{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:23:10Z","timestamp":1790792590356,"version":"4.1.0"},"reference-count":84,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Background and aim<\/jats:title><jats:p>Available evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data were extracted from an EHR system to construct an ML dataset in 672\u00d7384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, \u03b1-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Multiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2022.1011492","type":"journal-article","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T05:33:52Z","timestamp":1665639232000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients"],"prefix":"10.3389","volume":"13","author":[{"given":"Xiuqing","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinqing","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanqing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dewei","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuguan","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1152\/physrev.2000.80.4.1523","article-title":"Prolactin: structure, function, and regulation of secretion","volume":"80","author":"Freeman","year":"2000","journal-title":"Physiol Rev"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1530\/REP-10-0033","article-title":"Prolactin secretion patterns: basic mechanisms and clinical implications for reproduction","volume":"140","author":"Egli","year":"2010","journal-title":"Reproduction"},{"key":"B3","article-title":"Pharmacological causes of hyperprolactinemia","volume":"3","author":"Torre","year":"2007","journal-title":"Ther Clin Risk Manage"},{"key":"B4","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/s0306-4530(02)00112-9","article-title":"Elevated prolactin levels in patients with schizophrenia: mechanisms and related adverse effects","author":"Halbreich","year":"2003","journal-title":"Psychoneuroendocrinology"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1055\/s-0043-107243","article-title":"Hyperprolactinemia and the association with all-cause mortality and cardiovascular mortality","volume":"49","author":"Krogh","year":"2017","journal-title":"Horm Metab Res"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1530\/EJE-11-0076","article-title":"Cancer risk in hyperprolactinemia patients: A population-based cohort study","volume":"165","author":"Berinder","year":"2011","journal-title":"Eur J Endocrinol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1007\/s11102-020-01041-3","article-title":"Hyperprolactinemia and bone","volume":"23","author":"di Filippo","year":"2020","journal-title":"Pituitary"},{"key":"B8","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1177\/0269881107087951","article-title":"Medical causes and consequences of hyperprolactinaemia","volume":"22","author":"Holt","year":"2008","journal-title":"A Context Psychiatrists J Psychopharmacol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/S2215-0366(21)00241-8","article-title":"Antipsychotic use and risk of breast cancer in women with schizophrenia: A nationwide nested case-control study in Finland","volume":"8","author":"Taipale","year":"2021","journal-title":"Lancet Psychiatry"},{"key":"B10","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s13058-018-0969-z","article-title":"Hyperprolactinemia-inducing antipsychotics increase breast cancer risk by activating JAK-STAT5 in precancerous lesions","volume":"20","author":"Johnston","year":"2018","journal-title":"Breast Cancer Res"},{"key":"B11","doi-asserted-by":"publisher","first-page":"38","DOI":"10.2174\/1871530317666170424102332","article-title":"Hyperprolactinemia induced by antipsychotics: from diagnosis to treatment approach","volume":"17","author":"Milano","year":"2017","journal-title":"Endocr Metab Immune Disord Drug Targets"},{"key":"B12","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1002\/jat.1723","article-title":"Preclinical risk assessment of drug-induced hypo- and hyperprolactinemia","volume":"31","author":"Hargreaves","year":"2011","journal-title":"J Appl Toxicol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.yrtph.2010.08.001","article-title":"The antipsychotics haloperidol and chlorpromazine increase bone metabolism and induce osteopenia in female rats","volume":"58","author":"Kunimatsu","year":"2010","journal-title":"Regul Toxicol Pharmacol"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1517\/17425255.2013.791283","article-title":"Drug-induced hypo- and hyperprolactinemia: mechanisms, clinical and therapeutic consequences","volume":"9","author":"Voicu","year":"2013","journal-title":"Expert Opin Drug Metab Toxicol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1007\/s40263-014-0157-3","article-title":"The effects of novel and newly approved antipsychotics on serum prolactin levels: A comprehensive review","volume":"28","author":"Peuskens","year":"2014","journal-title":"CNS Drugs"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.2165\/00003495-200161010-00011","article-title":"Olanzapine: an updated review of its use in the management of