{"doi":"10.1093/jamia/ocac062","title":"Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population","abstract":"OBJECTIVE: To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs. MATERIALS AND METHODS: Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models. RESULTS: Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively. DISCUSSION: The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories. CONCLUSIONS: This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.","journal":"Journal of the American Medical Informatics Association","year":2022,"id":257397,"datarank":0.7293942381667078,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.2877283912917417,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.2877283912917417,"corpus_percentile":72.39885510946083,"corpus_rank":3569,"citation_count":18,"citer_count":14,"citers_with_citation_signal":11,"citers_with_endowment":11,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8571,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":12.5,"fair_percentile":28.767960868236013,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":832305,"name":"Yang Hu","orcid":"0009-0008-9778-5305","position":1,"is_corresponding":false},{"id":311815,"name":"Shahabeddin Sotudian","orcid":"0000-0002-5864-6192","position":2,"is_corresponding":false},{"id":713595,"name":"Zahra Zad","orcid":null,"position":3,"is_corresponding":false},{"id":274493,"name":"William G. Adams","orcid":"0000-0002-5330-0010","position":4,"is_corresponding":false},{"id":312537,"name":"Sabrina A. Assoumou","orcid":"0000-0002-5169-620X","position":5,"is_corresponding":false},{"id":253839,"name":"Heather Hsu","orcid":"0000-0002-5863-1804","position":6,"is_corresponding":false},{"id":253841,"name":"Rebecca G. Mishuris","orcid":"0000-0002-4804-3128","position":7,"is_corresponding":false},{"id":309152,"name":"Ioannis Ch. Paschalidis","orcid":"0000-0002-3343-2913","position":8,"is_corresponding":false},{"id":311814,"name":"Boran Hao","orcid":"0000-0001-7922-0513","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T00:25:30.183987Z","pmid":"35441692","pmcid":"PMC9129120","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":11.1111,"fair_a":0.0,"fair_i":0.0,"fair_r":25.0,"fair_zscore":-0.8688,"fair_rationale":{"fair_score":12.5,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":11.11,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No persistent identifier (DOI, handle, repository accession) is given for the dataset.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No repository is named as the holder of the data.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"A data use agreement with the Boston Medical Center does not allow us to make the original data available.","grounded":true,"rationale":"The data availability statement declares the data cannot be shared and offers no route (Colavizza category 0).","anchors":["Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li","Springer Nature research data policy — Data Availability Statements: standard statement templat","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes"],"scored":false,"signal":null},{"key":"f_discovery_metadata","label":"Description of the dataset as an object","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts.","grounded":true,"rationale":"The dataset content is described in running prose, not in an itemised inventory. 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