{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:44:30Z","timestamp":1787813070712,"version":"build-2784847793"},"reference-count":27,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004325","name":"AstraZeneca PLC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004325","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000038","name":"U.S. Food and Drug Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jacc.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["JACC: Advances"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.jacadv.2026.102818","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T19:24:38Z","timestamp":1781724278000},"page":"102818","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"P2","title":["Natural Language Processing Framework in Early Detection of Amyloidosis"],"prefix":"10.1016","volume":"5","author":[{"given":"Ahmed M.","family":"Altibi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miriam R.","family":"Elman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javed","family":"Butler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay","family":"Arora","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole","family":"Baldwin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisa L.","family":"Priest","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6390-6526","authenticated-orcid":false,"given":"Ahmad","family":"Masri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.jacadv.2026.102818_bib1","article-title":"Impact of different approaches to preparing notes for analysis with natural language processing on the performance of prediction models in intensive care","volume":"3","author":"Mahendra","year":"2021","journal-title":"Crit Care Explor"},{"key":"10.1016\/j.jacadv.2026.102818_bib2","doi-asserted-by":"crossref","DOI":"10.3389\/fmed.2019.00066","article-title":"The revival of the notes field: leveraging the unstructured content in electronic health records","volume":"6","author":"Assale","year":"2019","journal-title":"Front Med"},{"issue":"8","key":"10.1016\/j.jacadv.2026.102818_bib3","doi-asserted-by":"crossref","first-page":"e185097","DOI":"10.1001\/jamanetworkopen.2018.5097","article-title":"Validation of prediction models for critical care outcomes using natural language processing of electronic health record data","volume":"1","author":"Marafino","year":"2018","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.jacadv.2026.102818_bib4","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1136\/heartjnl-2021-319769","article-title":"Systematic review of current natural language processing methods and applications in cardiology","volume":"108","author":"Reading","year":"2022","journal-title":"Heart"},{"issue":"11","key":"10.1016\/j.jacadv.2026.102818_bib5","doi-asserted-by":"crossref","DOI":"10.1056\/NEJMoa1805689","article-title":"Tafamidis treatment for patients with transthyretin amyloid cardiomyopathy","volume":"379","author":"Maurer","year":"2018","journal-title":"N Engl J Med"},{"issue":"5","key":"10.1016\/j.jacadv.2026.102818_bib6","first-page":"42","article-title":"HDF: the hierarchical data format","volume":"23","author":"Fortner","year":"1998","journal-title":"Dr Dobb\u2019s J Softw Tools Prof Progr"},{"key":"10.1016\/j.jacadv.2026.102818_bib7","series-title":"Centrum voor Wiskunde en Informatica Amsterdam","author":"Van Rossum","year":"1995"},{"key":"10.1016\/j.jacadv.2026.102818_bib8","series-title":"XGBoost: a Scalable Tree Boosting System","author":"Chen","year":"2016"},{"key":"10.1016\/j.jacadv.2026.102818_bib9","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"issue":"5","key":"10.1016\/j.jacadv.2026.102818_bib10","doi-asserted-by":"crossref","DOI":"10.18637\/jss.v028.i05","article-title":"Building predictive models in R using the caret package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J Stat Softw"},{"key":"10.1016\/j.jacadv.2026.102818_bib11","series-title":"R: A Language and Environment for Statistical Computing","year":"2014"},{"key":"10.1016\/j.jacadv.2026.102818_bib12","series-title":"R: A Language and Environment for Statistical Computing","year":"2021"},{"issue":"11","key":"10.1016\/j.jacadv.2026.102818_bib13","doi-asserted-by":"crossref","DOI":"10.1001\/jamacardio.2021.3070","article-title":"Prevalence of Transthyretin amyloid cardiomyopathy in heart failure with preserved ejection fraction","volume":"6","author":"Abouezzeddine","year":"2021","journal-title":"JAMA Cardiol"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102818_bib14","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/ehf2.15091","article-title":"Early diagnosis, disease stage and prognosis in wild-type transthyretin amyloid cardiomyopathy: the DIAMOND study","volume":"12","author":"Tini","year":"2024","journal-title":"ESC Hear Fail"},{"issue":"2","key":"10.1016\/j.jacadv.2026.102818_bib15","doi-asserted-by":"crossref","DOI":"10.1056\/NEJMoa2305434","article-title":"Efficacy and safety of acoramidis in transthyretin amyloid cardiomyopathy","volume":"390","author":"Gillmore","year":"2024","journal-title":"N Engl J Med"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102818_bib16","doi-asserted-by":"crossref","DOI":"10.1056\/NEJMoa2409134","article-title":"Vutrisiran in patients with transthyretin amyloidosis with cardiomyopathy","volume":"392","author":"Fontana","year":"2024","journal-title":"N Engl J Med"},{"issue":"15","key":"10.1016\/j.jacadv.2026.102818_bib17","doi-asserted-by":"crossref","DOI":"10.1001\/jama.2023.18688","article-title":"Eplontersen for hereditary transthyretin amyloidosis with polyneuropathy","volume":"330","author":"Coelho","year":"2023","journal-title":"JAMA"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102818_bib18","doi-asserted-by":"crossref","DOI":"10.1080\/13506129.2022.2091985","article-title":"Efficacy and safety of vutrisiran for patients with hereditary transthyretin-mediated amyloidosis with polyneuropathy: a randomized clinical trial","volume":"30","author":"Adams","year":"2023","journal-title":"Amyloid"},{"issue":"9","key":"10.1016\/j.jacadv.2026.102818_bib19","doi-asserted-by":"crossref","DOI":"10.1200\/JCO.2011.38.5724","article-title":"Revised prognostic staging system for light chain amyloidosis incorporating cardiac biomarkers and serum free light chain measurements","volume":"30","author":"Kumar","year":"2012","journal-title":"J Clin Oncol"},{"issue":"6","key":"10.1016\/j.jacadv.2026.102818_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.cardfail.2023.11.017","article-title":"EstimATTR: a simplified, machine-learning-based tool to predict the risk of wild-type transthyretin amyloid cardiomyopathy","volume":"30","author":"Casta\u00f1o","year":"2024","journal-title":"J Card Fail"},{"issue":"12","key":"10.1016\/j.jacadv.2026.102818_bib21","doi-asserted-by":"crossref","DOI":"10.3390\/jpm11121268","article-title":"Convolutional neural networks for fully automated diagnosis of cardiac amyloidosis by cardiac magnetic resonance imaging","volume":"11","author":"Agibetov","year":"2021","journal-title":"J Pers Med"},{"issue":"11","key":"10.1016\/j.jacadv.2026.102818_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.mayocp.2021.04.023","article-title":"Artificial intelligence\u2013enhanced electrocardiogram for the early detection of cardiac amyloidosis","volume":"96","author":"Grogan","year":"2021","journal-title":"Mayo Clin Proc"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102818_bib23","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-021-22877-8","article-title":"Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms","volume":"12","author":"Goto","year":"2021","journal-title":"Nat Commun"},{"issue":"8","key":"10.1016\/j.jacadv.2026.102818_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcmg.2023.01.014","article-title":"Deep learning on bone scintigraphy to detect abnormal cardiac uptake at risk of cardiac amyloidosis","volume":"16","author":"Delbarre","year":"2023","journal-title":"JACC Cardiovasc Imaging"},{"issue":"1","key":"10.1016\/j.jacadv.2026.102818_bib25","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-021-22876-9","article-title":"A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy","volume":"12","author":"Huda","year":"2021","journal-title":"Nat Commun"},{"key":"10.1016\/j.jacadv.2026.102818_bib26","article-title":"A study of the generalizability of self-supervised representations","volume":"6","author":"Tendle","year":"2021","journal-title":"Mach Learn with Appl"},{"issue":"5","key":"10.1016\/j.jacadv.2026.102818_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav Ther"}],"container-title":["JACC: Advances"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772963X26002395?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772963X26002395?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:25:00Z","timestamp":1783725900000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2772963X26002395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":27,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["S2772963X26002395"],"URL":"https:\/\/doi.org\/10.1016\/j.jacadv.2026.102818","relation":{},"ISSN":["2772-963X"],"issn-type":[{"value":"2772-963X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Natural Language Processing Framework in Early Detection of Amyloidosis","name":"articletitle","label":"Article Title"},{"value":"JACC: Advances","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jacadv.2026.102818","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.","name":"copyright","label":"Copyright"}],"article-number":"102818"}}