# Deep-CDS: Deep Learning Semantic Data Lake for Clinical Decision Support

> **NIH NIH R44** · INFOTECH SOFT, INC. · 2022 · $253,463

## Abstract

More than 5 million patients are admitted annually to United States ICUs with average mortality rate reported
ranging from 8-19%, or about 500,000 deaths annually. Sepsis is the leading cause of in-hospital mortality,
where one in three inpatient deaths are due to sepsis. Incidence of sepsis has been increasing with 1.7 million
sepsis cases and 270,000 deaths per year. Early identification of deterioration has been shown to reduce the
need for patient transfer to higher care units, reduce lengths of stay, and improve survival rates. Each hour of
delay in ICU admission has been associated with a 1.5% increased risk of ICU death and a 1% increase in risk
of hospital death. Many studies support that there is an increase in mortality rate for every hour delay in
antibiotics. Pairing patient risk stratification with appropriate levels of hospital intervention is essential to reduce
risk of mortality. Patients in intermediate units between the levels of monitoring found in floor units and ICUs are
especially difficult to predict possibility of condition deterioration. Automated monitoring, alerts, and trend
analysis are essential to identifying and proactively intervening patients under duress. Current methods of
monitoring patient health have low specificity and have significant room for improvement.
 This project will develop Deep-CDS, a cloud-based deep learning system for context-sensitive clinical
decision support in monitoring and predicting the deterioration of patient health and progression of sepsis risk
factors in real-time to improve outcomes and optimize the management of care across the hospital population.
To support the clinical care team, Deep-CDS provides team members with (a) a clinical care knowledgebase,
(b) an early warning score for deteriorating health conditions, (c) a model for predicting septic conditions, (d)
evidence-based clinical practice guidelines, and (e) visualization of patient health status trends. Deep-CDS
addresses NIGMS Priorities for Small Business Development of Sepsis Diagnostics and Therapeutics, NOT-GM-20-
028: 1) Diagnostic tools for emergency department settings; 2) Predictive clinical algorithms and point-of-care
diagnostics; 3) Technologies that combine various types of data for diagnosis of sepsis patients; and 4) Clinical
decision support, including use of artificial intelligence and machine learning approaches, to develop tools for early
recognition of sepsis, assessment of treatment responses and patient deterioration, and long-term prognosis
prediction in various care settings.

## Key facts

- **NIH application ID:** 10546333
- **Project number:** 1R44GM143996-01A1
- **Recipient organization:** INFOTECH SOFT, INC.
- **Principal Investigator:** Mansur R. Kabuka
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $253,463
- **Award type:** 1
- **Project period:** 2022-09-10 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10546333

## Citation

> US National Institutes of Health, RePORTER application 10546333, Deep-CDS: Deep Learning Semantic Data Lake for Clinical Decision Support (1R44GM143996-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10546333. Licensed CC0.

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