{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T12:06:18Z","timestamp":1792065978438,"version":"4.4.0"},"reference-count":109,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Immunopharmacology"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.intimp.2024.112238","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T22:51:40Z","timestamp":1715467900000},"page":"112238","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"special_numbering":"C","title":["Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges"],"prefix":"10.1016","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3350-6270","authenticated-orcid":false,"given":"Xiaoshi","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guolin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changji","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxian","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6747-8592","authenticated-orcid":false,"given":"Yong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.intimp.2024.112238_b0005","doi-asserted-by":"crossref","DOI":"10.1084\/jem.20211487","article-title":"The X in seX-biased immunity and autoimmune rheumatic disease","volume":"219","author":"Jiwrajka","year":"2022","journal-title":"J. Exp. Med."},{"issue":"3 Pt B","key":"10.1016\/j.intimp.2024.112238_b0010","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.jid.2021.06.008","article-title":"Sex bias and autoimmune diseases","volume":"142","author":"Xing","year":"2022","journal-title":"J. Invest. Dermatol."},{"key":"10.1016\/j.intimp.2024.112238_b0015","doi-asserted-by":"crossref","first-page":"265","DOI":"10.3389\/fendo.2019.00265","article-title":"Autoimmune disease in women: endocrine transition and risk across the lifespan","volume":"10","author":"Desai","year":"2019","journal-title":"Front. Endocrinol."},{"key":"10.1016\/j.intimp.2024.112238_b0020","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.3389\/fpsyg.2019.01961","article-title":"A pilot study of the relationship between pregnancy and autoimmune disease: exploring the mother's psychological process","volume":"10","author":"Cataudella","year":"2019","journal-title":"Front. Psychol."},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0025","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/imm.13004","article-title":"Impact of sex hormones on immune function and multiple sclerosis development","volume":"156","author":"Ysrraelit","year":"2019","journal-title":"Immunology"},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0030","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1111\/eva.13167","article-title":"Why women have more autoimmune diseases than men: An evolutionary perspective","volume":"14","author":"Kronzer","year":"2021","journal-title":"Evol. Appl."},{"key":"10.1016\/j.intimp.2024.112238_b0035","doi-asserted-by":"crossref","first-page":"9501865","DOI":"10.1155\/2018\/9501865","article-title":"Autoimmunity in Reproductive Health and Pregnancy","volume":"2018","author":"Tabarkiewicz","year":"2018","journal-title":"J. Immunol. Res."},{"issue":"9","key":"10.1016\/j.intimp.2024.112238_b0040","first-page":"145","article-title":"Pregnancy and autoimmune disease","volume":"119","author":"Merz","year":"2022","journal-title":"Deutsches Arzteblatt International"},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0045","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1038\/nrneph.2014.251","article-title":"Birth weight, malnutrition and kidney-associated outcomes\u2013a global concern","volume":"11","author":"Luyckx","year":"2015","journal-title":"Nat. Rev. Nephrol."},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0050","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1002\/art.21663","article-title":"Obstetric hospitalizations in the United States for women with systemic lupus erythematosus and rheumatoid arthritis","volume":"54","author":"Chakravarty","year":"2006","journal-title":"Arthritis Rheum."},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0055","doi-asserted-by":"crossref","first-page":"J287","DOI":"10.1016\/j.jaut.2009.11.015","article-title":"The implications of autoimmunity and pregnancy","volume":"34","author":"Borchers","year":"2010","journal-title":"J. Autoimmun."