{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:36:10Z","timestamp":1790843770999,"version":"4.1.0"},"reference-count":30,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["arthroplastyjournal.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Arthroplasty"],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1016\/j.arth.2019.05.048","type":"journal-article","created":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T13:27:47Z","timestamp":1559568467000},"page":"2235-2241.e1","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":38,"title":["Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?"],"prefix":"10.1016","volume":"34","author":[{"given":"Jaret M.","family":"Karnuta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio M.","family":"Navarro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heather S.","family":"Haeberle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Matthew","family":"Helm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atul F.","family":"Kamath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan L.","family":"Schaffer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viktor E.","family":"Krebs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1704-9156","authenticated-orcid":false,"given":"Prem N.","family":"Ramkumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.arth.2019.05.048_bib1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4258\/hir.2018.24.1.29","article-title":"Comparison of models for the prediction of medical costs of spinal fusion in Taiwan diagnosis-related groups by machine learning algorithms","volume":"24","author":"Kuo","year":"2018","journal-title":"Healthc Inform Res"},{"key":"10.1016\/j.arth.2019.05.048_bib2","doi-asserted-by":"crossref","first-page":"769","DOI":"10.2106\/JBJS.N.00841","article-title":"Improving value in musculoskeletal care delivery","volume":"97","author":"Wei","year":"2015","journal-title":"J\u00a0Bone Joint Surg Am"},{"key":"10.1016\/j.arth.2019.05.048_bib3","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: review, opportunities and challenges","volume":"19","author":"Miotto","year":"2017","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.arth.2019.05.048_bib4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.arth.2019.05.048_bib5","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1056\/NEJMp1500523","article-title":"A\u00a0new initiative on precision medicine","volume":"372","author":"Collins","year":"2015","journal-title":"N\u00a0Engl J Med"},{"key":"10.1016\/j.arth.2019.05.048_bib6","doi-asserted-by":"crossref","DOI":"10.1097\/BOT.0000000000001454","article-title":"Bundled care for hip fractures: a machine learning approach to an untenable patient-specific payment model","author":"Karnuta","year":"2019","journal-title":"J\u00a0Orthop Trauma"},{"key":"10.1016\/j.arth.2019.05.048_bib7","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.arth.2019.05.048_bib8","doi-asserted-by":"crossref","first-page":"e35781","DOI":"10.1371\/journal.pone.0035781","article-title":"Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery","volume":"7","author":"Shi","year":"2012","journal-title":"PLoS One"},{"key":"10.1016\/j.arth.2019.05.048_bib9","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1093\/jamia\/ocv106","article-title":"Real-time prediction of inpatient length of stay for discharge prioritization","volume":"23","author":"Barnes","year":"2015","journal-title":"J\u00a0Am Med Inform Assoc"},{"key":"10.1016\/j.arth.2019.05.048_bib10","doi-asserted-by":"crossref","first-page":"e000158","DOI":"10.1136\/bmjoq-2017-000158","article-title":"Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units","volume":"6","author":"McCoy","year":"2017","journal-title":"BMJ Open Qual"},{"key":"10.1016\/j.arth.2019.05.048_bib11","doi-asserted-by":"crossref","first-page":"e0155705","DOI":"10.1371\/journal.pone.0155705","article-title":"Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications","volume":"11","author":"Thottakkara","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.arth.2019.05.048_bib12","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jbi.2015.05.016","article-title":"A\u00a0comparison of models for predicting early hospital readmissions","volume":"56","author":"Futoma","year":"2015","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.arth.2019.05.048_bib13","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.arth.2018.12.030","article-title":"Development and validation of a machine learning algorithm after primary total hip arthroplasty: applications to length of stay and payment models","volume":"34","author":"Ramkumar","year":"2019","journal-title":"J\u00a0Arthroplasty"},{"key":"10.1016\/j.arth.2019.05.048_bib14","doi-asserted-by":"crossref","first-page":"3617","DOI":"10.1016\/j.arth.2018.08.028","article-title":"Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model","volume":"33","author":"Navarro","year":"2018","journal-title":"J\u00a0Arthroplasty"},{"key":"10.1016\/j.arth.2019.05.048_bib15","first-page":"74","article-title":"Predicting