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An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

1
Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA
2
Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar 140001, India
3
Department of Cardiology, Hero DMC Heart Institute, Unit of Dayanand Medical College and Hospital, Ludhiana 141001, India
4
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Henk A. Marquering
Diagnostics 2022, 12(2), 241; https://doi.org/10.3390/diagnostics12020241
Received: 30 December 2021 / Revised: 14 January 2022 / Accepted: 14 January 2022 / Published: 19 January 2022
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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. View Full-Text
Keywords: machine learning; mortality; duration of stay; heart failure; STEMI; pulmonary embolism machine learning; mortality; duration of stay; heart failure; STEMI; pulmonary embolism
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MDPI and ACS Style

Bollepalli, S.C.; Sahani, A.K.; Aslam, N.; Mohan, B.; Kulkarni, K.; Goyal, A.; Singh, B.; Singh, G.; Mittal, A.; Tandon, R.; Chhabra, S.T.; Wander, G.S.; Armoundas, A.A. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics 2022, 12, 241. https://doi.org/10.3390/diagnostics12020241

AMA Style

Bollepalli SC, Sahani AK, Aslam N, Mohan B, Kulkarni K, Goyal A, Singh B, Singh G, Mittal A, Tandon R, Chhabra ST, Wander GS, Armoundas AA. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics. 2022; 12(2):241. https://doi.org/10.3390/diagnostics12020241

Chicago/Turabian Style

Bollepalli, Sandeep C., Ashish K. Sahani, Naved Aslam, Bishav Mohan, Kanchan Kulkarni, Abhishek Goyal, Bhupinder Singh, Gurbhej Singh, Ankit Mittal, Rohit Tandon, Shibba T. Chhabra, Gurpreet S. Wander, and Antonis A. Armoundas. 2022. "An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit" Diagnostics 12, no. 2: 241. https://doi.org/10.3390/diagnostics12020241

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