},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0060","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s12016-021-08893-y","article-title":"Systemic Lupus Erythematosus and pregnancy: a portuguese case-control study","volume":"62","author":"Braga","year":"2022","journal-title":"Clin Rev Allergy Immunol"},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0065","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13075-022-02783-0","article-title":"Systemic sclerosis and pregnancy outcomes: a retrospective study from a single center","volume":"24","author":"Barilaro","year":"2022","journal-title":"Arthritis Res. Ther."},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0070","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s12969-018-0242-0","article-title":"Adverse pregnancy outcomes in adolescents and young women with systemic lupus erythematosus: a national estimate","volume":"16","author":"Ling","year":"2018","journal-title":"Pediatr. Rheumatol. Online J."},{"key":"10.1016\/j.intimp.2024.112238_b0075","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s41927-018-0029-4","article-title":"Identifying the unmet information and support needs of women with autoimmune rheumatic diseases during pregnancy planning, pregnancy and early parenting: mixed-methods study","volume":"2","author":"Phillips","year":"2018","journal-title":"BMC Rheumatology"},{"issue":"10","key":"10.1016\/j.intimp.2024.112238_b0080","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1038\/nrrheum.2016.137","article-title":"The global burden of SLE: prevalence, health disparities and socioeconomic impact","volume":"12","author":"Carter","year":"2016","journal-title":"Nat. Rev. Rheumatol."},{"issue":"12","key":"10.1016\/j.intimp.2024.112238_b0085","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1001\/jamaneurol.2021.3416","article-title":"Health disparities, inequities, and social determinants of health in multiple sclerosis and related disorders in the US: a review","volume":"78","author":"Amezcua","year":"2021","journal-title":"JAMA Neurology"},{"issue":"8","key":"10.1016\/j.intimp.2024.112238_b0090","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1001\/jama.2020.26858","article-title":"Diagnosis and treatment of multiple sclerosis: a review","volume":"325","author":"McGinley","year":"2021","journal-title":"JAMA"},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0095","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.semarthrit.2014.05.001","article-title":"The impact of rheumatoid arthritis on quality-of-life assessed using the SF-36: a systematic review and meta-analysis","volume":"44","author":"Matcham","year":"2014","journal-title":"Semin. Arthritis Rheum."},{"key":"10.1016\/j.intimp.2024.112238_b0100","doi-asserted-by":"crossref","unstructured":"I. Cort\u00e9s-P\u00e9rez, M. S\u00e1nchez-Alcal\u00e1, F.A. Nieto-Esc\u00e1mez, Y. Castellote-Caballero, E. Obrero-Gait\u00e1n, M.C. Osuna-P\u00e9rez, Virtual Reality-Based Therapy Improves Fatigue, Impact, and Quality of Life in Patients with Multiple Sclerosis. A Systematic Review with a Meta-Analysis, Sensors (Basel, Switzerland) 21(21) (2021).","DOI":"10.3390\/s21217389"},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0105","first-page":"58","article-title":"From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning","volume":"10","author":"Li","year":"2023","journal-title":"Mil. Med. Res."},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0110","doi-asserted-by":"crossref","DOI":"10.1002\/VIW.20220042","article-title":"Algorithms push forward the application of MALDI\u2013TOF mass fingerprinting in rapid precise diagnosis","volume":"4","author":"Zhu","year":"2023","journal-title":"View"},{"issue":"7","key":"10.1016\/j.intimp.2024.112238_b0115","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.tips.2023.04.005","article-title":"Artificial intelligence-driven drug development against autoimmune diseases","volume":"44","author":"Moingeon","year":"2023","journal-title":"Trends Pharmacol. Sci."},{"key":"10.1016\/j.intimp.2024.112238_b0120","article-title":"Using machine learning to predict complications in pregnancy: a systematic review","volume":"9","author":"Bertini","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"8","key":"10.1016\/j.intimp.2024.112238_b0125","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1016\/j.cell.2023.01.035","article-title":"From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment","volume":"186","author":"Swanson","year":"2023","journal-title":"Cell"},{"key":"10.1016\/j.intimp.2024.112238_b0130","doi-asserted-by":"crossref","unstructured":"L. Myatt, The prediction of preeclampsia: the way forward, American journal of obstetrics and gynecology 