{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ming DK"],"funding":["Wellcome Trust"],"pagination":["e0000005"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9931311"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["1(1)"],"pubmed_abstract":["Background
Identifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.Methods
We developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fo"],"journal":["PLOS digital health"],"pubmed_title":["Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam."],"pmcid":["PMC9931311"],"funding_grant_id":["215010/Z/18/Z"],"pubmed_authors":["Tien NTH","Chanh HQ","Tuan NM","Trieu HT","Wills B","Trung DT","Vuong NL","Tam CT","Yacoub S","Holmes AH","Ming DK","Nguyet NM","Tam DTH","Lam PK","Chau NVV","Simmons CP","Vietnam ICU Translational Applications Laboratory (VITAL) investigators","Hernandez B","Georgiou P","Sangkaew S"],"additional_accession":[]},"is_claimable":false,"name":"Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam.","description":"Background
Identifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.Methods
We developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fo","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2026-03-27T15:47:40.589Z","creation":"2025-04-04T19:00:24.981Z"},"accession":"S-EPMC9931311","cross_references":{"pubmed":["36812518"],"doi":["10.1371/journal.pdig.0000005"]}}