# Synthesizing Intraoperative Multivariate Time Series with Conditional Generative Adversarial Networks

> **NIH NIH K08** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $191,554

## Abstract

Project Summary/Abstract
Patient safety is paramount in anesthesia. Intraoperative complications and hemodynamic instability are
associated with reduced long-term survival and can lead to risks such as myocardial injury, stroke, kidney
injury, and even death. Therefore, predicting and preventing intraoperative hemodynamic instability is very
important in the decision-making process of anesthesia providers. An ideal pre-operative assessment system
would predict, from patient information, all intraoperative complications and physiological changes before a
surgical procedure begins. Predicting intraoperative hemodynamic instability during surgery requires analyzing
an enormous amount of physiological data and spotting patterns in that data before adverse events occur.
However, doing this requires a large volume of high-resolution intraoperative data taken directly from the
physiological monitors in the operating room to train machine learning models, and these data currently are
unavailable. Therefore, the research goal of this proposed training program is to generate a continuous
multivariate intraoperative physiological time series that display the effects of anesthesia management using
state-of-the-art mathematic tools. The generated data can provide unlimited and realistic intraoperative data to
identify intraoperative complications and later build a real-time intraoperative clinical decision support system.
The proposed training program has two aims. Aim 1 will enable the applicant to create a data-driven objective
approach for intraoperative complication prediction and risk assessment. Key information from anesthesia pre-
op assessment will be used to generate synthetic low-resolution intraoperative physiological data. This data
will inform anesthesia providers of the type, timing, and range of a given patient’s intraoperative hemodynamic
instability and complications before surgery. Aim 2 will enable the applicant to build a virtual database that will
provide unlimited high-resolution intraoperative data to train machine learning algorithms for a future real-time
intraoperative clinical decision support system. The recorded low-resolution intraoperative data and the key
information from anesthesia pre-op assessment will be inputted into the second tool to upscale existing minute-
resolution intraoperative data to second-resolution level for data augmentation to boost the number of available
surgical cases. This K08 research program will enable the applicant to fill key knowledge gaps in applying data
science in the existing low-resolution intraoperative data in medical records and non-recorded high-resolution
intraoperative data displayed by anesthesia devices. The results will orient anesthesia providers and
researchers in the design and implementation of data-driven perioperative prediction systems over traditional
anesthesia risk assessment. Ultimately, this K08 award will provide the applicant with the senior mentorship,
skills, research ...

## Key facts

- **NIH application ID:** 10840481
- **Project number:** 5K08GM141489-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Fei Zhang
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $191,554
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840481

## Citation

> US National Institutes of Health, RePORTER application 10840481, Synthesizing Intraoperative Multivariate Time Series with Conditional Generative Adversarial Networks (5K08GM141489-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10840481. Licensed CC0.

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