the outcome for patients in a heart transplantation queue using deep learning","volume":"2017","author":"Medved","year":"2017","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.arth.2019.05.048_bib16","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0167-9236(01)00121-X","article-title":"Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural networks, and genetic algorithm: a case study in romantic decision support","volume":"32","author":"Leigh","year":"2002","journal-title":"Decis Support Syst"},{"key":"10.1016\/j.arth.2019.05.048_bib17","doi-asserted-by":"crossref","first-page":"E6","DOI":"10.3171\/2018.8.FOCUS18340","article-title":"Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders","volume":"45","author":"Karhade","year":"2018","journal-title":"Neurosurg Focus"},{"key":"10.1016\/j.arth.2019.05.048_bib18","doi-asserted-by":"crossref","first-page":"11591","DOI":"10.1073\/pnas.1806905115","article-title":"Deep neural network improves fracture detection by clinicians","volume":"115","author":"Lindsey","year":"2018","journal-title":"Proc Natl Acad Sci U S A"},{"key":"10.1016\/j.arth.2019.05.048_bib19","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s00256-018-3016-3","article-title":"Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network","volume":"48","author":"Urakawa","year":"2019","journal-title":"Skeletal Radiol"},{"key":"10.1016\/j.arth.2019.05.048_bib20","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","article-title":"Artificial intelligence for analyzing orthopedic trauma radiographs","volume":"88","author":"Olczak","year":"2017","journal-title":"Acta Orthop"},{"key":"10.1016\/j.arth.2019.05.048_bib21","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s10439-014-1231-1","article-title":"The application of NCaRBS to the Trendelenburg test and total hip arthroplasty outcome","volume":"43","author":"Whatling","year":"2015","journal-title":"Ann Biomed Eng"},{"key":"10.1016\/j.arth.2019.05.048_bib22","doi-asserted-by":"crossref","first-page":"2071945","DOI":"10.1155\/2016\/2071945","article-title":"Wear scar similarities between retrieved and simulator-tested polyethylene TKR components: an artificial neural network approach","volume":"2016","author":"Orozco Villase\u00f1or","year":"2016","journal-title":"Biomed Res Int"},{"key":"10.1016\/j.arth.2019.05.048_bib23","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J\u00a0Mach Learn Res"},{"key":"10.1016\/j.arth.2019.05.048_bib24","unstructured":"Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. ArXiv150203167 Cs\u00a02015."},{"key":"10.1016\/j.arth.2019.05.048_bib25","unstructured":"Cogswell M, Ahmed F, Girshick R, Zitnick L, Batra D. Reducing overfitting in deep networks by decorrelating representations. Arxiv151106068 Cs Stat\u00a02015."},{"key":"10.1016\/j.arth.2019.05.048_bib27","first-page":"19","article-title":"Use of the All Patient Refined-Diagnosis Related Group (APR-DRG) risk of mortality score as a severity adjustor in the medical ICU","volume":"2","author":"Baram","year":"2008","journal-title":"Clin Med Circ Respirat Pulm Med"},{"key":"10.1016\/j.arth.2019.05.048_bib29","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. ArXiv160806993 Cs\u00a02016.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.arth.2019.05.048_bib30","doi-asserted-by":"crossref","unstructured":"J\u00e9gou S, Drozdzal M, Vazquez D, Romero A, Bengio Y. The one hundred layers Tiramisu: fully convolutional DenseNets for semantic segmentation. ArXiv\u00a02016.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"10.1016\/j.arth.2019.05.048_bib31","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot","year":"2010"},{"key":"10.1016\/j.arth.2019.05.048_bib32","series-title":"ADAM: a method for stochastic optimization","author":"Kingma","year":"2015"}],"container-title":["The Journal of Arthroplasty"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0883540319305315?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0883540319305315?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:24:29Z","timestamp":1760120669000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0883540319305315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10]]},"references-count":30,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["S0883540319305315"],"URL":"https:\/\/doi.org\/10.1016\/j.arth.2019.05.048","relation":{},"ISSN":["0883-5403"],"issn-type":[{"value":"0883-5403","type":"print"}],"subject":[],"published":{"date-parts":[[2019,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?","name":"articletitle","label":"Article Title"},{"value":"The Journal of Arthroplasty","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.arth.2019.05.048","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}