226(2s) (2022) S1102-S1107.e8.","DOI":"10.1016\/j.ajog.2020.10.047"},{"key":"10.1016\/j.intimp.2024.112238_b0135","doi-asserted-by":"crossref","unstructured":"Improving the diagnosis of myocardial infarction with machine learning, Nature medicine 29(5) (2023) 1070-1071.","DOI":"10.1038\/s41591-023-02331-6"},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0140","doi-asserted-by":"crossref","DOI":"10.1002\/VIW.20220038","article-title":"Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis","volume":"4","author":"Chen","year":"2022","journal-title":"View"},{"key":"10.1016\/j.intimp.2024.112238_b0145","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.jad.2022.04.093","article-title":"Machine learning in the prediction of postpartum depression: A review","volume":"309","author":"Cellini","year":"2022","journal-title":"J. Affect. Disord."},{"key":"10.1016\/j.intimp.2024.112238_b0150","doi-asserted-by":"crossref","first-page":"1130139","DOI":"10.3389\/fendo.2023.1130139","article-title":"Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications","volume":"14","author":"Mennickent","year":"2023","journal-title":"Front. Endocrinol."},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0155","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1097\/ALN.0000000000004764","article-title":"Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools","volume":"140","author":"Arina","year":"2024","journal-title":"Anesthesiology"},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0160","doi-asserted-by":"crossref","first-page":"e1191","DOI":"10.1210\/clinem\/dgaa899","article-title":"Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning","volume":"106","author":"Wu","year":"2021","journal-title":"J. Clin. Endocrinol. Metab."},{"issue":"8","key":"10.1016\/j.intimp.2024.112238_b0165","doi-asserted-by":"crossref","first-page":"e0221202","DOI":"10.1371\/journal.pone.0221202","article-title":"Prediction model development of late-onset preeclampsia using machine learning-based methods","volume":"14","author":"Jhee","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.intimp.2024.112238_b0170","doi-asserted-by":"crossref","DOI":"10.1007\/s40618-024-02328-z","article-title":"Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach","author":"Xing","year":"2024","journal-title":"J. Endocrinol. Invest."},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0175","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s12884-023-06220-1","article-title":"Machine learning models for predicting preeclampsia: a systematic review","volume":"24","author":"Ranjbar","year":"2024","journal-title":"BMC Pregnancy Childbirth"},{"issue":"9","key":"10.1016\/j.intimp.2024.112238_b0180","doi-asserted-by":"crossref","first-page":"e074705","DOI":"10.1136\/bmjopen-2023-074705","article-title":"Machine learning models for predicting pre-eclampsia: a systematic review protocol","volume":"13","author":"Ranjbar","year":"2023","journal-title":"BMJ Open"},{"key":"10.1016\/j.intimp.2024.112238_b0185","first-page":"333","article-title":"Contribution of Artificial Intelligence in Pregnancy: A Scoping Review","volume":"289","author":"Abuelezz","year":"2022","journal-title":"Stud. Health Technol. Inform."},{"issue":"5","key":"10.1016\/j.intimp.2024.112238_b0190","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa369","article-title":"Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes","volume":"22","author":"Davidson","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0195","doi-asserted-by":"crossref","first-page":"219","DOI":"10.5811\/westjem.2019.1.41244","article-title":"Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview","volume":"20","author":"Lee","year":"2019","journal-title":"West J Emerg Med"},{"issue":"6","key":"10.1016\/j.intimp.2024.112238_b0200","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"eDoctor: machine learning and the future of medicine","volume":"284","author":"Handelman","year":"2018","journal-title":"J. Intern. Med."},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0205","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nbt1386","article-title":"What are artificial neural networks?","volume":"26","author":"Krogh","year":"2008","journal-title":"Nat. Biotechnol."