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An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit
Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learni...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871182/ https://www.ncbi.nlm.nih.gov/pubmed/35204333 http://dx.doi.org/10.3390/diagnostics12020241 |
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author | Bollepalli, Sandeep Chandra Sahani, Ashish Kumar Aslam, Naved Mohan, Bishav Kulkarni, Kanchan Goyal, Abhishek Singh, Bhupinder Singh, Gurbhej Mittal, Ankit Tandon, Rohit Chhabra, Shibba Takkar Wander, Gurpreet S. Armoundas, Antonis A. |
author_facet | Bollepalli, Sandeep Chandra Sahani, Ashish Kumar Aslam, Naved Mohan, Bishav Kulkarni, Kanchan Goyal, Abhishek Singh, Bhupinder Singh, Gurbhej Mittal, Ankit Tandon, Rohit Chhabra, Shibba Takkar Wander, Gurpreet S. Armoundas, Antonis A. |
author_sort | Bollepalli, Sandeep Chandra |
collection | PubMed |
description | Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972), heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources. |
format | Online Article Text |
id | pubmed-8871182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88711822022-02-25 An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit Bollepalli, Sandeep Chandra Sahani, Ashish Kumar Aslam, Naved Mohan, Bishav Kulkarni, Kanchan Goyal, Abhishek Singh, Bhupinder Singh, Gurbhej Mittal, Ankit Tandon, Rohit Chhabra, Shibba Takkar Wander, Gurpreet S. Armoundas, Antonis A. Diagnostics (Basel) Article Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972), heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources. MDPI 2022-01-19 /pmc/articles/PMC8871182/ /pubmed/35204333 http://dx.doi.org/10.3390/diagnostics12020241 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bollepalli, Sandeep Chandra Sahani, Ashish Kumar Aslam, Naved Mohan, Bishav Kulkarni, Kanchan Goyal, Abhishek Singh, Bhupinder Singh, Gurbhej Mittal, Ankit Tandon, Rohit Chhabra, Shibba Takkar Wander, Gurpreet S. Armoundas, Antonis A. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit |
title | An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit |
title_full | An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit |
title_fullStr | An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit |
title_full_unstemmed | An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit |
title_short | An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit |
title_sort | optimized machine learning model accurately predicts in-hospital outcomes at admission to a cardiac unit |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871182/ https://www.ncbi.nlm.nih.gov/pubmed/35204333 http://dx.doi.org/10.3390/diagnostics12020241 |
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