Mortality prediction in critically ill patients using machine learning score
Scoring tools are often used to predict patient severity of illness and mortality in intensive care units (ICU). Accurate prediction is important in the clinical setting to ensure efficient management of limited resources. However, studies have shown that the scoring tools currently in use are limit...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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Institute of Physics Publishing
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37356/1/Mortality%20prediction%20in%20critically%20ill%20patients%20using%20machine%20learning%20score.pdf |
| _version_ | 1796995662660763648 |
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| author | Fatimah, Dzaharudin Azrina, Md Ralib Ummu Kulthum, Jamaludin Mohd Basri, Mat Nor Afidalina, Tumian Har, Lim Chiew Ceng, T. C. |
| author_facet | Fatimah, Dzaharudin Azrina, Md Ralib Ummu Kulthum, Jamaludin Mohd Basri, Mat Nor Afidalina, Tumian Har, Lim Chiew Ceng, T. C. |
| author_sort | Fatimah, Dzaharudin |
| collection | UMP |
| description | Scoring tools are often used to predict patient severity of illness and mortality in intensive care units (ICU). Accurate prediction is important in the clinical setting to ensure efficient management of limited resources. However, studies have shown that the scoring tools currently in use are limited in predictive value. The aim of this study is to develop a machine learning (ML) based algorithm to improve the prediction of patient mortality for Malaysian ICU and evaluate the algorithm to determine whether it improves mortality prediction relative to the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment Score (SOFA) scores. Various types of classification algorithms in machine learning were investigated using common clinical variables extracted from patient records obtained from four major ICUs in Malaysia to predict mortality and assign patient mortality risk scores. The algorithm was validated with data obtained from a retrospective study on ICU patients in Malaysia. The performance was then assessed relative to prediction based on the SAPS II and SOFA scores by comparing the prediction accuracy, area under the curve (AUC) and sensitivity. It was found that the Decision Tree with SMOTE 500% with the inclusion of both SAPS II and SOFA score in the dataset could provide the highest confidence in categorizing patients into two outcomes: death and survival with a mean AUC of 0.9534 and a mean sensitivity 88.91%. The proposed ML score were found to have higher predictive power compared with ICU severity scores; SOFA and SAPS II. |
| first_indexed | 2024-03-06T13:05:40Z |
| format | Conference or Workshop Item |
| id | UMPir37356 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:05:40Z |
| publishDate | 2020 |
| publisher | Institute of Physics Publishing |
| record_format | dspace |
| spelling | UMPir373562023-11-06T01:09:51Z http://umpir.ump.edu.my/id/eprint/37356/ Mortality prediction in critically ill patients using machine learning score Fatimah, Dzaharudin Azrina, Md Ralib Ummu Kulthum, Jamaludin Mohd Basri, Mat Nor Afidalina, Tumian Har, Lim Chiew Ceng, T. C. T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Scoring tools are often used to predict patient severity of illness and mortality in intensive care units (ICU). Accurate prediction is important in the clinical setting to ensure efficient management of limited resources. However, studies have shown that the scoring tools currently in use are limited in predictive value. The aim of this study is to develop a machine learning (ML) based algorithm to improve the prediction of patient mortality for Malaysian ICU and evaluate the algorithm to determine whether it improves mortality prediction relative to the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment Score (SOFA) scores. Various types of classification algorithms in machine learning were investigated using common clinical variables extracted from patient records obtained from four major ICUs in Malaysia to predict mortality and assign patient mortality risk scores. The algorithm was validated with data obtained from a retrospective study on ICU patients in Malaysia. The performance was then assessed relative to prediction based on the SAPS II and SOFA scores by comparing the prediction accuracy, area under the curve (AUC) and sensitivity. It was found that the Decision Tree with SMOTE 500% with the inclusion of both SAPS II and SOFA score in the dataset could provide the highest confidence in categorizing patients into two outcomes: death and survival with a mean AUC of 0.9534 and a mean sensitivity 88.91%. The proposed ML score were found to have higher predictive power compared with ICU severity scores; SOFA and SAPS II. Institute of Physics Publishing 2020-06-05 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/37356/1/Mortality%20prediction%20in%20critically%20ill%20patients%20using%20machine%20learning%20score.pdf Fatimah, Dzaharudin and Azrina, Md Ralib and Ummu Kulthum, Jamaludin and Mohd Basri, Mat Nor and Afidalina, Tumian and Har, Lim Chiew and Ceng, T. C. (2020) Mortality prediction in critically ill patients using machine learning score. In: IOP Conference Series: Materials Science and Engineering; 5th International Conference on Mechanical Engineering Research 2019, ICMER 2019 , 30-31 July 2019 , Kuantan. pp. 1-12., 788 (012029). ISSN 1757-8981 https://doi.org/10.1088/1757-899X/788/1/012029 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Fatimah, Dzaharudin Azrina, Md Ralib Ummu Kulthum, Jamaludin Mohd Basri, Mat Nor Afidalina, Tumian Har, Lim Chiew Ceng, T. C. Mortality prediction in critically ill patients using machine learning score |
| title | Mortality prediction in critically ill patients using machine learning score |
| title_full | Mortality prediction in critically ill patients using machine learning score |
| title_fullStr | Mortality prediction in critically ill patients using machine learning score |
| title_full_unstemmed | Mortality prediction in critically ill patients using machine learning score |
| title_short | Mortality prediction in critically ill patients using machine learning score |
| title_sort | mortality prediction in critically ill patients using machine learning score |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/37356/1/Mortality%20prediction%20in%20critically%20ill%20patients%20using%20machine%20learning%20score.pdf |
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