},{"issue":"8982","key":"10.1016\/j.intimp.2024.112238_b0210","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/S0140-6736(95)91746-2","article-title":"Introduction to neural networks","volume":"346","author":"Cross","year":"1995","journal-title":"Lancet (london, England)"},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0215","doi-asserted-by":"crossref","DOI":"10.3390\/jpm10020021","article-title":"Applications of machine learning predictive models in the chronic disease diagnosis","volume":"10","author":"Battineni","year":"2020","journal-title":"Journal of Personalized Medicine"},{"key":"10.1016\/j.intimp.2024.112238_b0220","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3389\/fnbot.2018.00063","article-title":"Intrinsic rewards for maintenance, approach, avoidance, and achievement goal types","volume":"12","author":"Dhakan","year":"2018","journal-title":"Frontiers in Neurorobotics"},{"issue":"12","key":"10.1016\/j.intimp.2024.112238_b0225","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1038\/s41584-021-00708-w","article-title":"An introduction to machine learning and analysis of its use in rheumatic diseases","volume":"17","author":"Kingsmore","year":"2021","journal-title":"Nat. Rev. Rheumatol."},{"issue":"6","key":"10.1016\/j.intimp.2024.112238_b0230","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nrg3920","article-title":"Machine learning applications in genetics and genomics","volume":"16","author":"Libbrecht","year":"2015","journal-title":"Nat. Rev. Genet."},{"key":"10.1016\/j.intimp.2024.112238_b0235","doi-asserted-by":"crossref","first-page":"267","DOI":"10.3389\/fgene.2019.00267","article-title":"Machine Learning SNP Based Prediction for Precision Medicine","volume":"10","author":"Ho","year":"2019","journal-title":"Front. Genet."},{"key":"10.1016\/j.intimp.2024.112238_b0240","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41746-020-0229-3","article-title":"A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases","volume":"3","author":"Stafford","year":"2020","journal-title":"Npj Digital Med."},{"issue":"8","key":"10.1016\/j.intimp.2024.112238_b0245","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1111\/cup.13954","article-title":"Artificial intelligence in dermatopathology: Diagnosis, education, and research","volume":"48","author":"Wells","year":"2021","journal-title":"J. Cutan. Pathol."},{"issue":"5","key":"10.1016\/j.intimp.2024.112238_b0250","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.cmi.2019.09.009","article-title":"Machine learning for clinical decision support in infectious diseases: a narrative review of current applications","volume":"26","author":"Peiffer-Smadja","year":"2020","journal-title":"Clin. Microbiol. Infect."},{"key":"10.1016\/j.intimp.2024.112238_b0255","doi-asserted-by":"crossref","unstructured":"N. Aslam, I.U. Khan, A. Bashamakh, F.A. Alghool, M. Aboulnour, N.M. Alsuwayan, R.K. Alturaif, S. Brahimi, S.S. Aljameel, K. Al Ghamdi, Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities, Sensors (Basel, Switzerland) 22(20) (2022).","DOI":"10.3390\/s22207856"},{"key":"10.1016\/j.intimp.2024.112238_b0260","doi-asserted-by":"crossref","unstructured":"L. Balyen, T. Peto, Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology, Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 8(3) (2019) 264-272.","DOI":"10.1097\/01.APO.0000586388.81551.d0"},{"key":"10.1016\/j.intimp.2024.112238_b0265","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.media.2016.06.032","article-title":"Machine learning approaches in medical image analysis: From detection to diagnosis","volume":"33","author":"de Bruijne","year":"2016","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0270","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12551-018-0446-z","article-title":"Machine learning and feature selection for drug response prediction in precision oncology applications","volume":"11","author":"Ali","year":"2019","journal-title":"Biophys. Rev."},{"issue":"23","key":"10.1016\/j.intimp.2024.112238_b0275","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.1016\/j.jacc.2018.03.521","article-title":"Artificial Intelligence in Cardiology","volume":"71","author":"Johnson","year":"2018","journal-title":"J. Am. Coll. Cardiol."},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0280","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1177\/0961203320983445","article-title":"Delayed diagnosis adversely affects outcome in systemic lupus erythematosus: Cross sectional analysis of the LuLa cohort","volume":"30","author":"Kernder","year":"2021","journal-title":"Lupus"},{"issue":"4","key":"10.1016\/j.intimp.2024.112238_b0285","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1002\/art.22510","article-title":"Reproductive and menopausal factors and risk of systemic lupus erythematosus in women","volume":"56","author":"Costenbader","year":"2007","journal-title":"Arthritis Rheum."