# Use of Machine Learning on Integrated Electronic Medical Record, Genetic and Waveform Data to Predict Perioperative Cardiorespiratory Instability

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $175,284

## Abstract

Project Summary/Abstract
 The objective of this K01 application is to give Dr. Hofer the necessary training and research experience
to establish himself as an independent investigator focused on using machine learning (ML) on a variety of
healthcare data to predict outcomes during the perioperative period. The career development activities consist
of escalating coursework on machine learning beginning with an online course of ML fundamentals and ending
with a UCLA course on ML applications in healthcare. Augmenting these courses are tutorials on the application
of these techniques to healthcare data and a research program is designed to use ML on healthcare data to
predict perioperative cardio-respiratory instability (CRI) – specifically hypotension and arrhythmia.
 To achieve these goals, Dr. Hofer has established an outstanding team of leaders in machine learning,
perioperative medicine, and clinical informatics. Dr. Maxime Cannesson, his primary mentor, is an expert in
perioperative medicine and the use of ML on physiologic signals. Dr. Eran Halperin, the co-mentor for this pro-
posal, is an expert in ML and its application to genomic and other healthcare data. Dr. Hofer has ongoing collab-
orations with Drs. Cannesson and Halperin on joint projects. Both Drs. Cannesson and Halperin have a strong
track record of mentoring individuals who have progressed to independent and productive academic careers.
Dr. Hofer will be aided by an advisory committee consisting of Dr. Douglas Bell (who will provide guidance on
integrating data from multiple sources), Dr. Mohammed Mahbouba (providing support regarding data security
and creating enterprise level analytic solutions) and Dr. Jeanine Wiener-Kronish (providing guidance on the most
relevant questions in perioperative outcome prediction).
 Challenges managing CRI have been implicated in the more than 15 million annual postoperative com-
plications, costing more than $165 billion, however no scores exist to predict CRI. This study will leverage unique
infrastructure at UCLA where whole EMR data has been combined with physiologic waveforms and genomic
data on more than 30,000 patients. This proposal will use a variety of ML techniques on these data to create
predictive models for CRI.
 In summary, this proposal will provide Dr. Hofer with both technical training in ML and hands on experi-
ence in using ML to predict perioperative outcomes. This study has the potential to create models that will help
clinicians predict, and thus avoid, perioperative instability, thereby improving patient outcomes. Additionally, this
program will provide Dr. Hofer with the tools he needs to successfully compete for a R01 focusing on using ML
models on a variety of healthcare data to predict the downstream effects of CRI – perioperative complications.

## Key facts

- **NIH application ID:** 10247089
- **Project number:** 5K01HL150318-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Ira Hofer
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $175,284
- **Award type:** 5
- **Project period:** 2020-08-25 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10247089

## Citation

> US National Institutes of Health, RePORTER application 10247089, Use of Machine Learning on Integrated Electronic Medical Record, Genetic and Waveform Data to Predict Perioperative Cardiorespiratory Instability (5K01HL150318-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10247089. Licensed CC0.

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