{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T16:22:25Z","timestamp":1784650945581,"version":"3.55.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"8","funder":[{"name":"Division of Intramural Research of the National Institute of Allergy and Infectious Diseases"},{"DOI":"10.13039\/100000098","name":"National Institutes of Health Clinical Center","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000015","name":"US Department of Energy","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000060","name":"NIAID","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["1734521"],"award-info":[{"award-number":["1734521"]}]},{"name":"Greenwall Foundation Faculty Scholars Program"},{"DOI":"10.13039\/100008177","name":"Davis Educational Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008177","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000181","name":"US Air Force Office of Scientific Research","doi-asserted-by":"crossref","award":["FA9550-21-1-0142"],"award-info":[{"award-number":["FA9550-21-1-0142"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ofid\/ofac401","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T19:07:03Z","timestamp":1659553623000},"source":"Crossref","is-referenced-by-count":15,"title":["Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis"],"prefix":"10.1093","volume":"9","author":[{"given":"Lisa M","family":"Mayer","sequence":"first","affiliation":[{"name":"Office of Data Science and Emerging Technologies, Office of Science Management and Operations, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) , Rockville, Maryland , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey R","family":"Strich","sequence":"additional","affiliation":[{"name":"Critical Care Medicine Department, NIH Clinical Center, NIH , Bethesda, Maryland , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameer S","family":"Kadri","sequence":"additional","affiliation":[{"name":"Critical Care Medicine Department, NIH Clinical Center, NIH , Bethesda, Maryland , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michail S","family":"Lionakis","sequence":"additional","affiliation":[{"name":"Fungal Pathogenesis Section, Laboratory of Clinical Immunology & Microbiology (LCIM), NIAID, NIH , Bethesda, Maryland , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas G","family":"Evans","sequence":"additional","affiliation":[{"name":"Department of Philosophy, University of Massachusetts Lowell , Lowell, Maryland , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D Rebecca","family":"Prevots","sequence":"additional","affiliation":[{"name":"Epidemiology and Population Studies Unit, LCIM, NIAID, NIH , Bethesda, Maryland , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9928-8275","authenticated-orcid":false,"given":"Emily E","family":"Ricotta","sequence":"additional","affiliation":[{"name":"Epidemiology and Population Studies Unit, LCIM, NIAID, NIH , Bethesda, Maryland , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"2022082217182296200_ofac401-B1","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1017\/ice.2018.265","article-title":"Introduction to machine learning in digital healthcare epidemiology","volume":"39","author":"Roth","year":"2018","journal-title":"Infect Control Hosp Epidemiol"},{"key":"2022082217182296200_ofac401-B2","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.cmi.2019.09.009","article-title":"Machine learning for clinical decision support in infectious diseases: a narrative review of current applications","volume":"26","author":"Peiffer-Smadja","year":"2020","journal-title":"Clin Microbiol Infect"},{"key":"2022082217182296200_ofac401-B3","author":"Clinical decision support."},{"key":"2022082217182296200_ofac401-B4","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1186\/1471-2105-15-276","article-title":"Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks","volume":"15","author":"Kane","year":"2014","journal-title":"BMC Bioinf"},{"key":"2022082217182296200_ofac401-B5","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.3390\/ijerph15081596","article-title":"Predicting infectious disease using deep learning and big data","volume":"15","author":"Chae","year":"2018","journal-title":"Int J Environ Res Public Health"},{"key":"2022082217182296200_ofac401-B6","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1038\/s41598-021-03687-w","article-title":"Machine learning for emerging infectious disease field responses","volume":"12","author":"Chiu","year":"2022","journal-title":"Sci Rep"},{"key":"2022082217182296200_ofac401-B7","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1016\/j.cmi.2020.02.003","article-title":"Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies","volume":"26","author":"Luz","year":"2020","journal-title":"Clin Microbiol Infect"},{"key":"2022082217182296200_ofac401-B8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s12887-018-1082-2","article-title":"Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children\u2014a data-driven approach using machine-learning algorithms","volume":"18","author":"Lamping","year":"2018","journal-title":"BMC Pediatr"},{"key":"2022082217182296200_ofac401-B9","doi-asserted-by":"crossref","first-page":"10800","DOI":"10.1038\/s41598-017-09766-1","article-title":"Combining