},{"issue":"10","key":"10.1016\/j.intimp.2024.112238_b0290","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1002\/1529-0131(199810)41:10<1714::AID-ART3>3.0.CO;2-U","article-title":"Hormonal, environmental, and infectious risk factors for developing systemic lupus erythematosus","volume":"41","author":"Cooper","year":"1998","journal-title":"Arthritis Rheum."},{"issue":"8","key":"10.1016\/j.intimp.2024.112238_b0295","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1177\/0961203310397967","article-title":"Predictors of maternal and fetal outcomes in pregnancies of patients with systemic lupus erythematosus","volume":"20","author":"Kwok","year":"2011","journal-title":"Lupus"},{"key":"10.1016\/j.intimp.2024.112238_b0300","first-page":"24","article-title":"Pregnancy and Systemic Lupus Erythematosus, Best practice & research","volume":"64","author":"Petri","year":"2020","journal-title":"Clinical Obstetrics & Gynaecology"},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0305","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1136\/annrheumdis-2016-209770","article-title":"EULAR recommendations for women's health and the management of family planning, assisted reproduction, pregnancy and menopause in patients with systemic lupus erythematosus and\/or antiphospholipid syndrome","volume":"76","author":"Andreoli","year":"2017","journal-title":"Ann. Rheum. Dis."},{"issue":"11","key":"10.1016\/j.intimp.2024.112238_b0310","first-page":"1191","article-title":"Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases","volume":"49","author":"Nelson","year":"2022","journal-title":"Goals, and Future Directions, the Journal of Rheumatology"},{"issue":"6","key":"10.1016\/j.intimp.2024.112238_b0315","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1136\/annrheumdis-2020-219069","article-title":"Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus","volume":"80","author":"Adamichou","year":"2021","journal-title":"Ann. Rheum. Dis."},{"key":"10.1016\/j.intimp.2024.112238_b0320","doi-asserted-by":"crossref","unstructured":"Y. Wang, W. Shu, S. Lin, J. Wu, M. Jiang, S. Li, C. Liu, R. Li, C. Pei, Y. Ding, J. Wan, W. Di, Hollow Cobalt Oxide\/Carbon Hybrids Aid Metabolic Encoding for Active Systemic Lupus Erythematosus during Pregnancy, Small (Weinheim an der Bergstrasse, Germany) 18(11) (2022) e2106412.","DOI":"10.1002\/smll.202106412"},{"key":"10.1016\/j.intimp.2024.112238_b0325","article-title":"Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus","volume":"13","author":"Ma","year":"2022","journal-title":"Front. Genet."},{"issue":"7","key":"10.1016\/j.intimp.2024.112238_b0330","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1136\/ard-2022-223808","article-title":"Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes","volume":"82","author":"Choi","year":"2023","journal-title":"Ann. Rheum. Dis."},{"key":"10.1016\/j.intimp.2024.112238_b0335","doi-asserted-by":"crossref","DOI":"10.3389\/fendo.2022.957010","article-title":"Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus","volume":"13","author":"Deng","year":"2022","journal-title":"Front. Endocrinol."},{"key":"10.1016\/j.intimp.2024.112238_b0340","doi-asserted-by":"crossref","unstructured":"X. Hao, D. Zheng, M. Khan, L. Wang, T. H\u00e4m\u00e4l\u00e4inen, F. Cong, H. Xu, K. Song, Machine Learning Models for Predicting Adverse Pregnancy Outcomes in Pregnant Women with Systemic Lupus Erythematosus, Diagnostics (Basel, Switzerland) 13(4) (2023).","DOI":"10.3390\/diagnostics13040612"},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0345","doi-asserted-by":"crossref","DOI":"10.1136\/lupus-2022-000769","article-title":"Adverse pregnancy outcomes in women with systemic lupus erythematosus: can we improve predictions with machine learning?","volume":"9","author":"Fazzari","year":"2022","journal-title":"Lupus Science & Medicine"},{"key":"10.1016\/j.intimp.2024.112238_b0350","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.ijmedinf.2016.10.018","article-title":"A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus","volume":"97","author":"Paydar","year":"2017","journal-title":"Int. J. Med. Inf."