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Case Report Open Access November 16, 2022

AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems

Tulasi Naga Subhash Polineni 1,*, Chandrashekar Pandugula 2 and Venkata Krishna Azith Teja Ganti 3
1
Sr Data Engineer, Exelon, Baltimore MD, USA
2
Sr Data Engineer, Lowes Inc NC, USA
3
Sr Data Support Engineer, Microsoft Corporation, Charlotte NC, USA
Publihed in: Current Research in Public Health (Volume 2, Issue 1, 2022)
Page(s): 32-46
DOI: 10.31586/gjmcr.2022.1225
Received
August 26, 2022
Revised
October 12, 2022
Accepted
November 11, 2022
Published
November 16, 2022
Keywords
Artificial Intelligence; Post-Operative Complications; Prediction Models; Cardiac Complications; Pulmonary Complications; Thromboembolic Complications; Septic Complications; Elective Surgery; Non-Cardiac Surgery; Non-Ambulatory Surgery; Electronic Health Records; Neural Networks; Surgical Encounters; Risk Assessment; High-Risk Patients; Medical Centers; Structured Data; Unstructured Data; Patient Outcomes; Clinical Decision-Making
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2022. Published by Scientific Publications
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Cite This Article

APA Style
Polineni, T. N. S. , Pandugula, C. , & Ganti, V. K. A. T. (2022). AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems. Current Research in Public Health, 2(1), 32-46. https://doi.org/10.31586/gjmcr.2022.1225
ACS Style
Polineni, T. N. S. ; Pandugula, C. ; Ganti, V. K. A. T. AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems. Current Research in Public Health 2022 2(1), 32-46. https://doi.org/10.31586/gjmcr.2022.1225
Chicago/Turabian Style
Polineni, Tulasi Naga Subhash, Chandrashekar Pandugula, and Venkata Krishna Azith Teja Ganti. 2022. "AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems". Current Research in Public Health 2, no. 1: 32-46. https://doi.org/10.31586/gjmcr.2022.1225
AMA Style
Polineni TNS, Pandugula C, Ganti VKAT. AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems. Current Research in Public Health. 2022; 2(1):32-46. https://doi.org/10.31586/gjmcr.2022.1225 
@Article{crph1225,
AUTHOR = {Polineni, Tulasi Naga Subhash and Pandugula, Chandrashekar and Ganti, Venkata Krishna Azith Teja},
TITLE = {AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems},
JOURNAL = {Current Research in Public Health},
VOLUME = {2},
YEAR = {2022},
NUMBER = {1},
PAGES = {32-46},
URL = {https://www.scipublications.com/journal/index.php/GJMCR/article/view/1225},
ISSN = {2831-5162},
DOI = {10.31586/gjmcr.2022.1225},
ABSTRACT = {Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].},
}
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%A Ganti, Venkata Krishna Azith Teja
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%U https://www.scipublications.com/journal/index.php/GJMCR/article/view/1225

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AB  - Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].
DO  - AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems
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