schizophrenia","volume":"61","author":"Bhana","year":"2001","journal-title":"Drugs"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1172\/JCI93362","article-title":"The atypical antipsychotic olanzapine causes weight gain by targeting serotonin receptor 2C","volume":"127","author":"Lord","year":"2017","journal-title":"J Clin Invest"},{"key":"B18","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1111\/j.1749-6632.1990.tb16887.x","article-title":"Endocrinological responses to 5-HT","volume":"600","author":"Cowen","year":"1990","journal-title":"Ann N Y Acad Sci"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2021.718800","article-title":"The relationships between hyperprolactinemia, metabolic disturbance, and sexual dysfunction in patients with schizophrenia under olanzapine treatment","volume":"12","author":"Wu","year":"2021","journal-title":"Front Pharmacol"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1002\/hup.2308","article-title":"Polymorphisms influencing olanzapine metabolism and adverse effects in healthy subjects","volume":"28","author":"Cabaleiro","year":"2013","journal-title":"Hum Psychopharmacol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"e100206","DOI":"10.1136\/gpsych-2020-100206","article-title":"Risk factors of hyperprolactinemia induced by risperidone and olanzapine and their correlations with plasma glucose and lipids","volume":"33","author":"He","year":"2020","journal-title":"Gen Psychiatr"},{"key":"B22","article-title":"Alterations in the mitochondria of rat spermatozoa after experimental hyperprolactinemia","volume":"37","author":"Laszczy\u0144ska","year":"1999","journal-title":"Folia Histochem Cytobiol"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1007\/s00441-019-03067-x","article-title":"The role of oxidative stress in ovarian toxicity induced by haloperidol and clozapine-a histological and biochemical study in albino rats","volume":"378","author":"Khalaf","year":"2019","journal-title":"Cell Tissue Res"},{"key":"B24","doi-asserted-by":"publisher","first-page":"16","DOI":"10.4088\/pcc.08m00668","article-title":"Prolactin levels in olanzapine treatment correlate with positive symptoms of schizophrenia: results from an open-label, flexible-dose study","volume":"11","author":"Chen","year":"2009","journal-title":"Prim Care Companion J Clin Psychiatry"},{"key":"B25","doi-asserted-by":"publisher","first-page":"84","DOI":"10.15406\/oajtmr.2018.02.00043","article-title":"Review of serum prolactin levels as an antipsychotic-response biomarker","volume":"2","author":"Gault","year":"2018","journal-title":"Open Access J Transl Med Res"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.4088\/PCC.11l01155","article-title":"Prolactin-a biomarker for antipsychotic treatment response","volume":"13","author":"Agarwal","year":"2011","journal-title":"Prim Care Companion CNS Disord"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinthera.2020.12.014","article-title":"Artificial intelligence in pharmacovigilance: scoping points to consider","volume":"43","author":"Hauben","year":"2021","journal-title":"Clin Ther"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2019.03.003","article-title":"Machine learning on adverse drug reactions for pharmacovigilance","volume":"24","author":"Lee","year":"2019","journal-title":"Drug Discov Today"},{"key":"B29","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s40264-015-0352-2","article-title":"Feasibility of prioritizing drug-drug-event associations found in electronic health records","volume":"39","author":"Banda","year":"2016","journal-title":"Drug Saf"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejon.2021.102066","article-title":"Development of a prediction models for chemotherapy-induced adverse drug reactions: A retrospective observational study using electronic health records","volume":"56","author":"On","year":"2022","journal-title":"Eur J Oncol Nurs"},{"key":"B31","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1111\/acps.13250","article-title":"Combining machine learning algorithms for prediction of antidepressant treatment response","volume":"143","author":"Kautzky","year":"2021","journal-title":"Acta Psychiatr Scand"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04278-5","article-title":"Multi-omic machine learning predictor of breast cancer therapy response","volume":"601","author":"Sammut","year":"2022","journal-title":"Nature"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.118.024293","article-title":"Machine learning-based model for prediction of outcomes in acute stroke","volume":"50","author":"Heo","year":"2019","journal-title":"Stroke"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1001\/jamacardio.2021.0122","article-title":"Use of machine learning models to predict death after acute myocardial infarction","volume":"6","author":"Khera","year":"2021","journal-title":"JAMA Cardiol"},{"key":"B35","article-title":"A unified approach to interpreting model predictions","volume-title":"Proceedings of the 31st international conference on neural information processing systems","author":"Lundberg","year":"2017"},{"key":"B36","doi-asserted-by":"publisher","first-page":"e0157753","DOI":"10.1371\/journal.pone.0157753","article-title":"OpenVigil