},{"key":"10.1016\/j.intimp.2024.112238_b0355","doi-asserted-by":"crossref","unstructured":"D.C. Wang, W.D. Xu, S.N. Wang, X. Wang, W. Leng, L. Fu, X.Y. Liu, Z. Qin, A.F. Huang, Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis, Inflammation research : official journal of the European Histamine Research Society ... [et al.] 72(6) (2023) 1315-1324.","DOI":"10.1007\/s00011-023-01755-7"},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0360","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.kint.2021.11.014","article-title":"Prediction models of treatment response in lupus nephritis","volume":"101","author":"Ayoub","year":"2022","journal-title":"Kidney Int."},{"issue":"Suppl 1","key":"10.1016\/j.intimp.2024.112238_b0365","doi-asserted-by":"crossref","first-page":"S41","DOI":"10.1016\/S0049-3848(19)30366-4","article-title":"Managing antiphospholipid syndrome in pregnancy","volume":"181","author":"Schreiber","year":"2019","journal-title":"Thromb. Res."},{"issue":"2\u20133","key":"10.1016\/j.intimp.2024.112238_b0370","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s12016-009-8144-z","article-title":"Predictors of pregnancy outcome in antiphospholipid syndrome: a review","volume":"38","author":"De Carolis","year":"2010","journal-title":"Clin Rev Allergy Immunol"},{"key":"10.1016\/j.intimp.2024.112238_b0375","doi-asserted-by":"crossref","unstructured":"S. Miyakis, M.D. Lockshin, T. Atsumi, D.W. Branch, R.L. Brey, R. Cervera, R.H. Derksen, D.E.G. PG, T. Koike, P.L. Meroni, G. Reber, Y. Shoenfeld, A. Tincani, P.G. Vlachoyiannopoulos, S.A. Krilis, International consensus statement on an update of the classification criteria for definite antiphospholipid syndrome (APS), Journal of thrombosis and haemostasis : JTH 4(2) (2006) 295-306.","DOI":"10.1111\/j.1538-7836.2006.01753.x"},{"issue":"10","key":"10.1016\/j.intimp.2024.112238_b0380","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1056\/NEJMra002974","article-title":"The antiphospholipid syndrome","volume":"346","author":"Levine","year":"2002","journal-title":"N. Engl. J. Med."},{"issue":"22","key":"10.1016\/j.intimp.2024.112238_b0385","doi-asserted-by":"crossref","DOI":"10.3390\/jcm11226822","article-title":"Early Prediction of Adverse Pregnancy Outcome in Women with Systemic Lupus Erythematosus, Antiphospholipid Syndrome, or Non-Criteria Obstetric Antiphospholipid Syndrome","volume":"11","author":"Ba\u00f1os","year":"2022","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.intimp.2024.112238_b0390","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.thromres.2021.05.008","article-title":"Deciphered coagulation profile to diagnose the antiphospholipid syndrome using artificial intelligence","volume":"203","author":"de Laat-Kremers","year":"2021","journal-title":"Thromb. Res."},{"issue":"5","key":"10.1016\/j.intimp.2024.112238_b0395","first-page":"1017","article-title":"Early recognition of catastrophic antiphospholipid syndrome in patients with antiphospholipid syndrome based on a Chinese cohort study","volume":"41","author":"Huang","year":"2023","journal-title":"Clin. Exp. Rheumatol."},{"issue":"13","key":"10.1016\/j.intimp.2024.112238_b0400","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1001\/jama.2018.13103","article-title":"Diagnosis and Management of Rheumatoid Arthritis: A Review","volume":"320","author":"Aletaha","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.intimp.2024.112238_b0405","doi-asserted-by":"crossref","unstructured":"J.S. Chen, C.L. Roberts, J.M. Simpson, L.M. March, Pregnancy Outcomes in Women With Rare Autoimmune Diseases, Arthritis & rheumatology (Hoboken, N.J.) 67(12) (2015) 3314-23.","DOI":"10.1002\/art.39311"},{"issue":"4","key":"10.1016\/j.intimp.2024.112238_b0410","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1093\/rheumatology\/keq350","article-title":"Pregnancy and reproduction in autoimmune rheumatic diseases","volume":"50","author":"Ostensen","year":"2011","journal-title":"Rheumatology (oxford)"},{"key":"10.1016\/j.intimp.2024.112238_b0415","doi-asserted-by":"crossref","unstructured":"L.R. Sammaritano, B.L. Bermas, E.E. Chakravarty, C. Chambers, M.E.B. Clowse, M.D. Lockshin, W. Marder, G. Guyatt, D.W. Branch, J. Buyon, L. Christopher-Stine, R. Crow-Hercher, J. Cush, M. Druzin, A. Kavanaugh, C.A. Laskin, L. Plante, J. Salmon, J. Simard, E.C. Somers, V. Steen, S.K. Tedeschi, E. Vinet, C.W. White, J. Yazdany, M. Barbhaiya, B. Bettendorf, A. Eudy, A. Jayatilleke, A.A. Shah, N. Sullivan, L.L. Tarter, M. Birru Talabi, M. Turgunbaev, A. Turner, K.E. D'Anci, 2020 American College of Rheumatology Guideline for the Management of Reproductive Health in Rheumatic and Musculoskeletal Diseases, Arthritis & rheumatology (Hoboken, N.J.) 72(4) (2020) 529-556.","DOI":"10.1002\/art.41191"},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0420","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s10067-022-06436-0","article-title":"Maternal and fetal outcomes in pregnant women with rheumatoid arthritis: a systematic review and meta-analysis","volume":"42","author":"Huang","year":"2023","journal-title":"Clin. Rheumatol."