Archives of Academic Emergency Medicine

Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emergency Medicine Australasia. 2018;30(6):870-4.

Bonaccorso G. Machine learning algorithms: Packt Publishing Ltd; 2017.

Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M. Logistic regression: Springer; 2002.

Cortes C, Vapnik V. Support-vector networks. Machine learning. 1995;20(3):273-97.

Koning M, Smith C. Decision Trees and Random Forests: A Visual Introduction for Beginners: Amazon Digital Services LLC - Kdp Print Us; 2017.

Natekin A, Knoll A. Gradient boosting machines, a tutorial. Frontiers in Neurorobotics. 2013;7(21).

LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436.

Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, et al. Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Canadian journal of kidney health and disease. 2018;5:2054358118776326.

Li Y, Yao L, Mao C, Srivastava A, Jiang X, Luo Y, editors. Early prediction of acute kidney injury in critical care setting using clinical notes. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2018: IEEE.

Kate RJ, Perez RM, Mazumdar D, Pasupathy KS, Nilakantan V. Prediction and detection models for acute kidney injury in hospitalized older adults. BMC medical informatics and decision making. 2016;16(1):39.

Najafi I, Van Biesen W, Sharifi A, Hoseini M, Rashid Farokhi F, Sanadgol H, et al. Early detection of patients at high risk for acute kidney injury during disasters: development of a scoring system based on the Bam earthquake experience. J Nephrology 2008. 2008.

Koyner JL, Carey KA, Edelson DP, Churpek MM. The development of a machine learning inpatient acute kidney injury prediction model. Critical care medicine. 2018;46(7):1070-7.

Kanzawa M, Paul R, Weng N. Prediction of Acute Kidney Injury in the ICU.

Ye Y, Tsui F, Wagner M, Espino JU, Li Q. Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. Journal of the American Medical Informatics Association. 2014;21(5):815-23.

Pineda AL, Ye Y, Visweswaran S, Cooper GF, Wagner MM, Tsui FR. Comparison of machine learning classifiers for influenza detection from emergency department free-text reports. Journal of biomedical informatics. 2015;58:60-9.

Elkin PL, Froehling DA, Wahner-Roedler DL, Brown SH, Bailey KR. Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes. Annals of Internal Medicine. 2012;156(1_Part_1):11-8.

Taylor RA, Moore CL, Cheung K-H, Brandt C. Predicting urinary tract infections in the emergency department with machine learning. PLoS One. 2018;13(3):e0194085.

Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ open. 2018;8(1):e017833.

Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One. 2017;12(4):e0174708.

Goto T, Camargo Jr CA, Faridi MK, Yun BJ, Hasegawa K. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. The American journal of emergency medicine. 2018;36(9):1650-4.

Swaminathan S, Qirko K, Smith T, Corcoran E, Wysham NG, Bazaz G, et al. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS One. 2017;12(11):e0188532.

Farion K, Michalowski W, Wilk S, O’Sullivan D, Matwin S. A tree-based decision model to support prediction of the severity of asthma exacerbations in children. Journal of medical systems. 2010;34(4):551-62.

Farion KJ, Wilk S, Michalowski W, O’Sullivan D, Sayyad-Shirabad J. Comparing predictions made by a prediction model, clinical score, and physicians. Applied clinical informatics. 2013;4(03):376-91.

Elikashvili I, Spina L, Ayalin T, Cheng J, Morley EJ, Singh J. an Evidence-based Review Of Acute Appendicitis In Childhood. Pediatric Emergency Medicine Practice. 2012;9(3):1-11.

Deleger L, Brodzinski H, Zhai H, Li Q, Lingren T, Kirkendall ES, et al. Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department. Journal of the American Medical Informatics Association. 2013;20(e2):e212-e20.

Chapman WW, Dowling JN, Wagner MM. Classification of emergency department chief complaints into 7 syndromes: a retrospective analysis of 527,228 patients. Annals of emergency medicine. 2005;46(5):445-55.

Cameron A, Ireland AJ, McKay GA, Stark A, Lowe DJ. Predicting admission at triage: are nurses better than a simple objective score? Emerg Med J. 2017;34(1):2-7.

Barak-Corren Y, Fine AM, Reis BY. Early prediction model of patient hospitalization from the pediatric emergency department. Pediatrics. 2017;139(5):e20162785.

Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018;13(7):e0201016.

Graham B, Bond R, Quinn M, Mulvenna M. Using data mining to predict hospital admissions from the emergency department. IEEE Access. 2018;6:10458-69.

Sun Y, Heng BH, Tay SY, Seow E. Predicting hospital admissions at emergency department triage using routine administrative data. Academic Emergency Medicine. 2011;18(8):844-50.

Peck JS, Gaehde SA, Nightingale DJ, Gelman DY, Huckins DS, Lemons MF, et al. Generalizability of a simple approach for predicting hospital admission from an emergency department. Academic Emergency Medicine. 2013;20(11):1156-63.

Rojas JC, Carey KA, Edelson DP, Venable LR, Howell MD, Churpek MM. Predicting intensive care unit readmission with machine learning using electronic health record data. Annals of the American Thoracic Society. 2018;15(7):846-53.

Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, et al. Prediction of Inâ€hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach. Academic emergency medicine. 2016;23(3):269-78.

Barnes S, Hamrock E, Toerper M, Siddiqui S, Levin S. Real-time prediction of inpatient length of stay for discharge prioritization. Journal of the American Medical Informatics Association. 2015;23(e1):e2-e10.

Lucini FR, Fogliatto FS, da Silveira GJ, Neyeloff JL, Anzanello MJ, Kuchenbecker RdS, et al. Text mining approach to predict hospital admissions using early medical records from the emergency department. International journal of medical informatics. 2017;100:1-8.

Tang F, Xiao C, Wang F, Zhou J. Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open. 2018;1(1):87-98.

Liu W, Wang Z, Liu X, Zeng N, Bell D. A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Transactions on Evolutionary Computation. 2018.

Abad-Grau MM, Ierache J, Cervino C, Sebastiani P. Evolution and challenges in the design of computational systems for triage assistance. Journal of biomedical informatics. 2008;41(3):432-41.

Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Annals of emergency medicine. 2018;71(5):565-74. e2.

Olivia D, Nayak A, Balachandra M, editors. Machine Learning Based Electronic Triage for Emergency Department. International Conference on Applications and Techniques in Information Security; 2018: Springer.

Mondragon N, Istrate D, Wegrzyn-Wolska K, Garcia JC, Sanchez JC. Patient classification algorithm at urgency care area of a hospital based on the triage system. Journal of Medical Informatics & Technologies. 2013;22.