FDA - inspection of U.S. American adverse drug events pharmacovigilance data and novel clinical applications","volume":"11","author":"B\u00f6hm","year":"2016","journal-title":"PloS One"},{"key":"B37","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/s12911-016-0318-z","article-title":"Nearest neighbor imputation algorithms: A critical evaluation","author":"Beretta","year":"2016","journal-title":"BMC Med Inform Decis Mak"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: A new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.21037\/jtd-21-1107","article-title":"Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer","volume":"13","author":"Gong","year":"2021","journal-title":"J Thorac Dis"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9979560","article-title":"Keratoconus severity classification using features selection and machine learning algorithms","volume":"2021","author":"Aatila","year":"2021","journal-title":"Comput Math Methods Med"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14030623","article-title":"Advances in and the applicability of machine learning-based screening and early detection approaches for cancer: A primer","volume":"14","author":"Benning","year":"2022","journal-title":"Cancers (Basel)"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/S2215-0366(16)30171-7","article-title":"Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: A machine learning approach","volume":"3","author":"Koutsouleris","year":"2016","journal-title":"Lancet Psychiatry"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2020.00415","article-title":"Identifying direct coercion in a high risk subgroup of offender patients with schizophrenia via machine learning algorithms","volume":"11","author":"G\u00fcnther","year":"2020","journal-title":"Front Psychiatry"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2020.100108","article-title":"Using machine learning to identify adverse drug effects posing increased risk to women","volume":"1","author":"Chandak","year":"2020","journal-title":"Patterns (N Y)"},{"key":"B45","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: A scalable tree boosting system","volume-title":"KDD \u201816: proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining 2016","author":"Chen","year":"2016"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.3390\/e23010018","article-title":"Explainable AI: A review of machine learning interpretability methods","volume":"23","author":"Linardatos","year":"2020","journal-title":"Entropy (Basel)"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0679-x","article-title":"Explaining prediction models and individual predictions with feature contributions","volume":"41","author":"\u0160trumbelj","year":"2014","journal-title":"Knowl Inf Syst"},{"key":"B48","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1186\/s12967-021-02990-4","article-title":"An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation","volume":"19","author":"Zhang","year":"2021","journal-title":"J Transl Med"},{"key":"B49","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1055\/s-0043-116492","article-title":"Consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology: update 2017","volume":"51","author":"Hiemke","year":"2018","journal-title":"Pharmacopsychiatry"},{"key":"B50","article-title":"Hyperprolactinemia associated with psychiatric disorders","volume":"80","author":"Rizzo","year":"2020","journal-title":"Med (B Aires)"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1016\/j.rpsm.2015.11.003","article-title":"Spanish Consensus on the risks and detection of antipsychotic drug-related hyperprolactinaemia","volume":"9","author":"Montejo","year":"2016","journal-title":"Rev Psiquiatr Salud Ment"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1097\/00004714-200204000-00002","article-title":"The effects of antipsychotic-induced hyperprolactinaemia on the hypothalamic-pituitary-gonadal axis","volume":"22","author":"Smith","year":"2002","journal-title":"J Clin Psychopharmacol"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1093\/geronj\/44.4.m131","article-title":"Serum prolactin and aging: basal values and changes with estrogen use and hypothyroidism","volume":"44","author":"Sawin","year":"1989","journal-title":"J Gerontol"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnpbp.2020.109941","article-title":"Antipsychotic-induced hyperprolactinemia in aging populations: prevalence, implications, prevention and management","volume":"101","author":"Gonz\u00e1lez-Rodr\u00edguez","year":"2020","journal-title":"Prog Neuropsychopharmacol Biol Psychiatry"},{"key":"B55","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s12958-018-0406-2","article-title":"Reactive oxygen species and male reproductive hormones","volume":"16","author":"Darbandi","year":"2018","journal-title":"Reprod Biol Endocrinol"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1210\/endo-110-4-1085","article-title":"Divergent effects of prolactin on estrogen and progesterone production by granulosa cells of rat graafian