Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review
Background: Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/12/17/5658 |
| _version_ | 1797582324224753664 |
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| author | Khandaker Reajul Islam Johayra Prithula Jaya Kumar Toh Leong Tan Mamun Bin Ibne Reaz Md. Shaheenur Islam Sumon Muhammad E. H. Chowdhury |
| author_facet | Khandaker Reajul Islam Johayra Prithula Jaya Kumar Toh Leong Tan Mamun Bin Ibne Reaz Md. Shaheenur Islam Sumon Muhammad E. H. Chowdhury |
| author_sort | Khandaker Reajul Islam |
| collection | DOAJ |
| description | Background: Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention. Methods: PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality. Results: This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding–article quality correlation. Conclusions: This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data. |
| first_indexed | 2024-03-10T23:19:27Z |
| format | Article |
| id | doaj.art-60fd745c973a4ce88f753175bb53e3bd |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T23:19:27Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-60fd745c973a4ce88f753175bb53e3bd2023-11-19T08:23:36ZengMDPI AGJournal of Clinical Medicine2077-03832023-08-011217565810.3390/jcm12175658Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic ReviewKhandaker Reajul Islam0Johayra Prithula1Jaya Kumar2Toh Leong Tan3Mamun Bin Ibne Reaz4Md. Shaheenur Islam Sumon5Muhammad E. H. Chowdhury6Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, MalaysiaDepartment of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, BangladeshDepartment of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, MalaysiaDepartment of Emergency Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, MalaysiaDepartment of Electrical and Electronic Engineering, Independent University, Bangladesh Bashundhara, Dhaka 1229, BangladeshDepartment of Biomedical Engineering, Military Institute of Science and Technology (MIST), Dhaka 1216, BangladeshDepartment of Electrical Engineering, Qatar University, Doha 2713, QatarBackground: Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention. Methods: PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality. Results: This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding–article quality correlation. Conclusions: This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.https://www.mdpi.com/2077-0383/12/17/5658sepsismachine learningdeep learningearly predictionelectronic health recordintensive care unit (ICU) |
| spellingShingle | Khandaker Reajul Islam Johayra Prithula Jaya Kumar Toh Leong Tan Mamun Bin Ibne Reaz Md. Shaheenur Islam Sumon Muhammad E. H. Chowdhury Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review Journal of Clinical Medicine sepsis machine learning deep learning early prediction electronic health record intensive care unit (ICU) |
| title | Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review |
| title_full | Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review |
| title_fullStr | Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review |
| title_full_unstemmed | Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review |
| title_short | Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review |
| title_sort | machine learning based early prediction of sepsis using electronic health records a systematic review |
| topic | sepsis machine learning deep learning early prediction electronic health record intensive care unit (ICU) |
| url | https://www.mdpi.com/2077-0383/12/17/5658 |
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