},{"issue":"2","key":"10.1016\/j.intimp.2024.112238_b0425","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s11517-020-02294-7","article-title":"Computer-aided diagnosis based on hand thermal, RGB Images, and Grip Force Using Artificial Intelligence as Screening Tool for Rheumatoid Arthritis in Women","volume":"59","author":"Alarc\u00f3n-Paredes","year":"2021","journal-title":"Medical & Biological Engineering & Computing"},{"issue":"9","key":"10.1016\/j.intimp.2024.112238_b0430","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1002\/acr2.11068","article-title":"Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach","volume":"1","author":"Feldman","year":"2019","journal-title":"ACR Open Rheumatology"},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0435","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s13075-022-02800-2","article-title":"Identification of biomarkers by machine learning classifiers to assist diagnose rheumatoid arthritis-associated interstitial lung disease","volume":"24","author":"Qin","year":"2022","journal-title":"Arthritis Res. Ther."},{"issue":"11","key":"10.1016\/j.intimp.2024.112238_b0440","doi-asserted-by":"crossref","first-page":"e23930","DOI":"10.2196\/23930","article-title":"Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study","volume":"8","author":"Maarseveen","year":"2020","journal-title":"JMIR Med. Inform."},{"issue":"6","key":"10.1016\/j.intimp.2024.112238_b0445","doi-asserted-by":"crossref","first-page":"e1011073","DOI":"10.1371\/journal.pcbi.1011073","article-title":"Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs","volume":"19","author":"Kalweit","year":"2023","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.intimp.2024.112238_b0450","doi-asserted-by":"crossref","first-page":"16047","DOI":"10.1038\/nrdp.2016.47","article-title":"Sj\u00f6gren syndrome","volume":"2","author":"Brito-Zer\u00f3n","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"10.1016\/j.intimp.2024.112238_b0455","doi-asserted-by":"crossref","first-page":"iii","DOI":"10.1016\/j.coi.2019.11.006","article-title":"Complexity and heterogeneity - the defining features of autoimmune disease","volume":"61","author":"Sanz","year":"2019","journal-title":"Curr. Opin. Immunol."},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0460","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.cell.2020.03.007","article-title":"Challenges, Progress, and Prospects of Developing Therapies to Treat Autoimmune Diseases","volume":"181","author":"Fugger","year":"2020","journal-title":"Cell"},{"issue":"1","key":"10.1016\/j.intimp.2024.112238_b0465","doi-asserted-by":"crossref","first-page":"3523","DOI":"10.1038\/s41467-021-23472-7","article-title":"A new molecular classification to drive precision treatment strategies in primary Sj\u00f6gren's syndrome","volume":"12","author":"Soret","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.intimp.2024.112238_b0470","doi-asserted-by":"crossref","DOI":"10.1016\/j.semarthrit.2023.152252","article-title":"Trends in adverse pregnancy outcomes among women with systemic sclerosis in the United States","volume":"63","author":"Kawano","year":"2023","journal-title":"Semin. Arthritis Rheum."},{"key":"10.1016\/j.intimp.2024.112238_b0475","doi-asserted-by":"crossref","unstructured":"I. Miniati, S. Guiducci, F. Mecacci, G. Mello, M. Matucci-Cerinic, Pregnancy in systemic sclerosis, Rheumatology (Oxford, England) 47 Suppl 3 (2008) iii16-8.","DOI":"10.1093\/rheumatology\/ken174"},{"issue":"6","key":"10.1016\/j.intimp.2024.112238_b0480","doi-asserted-by":"crossref","first-page":"881","DOI":"10.3899\/jrheum.181460","article-title":"Pregnancy in Systemic Sclerosis: Results of a Systematic Review and Metaanalysis","volume":"47","author":"Blagojevic","year":"2020","journal-title":"J. Rheumatol."},{"key":"10.1016\/j.intimp.2024.112238_b0485","doi-asserted-by":"crossref","unstructured":"K. Tang, J. Zhou, Y. Lan, H. Zhang, H. Jin, Pregnancy in adult-onset dermatomyositis\/polymyositis: A systematic review, American journal of reproductive immunology (New York, N.Y. : 1989) 88(5) (2022) e13603.","DOI":"10.1111\/aji.13603"},{"issue":"8\u20139","key":"10.1016\/j.intimp.2024.112238_b0490","first-page":"606","article-title":"Pregnancy in dermatomyositis and polymyositis","volume":"90","author":"Kaddour","year":"2012","journal-title":"Tunis. Med."