# Prediction of Clinical Deterioration Using a Bayesian Belief System

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $205,524

## Abstract

Project Summary/Abstract
Approximately 5-10% of hospitalized patients suffer significant clinical deterioration after admission, resulting in
either transfer to the intensive care unit (ICU) or a "code" event (i.e., cardiac or pulmonary arrest). Delayed
identification of these events result in increased morbidity and mortality. Unfortunately, existing prediction
models result in multiple false alarms for every true positive alarm that they generate. In addition with every
passing year, new monitoring systems are introduced that generate more false alarms, resulting in alarm
fatigue which has been associated with patient deaths.
The objective of this mentored career development proposal is to develop and assess novel computational
algorithms that can predict the clinical deterioration of hospitalized patients earlier and more accurately than
clinicians or conventional early warning systems, thereby allowing for timely intervention. Building upon our
experience in the hematologic malignancy subpopulation of hospitalized patients, this new effort: 1) provides a
foundation upon which to combine newer machine learning (ML) methods and clinical informatics to improve
the capabilities of the model for an individual patient or specific subgroup; 2) assesses the impact and value of
different variables from the electronic medical record (EMR) as part of the predictive model; and 3) broadens
the evaluation of this approach to additional real-world patient populations, enabling insight into the translation
of the models to clinical usage. The specific aims of this project are thus:
Specific Aims
Aim 1 To identify and extract model variables (features) from the EMR, evaluating different feature selection
 methods to optimize different predictive criterion and their impact on ML algorithms.
Aim 2 To develop an ML approach that handles multiple asynchronous data streams of longitudinal
 information from the EMR, providing predictions on clinical deterioration in real-time.
Aim 3 To explore clinician and rapid response team responses to early prediction of clinical deterioration.
With successful completion of this proposal, the prediction model will be integrated into the EMR system.
Future direction as part of a R01 proposal will involve external validation at other institutions and assessment
of clinical impact on patient care.

## Key facts

- **NIH application ID:** 9999034
- **Project number:** 5K01LM012873-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Scott B. Hu
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $205,524
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999034

## Citation

> US National Institutes of Health, RePORTER application 9999034, Prediction of Clinical Deterioration Using a Bayesian Belief System (5K01LM012873-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9999034. Licensed CC0.

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