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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: May 19, 2023
Date Accepted: Apr 23, 2024

The final, peer-reviewed published version of this preprint can be found here:

Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning–Based Multimodal Approach

Lee HY, Kuo PC, Qian F, Li CH, Hu JR, Hsu WT, Jhou HJ, Chen PH, Lee CH, Su CH, Liao PC, Wu IJ, Lee CC

Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning–Based Multimodal Approach

JMIR Med Inform 2024;12:e49142

DOI: 10.2196/49142

PMID: 39051152

PMCID: 11287234

Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: A Machine Learning-Based Multimodal Approach

  • Hsin-Ying Lee; 
  • Po-Chih Kuo; 
  • Frank Qian; 
  • Chien-Hung Li; 
  • Jiun-Ruey Hu; 
  • Wan-Ting Hsu; 
  • Hong-Jie Jhou; 
  • Po-Huang Chen; 
  • Cho-Hao Lee; 
  • Chin-Hua Su; 
  • Po-Chun Liao; 
  • I-Ju Wu; 
  • Chien-Chang Lee

ABSTRACT

Background:

Early identification of impending in-hospital cardiac arrest (IHCA) improves clinical outcomes but remains elusive for practicing clinicians.

Objective:

We aimed to develop a multimodal machine learning algorithm based on ensemble techniques to predict the occurrence of IHCA.

Methods:

Our model was developed by the MIMIC-IV database and validated in the eICU-CRD database. Baseline features consisting of patient demographics, presenting illness, and comorbidities were collected to train a Random Forest (RF) model. Next, vital signs were extracted to train a long short-term memory (LSTM) model. A Support Vector Machine (SVM) algorithm then stacked the results to form the final prediction model.

Results:

Of 23,909 patients in the MIMIC-IV database and 10,049 patients in the eICU database, 452 and 85 patients had incident IHCA. Up to 13 hours in advance of an IHCA event, our algorithm maintained an area under the ROC curve above 0.78. Satisfactory results were also seen in validation from two external databases and comparison to existing warning systems.

Conclusions:

Using only vital signs and information available in the electronic medical record, our model demonstrates it is possible to detect a trajectory of clinical deterioration up to 13 hours in advance. This predictive tool, which has undergone external validation, could forewarn and help clinicians identify patients in need of assessment to improve their overall prognosis.


 Citation

Please cite as:

Lee HY, Kuo PC, Qian F, Li CH, Hu JR, Hsu WT, Jhou HJ, Chen PH, Lee CH, Su CH, Liao PC, Wu IJ, Lee CC

Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning–Based Multimodal Approach

JMIR Med Inform 2024;12:e49142

DOI: 10.2196/49142

PMID: 39051152

PMCID: 11287234

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