{"_id":"LLHFTB9u2GLgP5c4C","bibbaseid":"steele-denaxas-shah-hemingway-luscombe-machinelearningmodelsinelectronichealthrecordscanoutperformconventionalsurvivalmodelsforpredictingpatientmortalityincoronaryarterydisease-2018","authorIDs":[],"author_short":["Steele, A. J","Denaxas, S. C","Shah, A. D","Hemingway, H.","Luscombe, N. M"],"bibdata":{"bibtype":"article","type":"article","title":"Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease","author":[{"propositions":[],"lastnames":["Steele"],"firstnames":["Andrew","J"],"suffixes":[]},{"propositions":[],"lastnames":["Denaxas"],"firstnames":["Spiros","C"],"suffixes":[]},{"propositions":[],"lastnames":["Shah"],"firstnames":["Anoop","D"],"suffixes":[]},{"propositions":[],"lastnames":["Hemingway"],"firstnames":["Harry"],"suffixes":[]},{"propositions":[],"lastnames":["Luscombe"],"firstnames":["Nicholas","M"],"suffixes":[]}],"journal":"PLoS One","volume":"13","number":"8","pages":"e0202344","year":"2018","publisher":"Public Library of Science San Francisco, CA USA","bibtex":"@article{steele2018machine,\n title={Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease},\n author={Steele, Andrew J and Denaxas, Spiros C and Shah, Anoop D and Hemingway, Harry and Luscombe, Nicholas M},\n journal={PLoS One},\n volume={13},\n number={8},\n pages={e0202344},\n year={2018},\n publisher={Public Library of Science San Francisco, CA USA}\n}\n\n","author_short":["Steele, A. J","Denaxas, S. C","Shah, A. D","Hemingway, H.","Luscombe, N. M"],"key":"steele2018machine","id":"steele2018machine","bibbaseid":"steele-denaxas-shah-hemingway-luscombe-machinelearningmodelsinelectronichealthrecordscanoutperformconventionalsurvivalmodelsforpredictingpatientmortalityincoronaryarterydisease-2018","role":"author","urls":{},"downloads":0,"html":""},"bibtype":"article","biburl":"http://denaxaslab.org/output.bib","creationDate":"2020-01-20T09:08:49.571Z","downloads":0,"keywords":[],"search_terms":["machine","learning","models","electronic","health","records","outperform","conventional","survival","models","predicting","patient","mortality","coronary","artery","disease","steele","denaxas","shah","hemingway","luscombe"],"title":"Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease","year":2018,"dataSources":["RXyKFmEdt9bbAisky"]}