{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:13:54Z","timestamp":1787318034093,"version":"build-2736575974"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":["eje.bioscientifica.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Differentiation between central diabetes insipidus (cDI) and primary polydipsia (PP) remains challenging in clinical practice. Although the hypertonic saline infusion test led to high diagnostic accuracy, it is a laborious test requiring close monitoring of plasma sodium levels. As such, we leverage machine learning (ML) to facilitate differential diagnosis of cDI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>We analyzed data of 59 patients with cDI and 81 patients with PP from a prospective multicenter study evaluating the hypertonic saline test as new test approach to diagnose cDI. Our primary outcome was the diagnostic accuracy of the ML-based algorithm in differentiating cDI from PP patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The data set used included 56 clinical, biochemical, and radiological covariates. We identified a set of five covariates which were crucial for differentiating cDI from PP patients utilizing standard ML methods. We developed ML-based algorithms on the data and validated them with an unseen test data set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Urine osmolality, plasma sodium and glucose, known transsphenoidal surgery, or anterior pituitary deficiencies were selected as input parameters for the basic ML-based algorithm. Testing it on an unseen test data set resulted in a high area under the curve (AUC) score of 0.87. A further improvement of the ML-based algorithm was reached with the addition of MRI characteristics and the results of the hypertonic saline infusion test (AUC: 0.93 and 0.98, respectively).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The developed ML-based algorithm facilitated differentiation between cDI and PP patients with high accuracy even if only clinical information and laboratory data were available, thereby possibly avoiding cumbersome clinical tests in the future.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1530\/eje-22-0368","type":"journal-article","created":{"date-parts":[[2022,10,6]],"date-time":"2022-10-06T11:22:06Z","timestamp":1665055326000},"page":"777-786","update-policy":"https:\/\/doi.org\/10.1530\/crossmarkpolicy-7","source":"Crossref","is-referenced-by-count":12,"title":["Machine learning-based algorithm as an innovative approach for the differentiation between diabetes insipidus and primary polydipsia in clinical practice"],"prefix":"10.1093","volume":"187","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6186-1830","authenticated-orcid":false,"given":"Uri","family":"Nahum","sequence":"first","affiliation":[{"name":"Pediatric Pharmacology and Pharmacometrics Research Center, University Children's Hospital Basel, University of Basel , Basel, Switzerland"},{"name":"Department of Clinical Research, University Hospital Basel , Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3040-4685","authenticated-orcid":false,"given":"Julie","family":"Refardt","sequence":"additional","affiliation":[{"name":"Departments of Endocrinology, Diabetology and Metabolism, University Hospital Basel , Basel, Switzerland"},{"name":"Department of Clinical Research, University Hospital Basel , Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6741-2689","authenticated-orcid":false,"given":"Irina","family":"Chifu","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Diabetes, Department of Internal Medicine I, University Hospital, University of Wuerzburg , Wuerzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wiebke K","family":"Fenske","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, University Hospital of Bonn , Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6170-6398","authenticated-orcid":false,"given":"Martin","family":"Fassnacht","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Diabetes, Department of Internal Medicine I, University Hospital, University of Wuerzburg , Wuerzburg, Germany"},{"name":"Central Laboratory, University Hospital Wuerzburg , Wuerzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabor","family":"Szinnai","sequence":"additional","affiliation":[{"name":"Department of Clinical Research, University Hospital Basel , Basel, Switzerland"},{"name":"Pediatric Endocrinology and Diabetology, University Children's Hospital Basel, University of Basel , Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-0965","authenticated-orcid":false,"given":"Mirjam","family":"Christ-Crain","sequence":"additional","affiliation":[{"name":"Departments of Endocrinology, Diabetology and Metabolism, University Hospital Basel , Basel, Switzerland"},{"name":"Department of Clinical Research, University Hospital Basel , Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2597-1228","authenticated-orcid":false,"given":"Marc","family":"Pfister","sequence":"additional","affiliation":[{"name":"Pediatric Pharmacology and Pharmacometrics Research Center, University Children's Hospital Basel, University of Basel , Basel, Switzerland"},{"name":"Department of Clinical Research, University Hospital Basel , Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"2023012409095433500_bib1","first-page":"54","article-title":"Diabetes insipidus","volume":"5","author":"Christ-Crain","year":"2019","journal-title":"Nature Reviews: Disease Primers"},{"key":"2023012409095433500_bib2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1136\/adc.79.1.84","article-title":"Diabetes insipidus","volume":"79","author":"Baylis","year":"1998","journal-title":"Archives of Disease in Childhood"},{"key":"2023012409095433500_bib3","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1210\/jc.2010-2345","article-title":"Copeptin in the differential diagnosis of the polydipsia-polyuria syndrome \u2013 revisiting the direct and indirect water deprivation tests","volume":"96","author":"Fenske","year":"2011","journal-title":"Journal of Clinical Endocrinology and Metabolism"},{"key":"2023012409095433500_bib4","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1056\/NEJMoa1803760","article-title":"A