An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm
Abstract Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in app...
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Nature Portfolio
2021
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oai:doaj.org-article:770683cc8bca41dfbf6c5f85c34915872021-12-02T16:46:35ZAn approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm10.1038/s41598-021-96081-52045-2322https://doaj.org/article/770683cc8bca41dfbf6c5f85c34915872021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96081-5https://doaj.org/toc/2045-2322Abstract Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.Abha Umesh SardesaiAmbalika Sanjeev TanakSubramaniam KrishnanDeborah A. StriegelKevin L. SchullyDanielle V. ClarkSriram MuthukumarShalini PrasadNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Abha Umesh Sardesai Ambalika Sanjeev Tanak Subramaniam Krishnan Deborah A. Striegel Kevin L. Schully Danielle V. Clark Sriram Muthukumar Shalini Prasad An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm |
| description |
Abstract Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making. |
| format |
article |
| author |
Abha Umesh Sardesai Ambalika Sanjeev Tanak Subramaniam Krishnan Deborah A. Striegel Kevin L. Schully Danielle V. Clark Sriram Muthukumar Shalini Prasad |
| author_facet |
Abha Umesh Sardesai Ambalika Sanjeev Tanak Subramaniam Krishnan Deborah A. Striegel Kevin L. Schully Danielle V. Clark Sriram Muthukumar Shalini Prasad |
| author_sort |
Abha Umesh Sardesai |
| title |
An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm |
| title_short |
An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm |
| title_full |
An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm |
| title_fullStr |
An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm |
| title_full_unstemmed |
An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm |
| title_sort |
approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/770683cc8bca41dfbf6c5f85c3491587 |
| work_keys_str_mv |
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