biomarkers with EMR data to identify patients in different phases of sepsis","volume":"7","author":"Taneja","year":"2017","journal-title":"Sci Rep"},{"key":"2022082217182296200_ofac401-B10","doi-asserted-by":"crossref","first-page":"e1002683","DOI":"10.1371\/journal.pmed.1002683","article-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study","volume":"15","author":"Zech","year":"2018","journal-title":"PLoS Med"},{"key":"2022082217182296200_ofac401-B11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s40537-018-0143-6","article-title":"Step away from stepwise","volume":"5","author":"Smith","year":"2018","journal-title":"J Big Data"},{"key":"2022082217182296200_ofac401-B12","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jcrc.2018.01.022","article-title":"Healthcare-associated ventriculitis and meningitis in a neuro-ICU: incidence and risk factors selected by machine learning approach","volume":"45","author":"Savin","year":"2018","journal-title":"J Crit Care"},{"key":"2022082217182296200_ofac401-B13","doi-asserted-by":"crossref","first-page":"e0005756","DOI":"10.1371\/journal.pntd.0005756","article-title":"Wetlands, wild Bovidae species richness and sheep density delineate risk of rift valley fever outbreaks in the African continent and Arabian Peninsula","volume":"11","author":"Walsh","year":"2017","journal-title":"PLoS Negl Trop Dis"},{"key":"2022082217182296200_ofac401-B14","doi-asserted-by":"crossref","first-page":"R198","DOI":"10.1186\/cc10366","article-title":"Validation and comparison of clinical prediction rules for invasive candidiasis in intensive care unit patients: a matched case-control study","volume":"15","author":"Hermsen","year":"2011","journal-title":"Crit Care"},{"key":"2022082217182296200_ofac401-B15","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1186\/s12879-016-1803-9","article-title":"Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units","volume":"16","author":"Shahin","year":"2016","journal-title":"BMC Infect Dis"},{"key":"2022082217182296200_ofac401-B16","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1093\/cid\/ciw610","article-title":"Problematic dichotomization of risk for intensive care unit (ICU)\u2013acquired invasive candidiasis: results using a risk-predictive model to categorize 3 levels of risk from a multicenter prospective cohort of Australian ICU patients","volume":"63","author":"Playford","year":"2016","journal-title":"Clin Infect Dis"},{"key":"2022082217182296200_ofac401-B17","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.jcrc.2015.03.010","article-title":"Development and validation of a clinical prediction rule for candidemia in hospitalized patients with severe sepsis and septic shock","volume":"30","author":"Guillamet","year":"2015","journal-title":"J Crit Care"},{"key":"2022082217182296200_ofac401-B18","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1093\/mmy\/myab043","article-title":"Clinical predictive models of invasive Candida infection: a systematic literature review","volume":"59","author":"Rauseo","year":"2021","journal-title":"Med Mycol"},{"key":"2022082217182296200_ofac401-B19","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.idc.2021.03.007","article-title":"Candidemia and invasive candidiasis","volume":"35","author":"McCarty","year":"2021","journal-title":"Infect Dis Clin North Am"},{"key":"2022082217182296200_ofac401-B20","doi-asserted-by":"crossref","first-page":"e01909","DOI":"10.1128\/JCM.01909-17","article-title":"Diagnosing invasive candidiasis","volume":"56","author":"Clancy","year":"2018","journal-title":"J Clin Microbiol"},{"key":"2022082217182296200_ofac401-B21","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1186\/s12913-015-1025-7","article-title":"A comparison of a multistate inpatient EHR database to the HCUP nationwide inpatient sample","volume":"15","author":"DeShazo","year":"2015","journal-title":"BMC Health Serv Res"},{"key":"2022082217182296200_ofac401-B22","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1093\/infdis\/jiaa502","article-title":"Invasive candidiasis species distribution and trends, United States, 2009\u20132017","volume":"223","author":"Ricotta","year":"2021","journal-title":"J Infect Dis"},{"key":"2022082217182296200_ofac401-B23","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","article-title":"Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data","volume":"43","author":"Quan","year":"2005","journal-title":"Med Care"},{"key":"2022082217182296200_ofac401-B24","article-title":"ICD: comorbidity calculations and tools for ICD-9 and ICD-10 codes (package version 4.0.9.9000)","author":"Wasey","year":"2018"},{"key":"2022082217182296200_ofac401-B25","author":"Clinical Classifications Software (CCS) for ICD-9-CM","year":"2017"},{"key":"2022082217182296200_ofac401-B26","article-title":"Clinical classifications software refined (CCSR) for ICD-10-PCS procedures","author":"Healthcare Cost and Utilization Project (HCUP)","year":"2021"},{"key":"2022082217182296200_ofac401-B27","author":"RxNorm"},{"key":"2022082217182296200_ofac401-B28","author":"openFDA"},{"key":"2022082217182296200_ofac401-B29","article-title":"Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles","author":"Kuhn","year":"2020"},{"key":"2022082217182296200_ofac401-B30","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit Lett"},{"key":"2022082217182296200_ofac401-B31","article-title":"R: a language and environment for statistical computing (version 4.0.3 and version 4.0.4)","author":"R Core Team","year":"2021"},{"key":"2022082217182296200_ofac401-B32","article-title":"RStudio: integrated development environment for R (version 1.3.1056)","author":"RStudio Team","year":"2020"},{"key":"2022082217182296200_ofac401-B33","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1093\/jac\/dkq216","article-title":"A multicentre study to evaluate the impact of timing of caspofungin administration on outcomes of invasive candidiasis in non-immunocompromised adult patients","volume":"65","author":"Hsu","year":"2010","journal-title":"J Antimicrob Chemother"},{"key":"2022082217182296200_ofac401-B34","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1128\/AAC.49.9.3640-3645.2005","article-title":"Delaying the empiric treatment of Candida bloodstream infection until positive blood culture results are obtained: a potential risk factor for hospital mortality","volume":"49","author":"Morrell","year":"2005","journal-title":"Antimicrob Agents Chemother"},{"key":"2022082217182296200_ofac401-B35","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1592\/phco.30.4.361","article-title":"Hospital resource utilization and costs of inappropriate treatment of candidemia","volume":"30","author":"Arnold","year":"2010","journal-title":"Pharmacother"},{"key":"2022082217182296200_ofac401-B36","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.mycmed.2018.04.011","article-title":"Epidemiology of antifungal susceptibility: review of literature","volume":"28","author":"Hadrich","year":"2018","journal-title":"J Mycol Med"},{"key":"2022082217182296200_ofac401-B37","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1093\/clinids\/20.1.115","article-title":"Importance of Candida species other than C. albicans as pathogens in oncology patients","volume":"20","author":"Wingard","year":"1995","journal-title":"Clin Infect Dis"},{"key":"2022082217182296200_ofac401-B38","author":"Elixhauser comorbidity\u2014coagulopathy"},{"key":"2022082217182296200_ofac401-B39","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1038\/nri3897","article-title":"Immune defence against Candida fungal infections","volume":"15","author":"Netea","year":"2015","journal-title":"Nat Rev Immunol"},{"key":"2022082217182296200_ofac401-B40","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1159\/000491030","article-title":"Candida: platelet interaction and platelet activity in vitro","volume":"11","author":"Eberl","year":"2019","journal-title":"J Innate Immun"},{"key":"2022082217182296200_ofac401-B41","doi-asserted-by":"crossref","first-page":"254","DOI":"10.18683\/germs.2020.1214","article-title":"Candida parapsilosis endocarditis. Report of cases and review of the literature","volume":"10","author":"Ioannou","year":"2020","journal-title":"Germs"},{"key":"2022082217182296200_ofac401-B42","author":"Elixhauser comorbidity\u2014congestive heart failure"},{"key":"2022082217182296200_ofac401-B43","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1128\/AAC.04867-14","article-title":"Candida infective endocarditis: an observational cohort study with a focus on therapy","volume":"59","author":"Arnold","year":"2015","journal-title":"Antimicrob Agents Chemother"},{"key":"2022082217182296200_ofac401-B44","doi-asserted-by":"crossref","first-page":"XXX\u2013XX","DOI":"10.1093\/ofid\/ofaa233","article-title":"Risk factors for candidemia after open heart surgery: results from a multicenter case-control study","volume":"7","author":"Giacobbe","year":"2020","journal-title":"Open Forum Infect Dis"},{"key":"2022082217182296200_ofac401-B45","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1510\/icvts.2010.257931","article-title":"Candidemia after cardiac surgery in the intensive care unit: an observational study","volume":"12","author":"Pasero","year":"2011","journal-title":"Interact Cardiovasc Thorac Surg"},{"key":"2022082217182296200_ofac401-B46","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1126\/science.aay9547","article-title":"Algorithms on regulatory lockdown in medicine","volume":"366","author":"Babic","year":"2019","journal-title":"Science"}],"container-title":["Open Forum Infectious Diseases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ofid\/advance-article-pdf\/doi\/10.1093\/ofid\/ofac401\/45228979\/ofac401.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ofid\/article-pdf\/9\/8\/ofac401\/45495761\/ofac401.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ofid\/article-pdf\/9\/8\/ofac401\/45495761\/ofac401.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T17:20:52Z","timestamp":1661188852000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ofid\/article\/doi\/10.1093\/ofid\/ofac401\/6654833"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":46,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8,2]]}},"URL":"https:\/\/doi.org\/10.1093\/ofid\/ofac401","relation":{},"ISSN":["2328-8957"],"issn-type":[{"value":"2328-8957","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,8,1]]},"published":{"date-parts":[[2022,8,1]]},"article-number":"ofac401"}}