copeptin-based approach in the diagnosis of diabetes insipidus","volume":"379","author":"Fenske","year":"2018","journal-title":"New England Journal of Medicine"},{"key":"2023012409095433500_bib5","doi-asserted-by":"crossref","first-page":"721","DOI":"10.7326\/0003-4819-73-5-721","article-title":"Recognition of partial defects in antidiuretic hormone secretion","volume":"73","author":"Miller","year":"1970","journal-title":"Annals of Internal Medicine"},{"key":"2023012409095433500_bib6","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","article-title":"Machine learning for medical diagnosis: history, state of the art and perspective","volume":"23","author":"Kononenko","year":"2001","journal-title":"Artificial Intelligence in Medicine"},{"key":"2023012409095433500_bib7","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.compchemeng.2017.06.011","article-title":"An analytical method for diseases prediction using machine learning techniques","volume":"106","author":"Nilashi","year":"2017","journal-title":"Computers and Chemical Engineering"},{"key":"2023012409095433500_bib8","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s00701-017-3385-8","article-title":"An introduction and overview of machine learning in neurosurgical care","volume":"160","author":"Senders","year":"2018","journal-title":"Acta Neurochirurgica"},{"key":"2023012409095433500_bib9","doi-asserted-by":"crossref","first-page":"107395","DOI":"10.1016\/j.pharmthera.2019.107395","article-title":"Machine learning and data mining frameworks for predicting drug response in cancer: an overview and a novel in silico screening process based on association rule mining","volume":"203","author":"Vougas","year":"2019","journal-title":"Pharmacology and Therapeutics"},{"key":"2023012409095433500_bib10","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s11882-015-0542-0","article-title":"Distinguishing asthma phenotypes using machine learning approaches","volume":"15","author":"Howard","year":"2015","journal-title":"Current Allergy and Asthma Reports"},{"key":"2023012409095433500_bib11","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"2023012409095433500_bib12","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1089\/dia.2020.0026","article-title":"Use of machine learning and hybrid closed loop insulin delivery at diabetes camps","volume":"22","author":"Taylor","year":"2020","journal-title":"Diabetes Technology and Therapeutics"},{"key":"2023012409095433500_bib13","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1038\/s41390-019-0384-x","article-title":"Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning","volume":"86","author":"Daunhawer","year":"2019","journal-title":"Pediatric Research"},{"key":"2023012409095433500_bib14","doi-asserted-by":"crossref","first-page":"185","DOI":"10.3389\/fendo.2019.00185","article-title":"Artificial intelligence and machine learning in endocrinology and metabolism: the dawn of a new era","volume":"10","author":"Gubbi","year":"2019","journal-title":"Frontiers in Endocrinology"},{"key":"2023012409095433500_bib15","doi-asserted-by":"crossref","first-page":"025006","DOI":"10.1088\/1361-6420\/aa5250","article-title":"Adaptive eigenspace method for inverse scattering problems in the frequency domain","volume":"33","author":"Grote","year":"2017","journal-title":"Inverse Problems"},{"key":"2023012409095433500_bib16","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1016\/j.camwa.2019.02.005","article-title":"Adaptive eigenspace for multi-parameter inverse scattering problems","volume":"77","author":"Grote","year":"2019","journal-title":"Computers and Mathematics with Applications"},{"key":"2023012409095433500_bib17","first-page":"1","article-title":"Explainers for complex predictive models in R","volume":"19","author":"Dalex","year":"2018","journal-title":"Journal of Machine Learning Research"},{"key":"2023012409095433500_bib18","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"2023012409095433500_bib19","volume-title":"European Conference on the Applications of Evolutinonary Computation","author":"Olson","year":"2016"},{"key":"2023012409095433500_bib20","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1210\/jc.2018-02195","article-title":"Water supplementation reduces copeptin and plasma glucose in adults with high copeptin: the H2O Metabolism Pilot Study","volume":"104","author":"Enh\u00f6rning","year":"2019","journal-title":"Journal of Clinical Endocrinology and Metabolism"},{"key":"2023012409095433500_bib21","first-page":"1954","article-title":"Thickened pituitary stalk on magnetic resonance imaging in children with central diabetes insipidus","volume":"84","author":"Leger","year":"1999","journal-title":"Journal of Clinical Endocrinology and Metabolism"},{"key":"2023012409095433500_bib22","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1111\/joim.13261","article-title":"Diagnosis and management of diabetes insipidus for the internist: an update","volume":"290","author":"Christ-Crain","year":"2021","journal-title":"Journal of Internal Medicine"}],"container-title":["European Journal of Endocrinology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/eje.bioscientifica.com\/view\/journals\/eje\/187\/6\/EJE-22-0368.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/eje.bioscientifica.com\/downloadpdf\/journals\/eje\/187\/6\/EJE-22-0368.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ejendo\/article-pdf\/187\/6\/777\/48837636\/eje-22-0368.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ejendo\/article-pdf\/187\/6\/777\/48837636\/eje-22-0368.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T11:18:11Z","timestamp":1676373491000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ejendo\/article\/187\/6\/777\/6979905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":22,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12,1]]}},"URL":"https:\/\/doi.org\/10.1530\/eje-22-0368","relation":{},"ISSN":["0804-4643","1479-683X"],"issn-type":[{"value":"0804-4643","type":"print"},{"value":"1479-683X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,12]]},"published":{"date-parts":[[2022,10,26]]}}}