagnostic capabilities. The work is designated under NAICS code 541714 and is to be performed in Bethesda, Maryland. Interested parties must submit their responses by August 28, 2026, at 4:00 PM.

General Info

NIH subcontract for sepsis and antimicrobial resistance predictive models. Due August 28, 2026.

NAICS

541714 - Research and Development in Biotechnology (except Nanobiotechnology)

Place of Performance

Bethesda, MD, 20892, USA

Set-Aside

NONE

Documents

This scope was carved out of 26-006633.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

PINC AI Healthcare Database (PINC AI)

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Timeline

PhaseClosed
Posted

subcontract

Response Deadline

Deadline has passed

Submission Closed

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Organization & Contact Information

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AgencyDepartment Of Health And Human Services → National Institutes Of Health Olao
ContactsNo contacts available
OfficeN/A
Office AddressN/A
ContactsNo contact information available

Full Description

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Build and validate predictive models for early detection of sepsis or antimicrobial resistance using longitudinal patient data from real-world sources.

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