{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T10:24:56Z","timestamp":1792664696667,"version":"4.5.0"},"reference-count":39,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2018,11,29]],"date-time":"2018-11-29T00:00:00Z","timestamp":1543449600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Academic Emergency Medicine"],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospital\u2010level care could triage patients more efficiently to high\u2010 or low\u2010resource ED tracks. Existing tools to predict disposition for pediatric asthma use only clinical data, perform best several hours into the ED stay, and are static or score\u2010based. Machine learning offers a population\u2010specific, dynamic option that allows real\u2010time integration of available nonclinical data at triage. Our objective was to compare the performance of four common machine learning approaches, incorporating clinical data available at the time of triage with information about weather, neighborhood characteristics, and community viral load for early prediction of the need for hospital\u2010level care in pediatric asthma.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Retrospective analysis of patients ages 2 to 18 years seen at two urban pediatric EDs with asthma exacerbation over 4\u00a0years. Asthma exacerbation was defined as receiving both albuterol and systemic corticosteroids. We included patient features, measures of illness severity available in triage, weather features, and Centers for Disease Control and Prevention influenza patterns. We tested four models: decision trees, LASSO logistic regression, random forests, and gradient boosting machines. For each model, 80% of the data set was used for training and 20% was used to validate the models. The area under the receiver operating characteristic (AUC) curve was calculated for each model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>There were 29,392 patients included in the analyses: mean (\u00b1SD) age of 7.0 (\u00b14.2) years, 42% female, 77% non\u2010Hispanic black, and 76% public insurance. The AUCs for each model were: decision tree 0.72 (95% confidence interval [CI]\u00a0= 0.66\u20130.77), logistic regression 0.83 (95% CI\u00a0= 0.82\u20130.83), random forests 0.82 (95% CI\u00a0= 0.81\u20130.83), and gradient boosting machines 0.84 (95% CI\u00a0= 0.83\u20130.85). In the lowest decile of risk, only 3% of patients required hospitalization; in the highest decile this rate was 100%. After patient vital signs and acuity, age and weight, followed by socioeconomic status (SES) and weather\u2010related features, were the most important for predicting hospitalization.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital\u2010level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.<\/jats:p><\/jats:sec>","DOI":"10.1111\/acem.13655","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T09:58:45Z","timestamp":1541066325000},"page":"1463-1470","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage"],"prefix":"10.1111","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9287-1430","authenticated-orcid":false,"given":"Shilpa J.","family":"Patel","sequence":"first","affiliation":[{"name":"Division of Emergency Medicine Children's National Health System Washington DC UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8332-0004","authenticated-orcid":false,"given":"Daniel B.","family":"Chamberlain","sequence":"additional","affiliation":[{"name":"Rotageek London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James M.","family":"Chamberlain","sequence":"additional","affiliation":[{"name":"Division of Emergency Medicine Children's National Health System Washington DC UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,11,29]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2008-2233C"},{"key":"e_1_2_7_3_1","first-page":"1","article-title":"Trends in asthma prevalence, health care use, and mortality in the United States, 2001\u20102010","volume":"94","author":"Akinbami LJ","year":"2012","journal-title":"NCHS Data Brief"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2010.10.020"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2016-2785"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1553-2712.2012.01435.x"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/adc.2004.050005"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199810083391503"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD002178"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpeds.2004.06.072"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1067\/mpd.2001.116282"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1067\/mem.2002.126813"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/PEC.0b013e31818c268f"},{"issue":"476","key":"e_1_2_7_14_1","first-page":"480.e1","article-title":"The Pediatric Respiratory Assessment Measure: a valid clinical score for assessing acute asthma severity from toddlers to teenagers","volume":"152","author":"Ducharme FM","year":"2008","journal-title":"J Pediatr"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaip.2014.09.017"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/acem.12422"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1378\/chest.98.6.1355"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(16)30160-6"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0196-0644(94)70347-7"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1553-2712.2010.00775.x"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1197\/j.aem.2003.07.015"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2009.10.047"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2014.10.003"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2010.01.025"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2005.07.032"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2004.12.006"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2004.09.025"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/ppul.21236"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1111\/resp.12165"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajem.2018.06.062"},{"key":"e_1_2_7_31_1","unstructured":"Asthma Surveillance in DC Emergency Departments. 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