A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study
Nguyen Tat Thanh, Vo Thanh Luan, Do Chau Viet, Trinh Huu Tung, Vu Thien, Nilanka Perera, Nilanka Perera, Nilanka Perera · 2024
Read A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study by Nguyen Tat Thanh, Vo Thanh Luan, Do Chau Viet, Trinh Huu Tung, Vu Thien, Nilanka Perera, Nilanka Perera, Nilanka Perera (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11623794.txt.
Description
Nguyen Tat Thanh and co-authors build a risk score to predict which children with dengue shock will need a breathing machine. Dengue can crash blood pressure in children, and those who progress to severe breathing failure die far more often. Their 2024 study reviews cases at a large children's hospital in Vietnam from 2013 to 2022.
Of 1,278 children with dengue shock, 170, about 13 percent, needed mechanical ventilation. The team pre-selected warning traits from data patterns, past literature, and clinical judgment, such as young age, heavy fluid needs, and severe bleeding. Five machine learning methods were trained on these traits. An explanation tool ranked what each model leaned on. A simple, trustworthy score could help wards prepare earlier.
Catalog record
| CREATOR | Nguyen Tat Thanh, Vo Thanh Luan, Do Chau Viet, Trinh Huu Tung, Vu Thien, Nilanka Perera, Nilanka Perera, Nilanka Perera |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 72 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |