{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T15:01:02Z","timestamp":1790434862361,"version":"4.1.0"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T00:00:00Z","timestamp":1611187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Pfizer S.L.U","award":["Private funding. No Grant number"],"award-info":[{"award-number":["Private funding. No Grant number"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>(1) Background: Cardiac amyloidosis or \u201cstiff heart syndrome\u201d is a rare condition that occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy that is difficult to diagnose, often associated with a poor prognosis. This research analyses the characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this study are the basis of the learning and training techniques of the algorithm. The approach consisted of using the information generated by the patients in each admission and discharge episode and treating it as data vectors to facilitate their aggregation. The large volume of clinical histories implied a high dimensionality of the data, and the lack of diagnosis led to a severe class imbalance caused by the low prevalence of the disease. (3) Results: Although there are few patients with amyloidosis in this study, the proposed approach demonstrates that it is possible to learn from clinical records despite the lack of data. In the validation phase, the algorithm first acted on data from the general study population. It then was applied to a sample of patients diagnosed with heart failure. The results revealed that the algorithm detects disease when data vectors profile each disease episode. (4) Conclusions: The prediction levels showed that this technique could be useful in screening processes on a specific population to detect the disease.<\/jats:p>","DOI":"10.3390\/ijerph18030908","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T11:49:40Z","timestamp":1611229780000},"page":"908","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Real-World Data and Machine Learning to Predict Cardiac Amyloidosis"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5817-1126","authenticated-orcid":false,"given":"Elena","family":"Garc\u00eda-Garc\u00eda","sequence":"first","affiliation":[{"name":"Fundaci\u00f3n San Juan de Dios, Centro CC de la Salud San Rafael, Universidad Nebrija, 28036 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1461-9766","authenticated-orcid":false,"given":"Gracia Mar\u00eda","family":"Gonz\u00e1lez-Romero","sequence":"additional","affiliation":[{"name":"Fundaci\u00f3n San Juan de Dios, Centro CC de la Salud San Rafael, Universidad Nebrija, 28036 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1774-9064","authenticated-orcid":false,"given":"Encarna M.","family":"Mart\u00edn-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Hospital San Juan de Dios de Le\u00f3n, 24010 Le\u00f3n, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5589-6295","authenticated-orcid":false,"given":"Enrique de Dios","family":"Zapata Cornejo","sequence":"additional","affiliation":[{"name":"Sopra Steria, 28050 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0764-9211","authenticated-orcid":false,"given":"Gema","family":"Escobar-Aguilar","sequence":"additional","affiliation":[{"name":"Fundaci\u00f3n San Juan de Dios, Centro CC de la Salud San Rafael, Universidad Nebrija, 28036 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marlon F\u00e9lix","family":"C\u00e1rdenas Bonnet","sequence":"additional","affiliation":[{"name":"Sopra Steria, 28050 Madrid, Spain"},{"name":"Escuela Superior de Ingenier\u00eda y Tecnolog\u00eda, Universidad Internacional de la Rioja (UNIR), 26006 Logro\u00f1o, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.1161\/CIRCULATIONAHA.116.021612","article-title":"Nonbiopsy diagnosis of cardiac transthyretin amyloidosis","volume":"133","author":"Gillmore","year":"2016","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s10741-019-09776-3","article-title":"Advances in the diagnosis and treatment of transthyretin amyloidosis with cardiac involvement","volume":"24","author":"Rigopoulos","year":"2019","journal-title":"Heart Fail. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/brb3.1371","article-title":"Advances in the treatment of hereditary transthyretin amyloidosis: A review","volume":"9","author":"Gertz","year":"2019","journal-title":"Brain Behav."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1111\/jce.14541","article-title":"Effect of ICD implantation on cardiovascular outcomes in patients with cardiac amyloidosis: A systematic review and meta-anaylsis","volume":"31","author":"Halawa","year":"2020","journal-title":"J. Cardiovasc. Electrophysiol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Macedo, A.V.S., Schwartzmann, P.V., de Gusm\u00e3o, B.M., Melo, M.D.T., and de Coelho-Filho, O.R. (2020). Advances in the treatment of cardiac Amyloidosis. Curr. Treat. Options Oncol., 21.","DOI":"10.1007\/s11864-020-00738-8"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Agibetov, A., Seirer, B., Dachs, T.-M., Koschutnik, M., Dalos, D., Rettl, R., Duca, F., Schrutka, L., Agis, H., and Kain, R. (2020). Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study. J. Clin. Med., 9.","DOI":"10.3390\/jcm9051334"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"655","DOI":"10.4067\/S0034-98872005000600006","article-title":"Amiloidosis, Comunicaci\u00f3n de 11 casos y revisi\u00f3n de la literatura. Clinical features of patients with the pathological diagnosis of amyloidosis","volume":"133","author":"Palma","year":"2005","journal-title":"Rev. M\u00e9d. Chile"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s40119-019-00160-8","article-title":"Diagnosis of Amyloidosis: A survey of current awareness and clinical challenges among cardiologists in Switzerland","volume":"9","author":"Mircsof","year":"2020","journal-title":"Cardiol. Ther."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2799","DOI":"10.1093\/eurheartj\/ehx589","article-title":"A new staging system for cardiac transthyretin amyloidosis","volume":"39","author":"Gillmore","year":"2018","journal-title":"Eur. Heart J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cpcardiol.2017.04.003","article-title":"Cardiac Amyloidosis: An updated review with emphasis on diagnosis and future directions","volume":"43","author":"Bhogal","year":"2018","journal-title":"Curr. Probl. Cardiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1097\/01.smj.0000251327.38080.f1","article-title":"Hypertrophic cardiomyopathy and symptomatic conduction system disease in cardiac amyloidosis","volume":"99","author":"Garg","year":"2006","journal-title":"South Med. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1161\/CIRCOUTCOMES.115.001855","article-title":"Enhancing the prediction of 30-day readmission after percutaneous coronary intervention using data extracted by querying of the electronic health record","volume":"8","author":"Wasfy","year":"2015","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.regg.2016.07.005","article-title":"En busca de respuestas al reto de la complejidad cl\u00ednica en el siglo xxi: A prop\u00f3sito de los \u00edndices de fragilidad","volume":"52","author":"Inzitari","year":"2017","journal-title":"Rev. Esp. Geriatr. Gerontol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Basharat, I., Ali, R.A., Fatima, M., Qamar, U., and Shoab, A.K. (2016). A framework for classifying unstructured data of cardiac patients: A supervised learning approach. IJACSA, 7.","DOI":"10.14569\/IJACSA.2016.070218"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Price, M. (2016). Circle of care modelling: An approach to assist in reasoning about healthcare change using a patient-centric system. BMC Health Serv. Res., 16.","DOI":"10.1186\/s12913-016-1806-7"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e921","DOI":"10.1038\/tp.2015.182","article-title":"Predicting early psychiatric readmission with natural language processing of narrative discharge summaries","volume":"6","author":"Rumshisky","year":"2016","journal-title":"Transl. Psychiatry"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Padhukasahasram, B., Reddy, C.K., Li, Y., and Lanfear, D.E. (2016). Point impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129553"},{"key":"ref_18","first-page":"260","article-title":"Prediction modelling and risk stratification for chronic disease for care coordination on a national scale: A systematic eeview","volume":"15","author":"Twomey","year":"2015","journal-title":"IJIC"},{"key":"ref_19","first-page":"1","article-title":"Engineering performant, innovative and sustainable health systems","volume":"16","author":"Wouters","year":"2016","journal-title":"IJIC"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.csbj.2016.11.001","article-title":"Heart failure: Diagnosis, severity estimation and prediction of adverse events through machine learning techniques","volume":"15","author":"Tripoliti","year":"2017","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1177\/1099800416679730","article-title":"Frailty assessment in hospitalized older adults using the electronic health record","volume":"19","author":"Lekan","year":"2017","journal-title":"Biol. Res. Nurs."