PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Clinical and Experimental Emergency Medicine10.15441/ceem.19.052202073197-205Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning modelsSoo Yeon Kang, Won Chul Cha, Junsang Yoo, Taerim Kim, Joo Hyun Park, Hee Yoon, Sung Yeon Hwang, Min Seob Sim, Ik Joon Jo, Tae Gun Shinhttp://ceemjournal.org/upload/pdf/ceem-19-052.pdf, http://ceemjournal.org/journal/view.php?doi=10.15441/ceem.19.052, http://ceemjournal.org/upload/pdf/ceem-19-052.pdf
Respirology10.1111/resp.13207_503201722273-273PREDICTING THE MORTALITY OF PNEUMONIA PATIENTS VISITING THE EMERGENCY DEMARTMENT THROUGH MACHINE LEARNINGhttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fresp.13207_503, http://onlinelibrary.wiley.com/wol1/doi/10.1111/resp.13207_503/fullpdf
The Journal of Emergency Medicine10.1016/j.jemermed.2017.12.0292018542267-268Predicting 30-day mortality for patients with acute heart failure in the emergency departmentDaniel Adamshttps://api.elsevier.com/content/article/PII:S0736467917311952?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0736467917311952?httpAccept=text/plain
BMC Emergency Medicine10.1186/s12873-021-00475-72021211Predicting mortality among septic patients presenting to the emergency department–a cross sectional analysis using machine learningAdam Karlsson, Willem Stassen, Amy Loutfi, Ulrika Wallgren, Eric Larsson, Lisa Kurlandhttps://link.springer.com/content/pdf/10.1186/s12873-021-00475-7.pdf, https://link.springer.com/article/10.1186/s12873-021-00475-7/fulltext.html, https://link.springer.com/content/pdf/10.1186/s12873-021-00475-7.pdf
JAMIA Open10.1093/jamiaopen/ooz019201923346-352Predicting 72-hour and 9-day return to the emergency department using machine learningWoo Suk Hong, Adrian Daniel Haimovich, Richard Andrew Taylorhttp://academic.oup.com/jamiaopen/article-pdf/2/3/346/32298691/ooz019.pdf, http://academic.oup.com/jamiaopen/article-pdf/2/3/346/32298691/ooz019.pdf
Chinese Journal of Traumatology10.1016/j.cjtee.2019.10.0042019226316-322Sepsis patient evaluation emergency department (SPEED) score & mortality in emergency department sepsis (MEDS) score in predicting 28-day mortality of emergency sepsis patientsAdel Hamed Elbaih, Zaynab Mohammed Elsayed, Rasha Mahmoud Ahmed, Sara Ahmed Abd-elwahedhttps://api.elsevier.com/content/article/PII:S1008127519303396?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1008127519303396?httpAccept=text/plain
Annals of Emergency Medicine10.1016/j.annemergmed.2008.01.0992008514511131: Estimated Glomerular Filtration and 30-Day Mortality in Emergency Department Observation Unit Heart Failure PatientsJ.F. Styron, W.F. Peacockhttps://api.elsevier.com/content/article/PII:S0196064408001819?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0196064408001819?httpAccept=text/plain
Nursing in Critical Care10.1111/nicc.1213720142149-15A simplified emergency trauma score for predicting mortality in emergency settingMargaret SY Yuen, Stephen KF Mann, Daniel HK Chowhttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fnicc.12137, http://onlinelibrary.wiley.com/wol1/doi/10.1111/nicc.12137/fullpdf
The Journal of Emergency Medicine10.1016/j.jemermed.2019.11.0062019575752-753Emergency Department Triage Prediction of Clinical Outcomes Using Machine Learning ModelsSeth Bartholomew, Amanda Younghttps://api.elsevier.com/content/article/PII:S0736467919309862?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0736467919309862?httpAccept=text/plain
Clinical and Experimental Emergency Medicine10.15441/ceem.17.2622019611-8Efficacy of quick Sequential Organ Failure Assessment with lactate concentration for predicting mortality in patients with community-acquired pneumonia in the emergency departmentHwan Song, Hyung Gi Moon, Soo Hyun Kimhttp://ceemjournal.org/upload/pdf/ceem-17-262.pdf, http://ceemjournal.org/journal/view.php?doi=10.15441/ceem.17.262, http://www.ceemjournal.org/upload/pdf/ceem-17-262.pdf