# Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients

> **NIH NIH K23** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $189,861

## Abstract

K23 Abstract
This application is for a K23 Mentored Clinical Scientist Research Career Development Award
entitled “Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies
for Circulatory Shock in Critically-ill Patients”.
I am a pulmonary and critical care physician at the University of Pittsburgh. This award will facilitate
my acquisition of advanced training in clinical research methods, clinical informatics, and computer
science to develop my career as a physician-scientist focused on data-driven studies of dynamic
physiology in critically-ill patients. The main objective of this proposal is to develop individualized
prediction models and treatment strategies for shock among critically-ill patients.
The aims of this study are:
1) To build machine learning-based prediction models of tachycardia and hypotension following blood
donation using non-invasive waveform data in healthy blood donor volunteers, and create baseline
features to compare with circulatory shock
2) To provide an operational definition, prediction models, differentiation of physiologic evolution
towards shock, and personalized treatment of circulatory shock in ICU patients.
Through this proposal, I will develop advanced skills in machine-learning, clinical bioinformatics, and
clinical research. I will complete a Master of Science in Biomedical Informatics to learn advanced data-
driven research methodologies to strengthen my technical training. This award will be a critical step
towards my long-term goal, being an independent physician scientist, with expertise in prediction
analytics in critical care medicine through clinical trials. I have committed mentors Dr. Michael Pinsky
(physiology, functional hemodynamics) and Dr. Gilles Clermont (critical care, algorithms, data science)
who will ensure successful completion of my proposed aims. My mentoring committee also includes an
advisor, Dr. Milos Hauskrecht - a renowned computer scientist in the Computer Science and Information
Sciences at the University of Pittsburgh. My work will be completed within the Division of Pulmonary,
Allergy, and Critical Care Medicine at the University of Pittsburgh, which has an extensive track record
of committing to the development of physician scientists.

## Key facts

- **NIH application ID:** 10039613
- **Project number:** 1K23GM138984-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Joo Heung Yoon
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $189,861
- **Award type:** 1
- **Project period:** 2020-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10039613

## Citation

> US National Institutes of Health, RePORTER application 10039613, Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients (1K23GM138984-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10039613. Licensed CC0.

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