},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0495","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1002\/art.1780270307","article-title":"Polymyositis\/dermatomyositis and pregnancy","volume":"27","author":"Guti\u00e9rrez","year":"1984","journal-title":"Arthritis Rheum."},{"issue":"9","key":"10.1016\/j.intimp.2024.112238_b0500","doi-asserted-by":"crossref","first-page":"3465","DOI":"10.1007\/s10067-021-05588-9","article-title":"Pregnancy outcomes in women with ankylosing spondylitis: a scoping literature and methodological review","volume":"40","author":"Mokbel","year":"2021","journal-title":"Clin. Rheumatol."},{"issue":"17","key":"10.1016\/j.intimp.2024.112238_b0505","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.1080\/14767058.2019.1671329","article-title":"Pregnancy outcomes in women with primary systemic vasculitis: a retrospective study","volume":"34","author":"Nguyen","year":"2021","journal-title":"The Journal of Maternal-Fetal & Neonatal Medicine : the Official Journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet"},{"issue":"3","key":"10.1016\/j.intimp.2024.112238_b0510","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1097\/AOG.0000000000002494","article-title":"Implementing Innovations in Global Women's, Children's, and Adolescents' Health: Realizing the Potential for Implementation Science","volume":"131","author":"Peterson","year":"2018","journal-title":"Obstet. Gynecol."},{"issue":"10293","key":"10.1016\/j.intimp.2024.112238_b0515","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1016\/S0140-6736(20)32708-2","article-title":"A Lancet Commission on 70 years of women's reproductive, maternal, newborn, child, and adolescent health in China","volume":"397","author":"Qiao","year":"2021","journal-title":"Lancet (london, England)"},{"issue":"11","key":"10.1016\/j.intimp.2024.112238_b0520","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1111\/aogs.13007","article-title":"Prevalence of maternal chronic diseases during pregnancy - a nationwide population based study from 1989 to 2013","volume":"95","author":"J\u00f8lving","year":"2016","journal-title":"Acta Obstet. Gynecol. Scand."},{"key":"10.1016\/j.intimp.2024.112238_b0525","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/1471-2393-14-75","article-title":"Chronic diseases in pregnant women: prevalence and birth outcomes based on the SNiP-study","volume":"14","author":"Kersten","year":"2014","journal-title":"BMC Pregnancy Childbirth"},{"key":"10.1016\/j.intimp.2024.112238_b0530","doi-asserted-by":"crossref","unstructured":"P.M. Vengetesh, S. Hebbar, L.J.I.j.o.r. Rai, contraception, obstetrics, gynecology, Autoimmune diseases in pregnancy: maternal and fetal outcomes, 4 (2015) 9-14.","DOI":"10.5455\/2320-1770.ijrcog20150202"},{"key":"10.1016\/j.intimp.2024.112238_b0535","first-page":"58","article-title":"Significance of machine learning in healthcare: Features, pillars and applications, International Journal of Intelligent","volume":"3","author":"Javaid","year":"2022","journal-title":"Networks"},{"key":"10.1016\/j.intimp.2024.112238_b0540","doi-asserted-by":"crossref","DOI":"10.1016\/j.health.2023.100285","article-title":"A Medical Cyber-physical system for predicting maternal health in developing countries using machine learning","volume":"5","author":"Hossain","year":"2024","journal-title":"Healthcare Analytics"},{"issue":"4","key":"10.1016\/j.intimp.2024.112238_b0545","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke and Vascular Neurology"}],"container-title":["International Immunopharmacology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1567576924007574?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1567576924007574?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T14:05:01Z","timestamp":1735308301000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1567576924007574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":109,"alternative-id":["S1567576924007574"],"URL":"https:\/\/doi.org\/10.1016\/j.intimp.2024.112238","relation":{},"ISSN":["1567-5769"],"issn-type":[{"value":"1567-5769","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges","name":"articletitle","label":"Article Title"},{"value":"International Immunopharmacology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.intimp.2024.112238","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"112238"}}