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Forecasting Survival Rates Post-Gastrointestinal Surgery: Integrating The New Japanese Association of Acute Medicine (JAAM Score) and Neural Network Classification


 
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1. Title Title of document Forecasting Survival Rates Post-Gastrointestinal Surgery: Integrating The New Japanese Association of Acute Medicine (JAAM Score) and Neural Network Classification
 
2. Creator Author's name, affiliation, country Ayu Nabila Kusuma Pradana; Kyushu University Fukuoka Japan; Indonesia
 
2. Creator Author's name, affiliation, country Aprinaldi Jasa Mantau; Kyushu Institute of Technology; Indonesia
 
2. Creator Author's name, affiliation, country Guo Jiea; Kyushu University Fukuoka Japan; Japan
 
2. Creator Author's name, affiliation, country Shuo Zhang; Kyushu University Fukuoka Japan; Japan
 
2. Creator Author's name, affiliation, country Masaharu Murata; Kyushu University Fukuoka Japan; Japan
 
2. Creator Author's name, affiliation, country Sayoko Narahara; Kyushu University Fukuoka Japan; Japan
 
2. Creator Author's name, affiliation, country Tomohiko Akahoshia; Kyushu University Fukuoka Japan; Japan
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) DIC; Gastrointestinal Gurgery; JAAM Gcore; Machine Learning; Weka Neural Network
 
4. Description Abstract Following gastrointestinal surgery, the incidence of disseminated intravascular coagulation (DIC) has a bad prognosis. Consequently, it is essential to identify the variables that can predict the prognosis of DIC. This study will examine the factors that may affect the outcome of DIC in patients who have had gastrointestinal surgery. From 2003 to 2021, 81 patients were admitted to the intensive care unit at Kyushu University Hospital following gastrointestinal surgery. DIC scores were computed using the new Japanese Association of Acute Medicine (JAAM) score from before and after surgery. Comparisons will be made between DIC values and The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a range of biochemical markers. This study utilized machine learning techniques to determine the prognosis of DIC following gastrointestinal surgery. After gastrointestinal surgery, the results of this study are anticipated to serve as an indicator for determining patient prognosis, hence increasing life expectancy and decreasing mortality rates among DIC patients.
 
5. Publisher Organizing agency, location Universitas Pendidikan Indonesia
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2024-04-30
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier https://ejournal.upi.edu/index.php/COELITE/article/view/68500
 
10. Identifier Digital Object Identifier (DOI) https://doi.org/10.17509/coelite.v3i1.68500
 
11. Source Title; vol., no. (year) Journal of Computer Engineering, Electronics and Information Technology; Vol 3, No 1 (2024): COELITE: Volume 3, Issue 1, 2024
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions Copyright (c) 2024 Journal of Computer Engineering, Electronics and Information Technology