{"doi":"10.3389/fped.2023.1035576","title":"Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data","abstract":"Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid \"one-size-fits-all\" approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatric sepsis treatments, this review provides a summary of two phenotyping strategies, empiric and machine-learning-based phenotyping based on multifaceted data underlying the complex pediatric sepsis pathobiology. Although empiric and machine-learning-based phenotypes help clinicians accelerate the diagnosis and treatments, neither empiric nor machine-learning-based phenotypes fully encapsulate all aspects of pediatric sepsis heterogeneity. To facilitate accurate delineations of pediatric sepsis phenotypes for precision medicine approach, methodological steps and challenges are further highlighted.","journal":"Frontiers in Pediatrics","year":2023,"id":343885,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9551,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1081903,"name":"Rebecca I. Caldino Bohn","orcid":"0009-0009-5584-8483","position":1,"is_corresponding":false},{"id":498784,"name":"Aditya Sriram","orcid":"0000-0001-7303-9529","position":2,"is_corresponding":false},{"id":390580,"name":"Kate F. Kernan","orcid":"0000-0002-6337-841X","position":3,"is_corresponding":false},{"id":34789,"name":"Joseph A. Carcillo","orcid":"0000-0001-8920-4330","position":4,"is_corresponding":false},{"id":409930,"name":"Soyeon Kim","orcid":"0000-0003-1573-2733","position":5,"is_corresponding":false},{"id":409933,"name":"Hyun Jung Park","orcid":"0000-0002-8324-2624","position":6,"is_corresponding":false},{"id":833426,"name":"Yidi Qin","orcid":null,"position":0,"is_corresponding":true}],"reference_count":98,"raw_metadata":null,"created_at":"2026-07-19T01:11:26.556051Z","pmid":"36793336","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}