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1177\/1073110516684800","article-title":"Show us the data: The critical role health information plays in health system transformation","volume":"44","author":"Thorpe","year":"2016","journal-title":"J. Law Med. Ethics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s13634-016-0355-x","article-title":"A survey of machine learning for big data processing","volume":"1","author":"Qiu","year":"2016","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104053","DOI":"10.1016\/j.ijmedinf.2019.104053","article-title":"Mining clinical phrases from nursing notes to discover risk factors of patient deterioration","volume":"135","author":"Korach","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1038\/s41591-018-0178-4","article-title":"New machine-learning technologies for computer-aided diagnosis","volume":"24","author":"Lynch","year":"2018","journal-title":"Nat. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.hfc.2016.12.003","article-title":"Current concepts of cardiac amyloidosis: Diagnosis, clinical management, and the need for collaboration","volume":"13","author":"Ritts","year":"2017","journal-title":"Heart Fail. Clin."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"S11","DOI":"10.1016\/j.cardfail.2017.07.026","article-title":"Carpal tunnel syndrome and amyloid cardiomyopathy","volume":"23","author":"Ikram","year":"2017","journal-title":"J. Card. Fail."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/jns.12153","article-title":"\u201cRed-flag\u201d symptom clusters in transthyretin familial amyloid polyneuropathy","volume":"21","author":"Obici","year":"2016","journal-title":"J. Peripher. Nerv. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","article-title":"A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance","volume":"505","author":"Elreedy","year":"2019","journal-title":"Inf. Sci. NY"},{"key":"ref_30","unstructured":"Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., and Garnett, R. (2015). Precision-recall-gain curves: PR analysis done right. Advances in Neural Information Processing Systems 28, Curran Associates Inc.. Available online: http:\/\/papers.nips.cc\/paper\/5867-precision-recall-gain-curves-pr-analysis-done-right.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"E537","DOI":"10.1503\/cmaj.121286","article-title":"Variation of a test\u2019s sensitivity and specificity with disease prevalence","volume":"185","author":"Leeflang","year":"2013","journal-title":"Cmaj"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tran, B.X., Latkin, C.A., Vu, G.T., Nguyen, H.L.T., Nghiem, S., Tan, M.-X., Lim, Z.-K., Ho, C.S., and Ho, R.C. (2019). The current research landscape of the application of artificial intelligence in managing cerebrovascular and heart diseases: A bibliometric and content analysis. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16152699"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Harada, T., Shimizu, T., Kaji, Y., Suyama, Y., Matsumoto, T., Kosaka, C., Shimizu, H., Nei, T., and Watanuki, S. (2020). A Perspective from a case conference on comparing the diagnostic process: Human diagnostic thinking vs. artificial intelligence (AI) decision support tools. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17176110"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1001\/jama.2020.5493","article-title":"Systemic Amyloidosis recognition, prognosis, and therapy: A systematic review","volume":"324","author":"Gertz","year":"2020","journal-title":"JAMA"},{"key":"ref_35","unstructured":"Garg, R., Dong, S., Shah, S., and Jonnalagadda, S.R. (2016). A bootstrap machine learning approach to identify rare disease patients from electronic health records. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e12239","DOI":"10.2196\/12239","article-title":"Natural language processing of clinical notes on chronic diseases: Systematic review","volume":"7","author":"Sheikhalishahi","year":"2019","journal-title":"JMIR Med. Inform."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/18\/3\/908\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:13:35Z","timestamp":1760159615000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/18\/3\/908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,21]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["ijerph18030908"],"URL":"https:\/\/doi.org\/10.3390\/ijerph18030908","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,21]]}}}