follicles","volume":"110","author":"Wang","year":"1982","journal-title":"Endocrinology"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.4065\/80.8.1050","article-title":"Medication-induced hyperprolactinemia","volume":"80","author":"Molitch","year":"2005","journal-title":"Mayo Clin Proc"},{"key":"B58","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1017\/s1092852900002340","article-title":"Management of hyperprolactinemia in patients receiving antipsychotics","volume":"9","author":"Miller","year":"2004","journal-title":"CNS Spectr"},{"key":"B59","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1038\/s41398-022-02027-4","article-title":"Pharmacological treatment strategies for antipsychotic-induced hyperprolactinemia: A systematic review and network meta-analysis","volume":"12","author":"Lu","year":"2022","journal-title":"Transl Psychiatry"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.3109\/09537104.2010.494743","article-title":"Platelet distribution width and the extent of coronary artery disease: results from a large prospective study","volume":"21","author":"De Luca","year":"2010","journal-title":"Platelets"},{"key":"B61","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/s12020-009-9304-x","article-title":"Macroprolactinemia, like hyperprolactinemia, may promote platelet activation","volume":"37","author":"Anaforoglu","year":"2010","journal-title":"Endocrine"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1097\/01.jcp.0000088914.24613.51","article-title":"Hyperprolactinemia in patients on antipsychotic drugs causes ADP-stimulated platelet activation that might explain the increased risk for venous thromboembolism: pilot study","volume":"23","author":"Wallaschofski","year":"2003","journal-title":"J Clin Psychopharmacol"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1016\/S1567-5688(09)70307-1","article-title":"Prolactin does not affect human platelet aggregation or secretion","volume":"101","author":"Reuwer","year":"2009","journal-title":"Thromb Haemost"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.3109\/00365513.2013.765963","article-title":"Effects of prolactin on platelet activation and blood clotting","volume":"73","author":"Wahlberg","year":"2013","journal-title":"Scand J Clin Lab Invest"},{"key":"B65","first-page":"28","article-title":"Platelet distribution width: A simple, practical and specific marker of activation of coagulation","volume":"14","author":"Vagdatli","year":"2010","journal-title":"Hippokratia"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2002-021137","article-title":"Identification of functional prolactin (PRL) receptor gene expression: PRL inhibits lipoprotein lipase activity in human white adipose tissue","volume":"88","author":"Ling","year":"2003","journal-title":"J Clin Endocrinol Metab"},{"key":"B67","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s40842-016-0024-x","article-title":"Lipoprotein (a), an independent cardiovascular risk marker","volume":"2","author":"Saeedi","year":"2016","journal-title":"Clin Diabetes Endocrinol"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.1530\/acta.0.0850372","article-title":"Increased serum prolactin in diabetic ketoacidosis; correlation between serum sodium and serum prolacting concentration","volume":"85","author":"Hanssen","year":"1977","journal-title":"Acta Endocrinol (Copenh)"},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.1126\/science.7302575","article-title":"Stimulation of intestinal calcium transport and bone calcium mobilization by prolactin in vitamin d-deficient rats","volume":"214","author":"Pahuja","year":"1981","journal-title":"Science"},{"key":"B70","article-title":"Role of prolactin on epididymal glycoprotein metabolism in matured monkeys, macaca radiata: specific activities of glycosyltransferases and glycosidases","volume":"30","author":"Jayakumar","year":"1992","journal-title":"Indian J Exp Biol"},{"key":"B71","doi-asserted-by":"publisher","DOI":"10.1007\/s40264-022-01170-7","article-title":"Artificial intelligence-based pharmacovigilance in the setting of limited resources","volume":"45","author":"Liang","year":"2022","journal-title":"Drug Saf"},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2022.874705","article-title":"Risperidone induced hyperprolactinemia: From basic to clinical studies","volume":"13","author":"Stojkovic","year":"2022","journal-title":"Front Psychiatry"},{"key":"B73","doi-asserted-by":"publisher","DOI":"10.1007\/s00213-005-0230-1","article-title":"Effects of the atypical antipsychotics olanzapine and risperidone on plasma prolactin levels in male rats: A comparison with clinical data","volume":"184","author":"Rourke","year":"2006","journal-title":"Psychopharmacol (Berl)"},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.1007\/s00213-017-4730-6","article-title":"Antipsychotic-induced hyperprolactinemia: synthesis of world-wide guidelines and integrated recommendations for assessment, management and future research","volume":"234","author":"Grigg","year":"2017","journal-title":"Psychopharmacol (Berl)"},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.1093\/ajhp\/zxab065","article-title":"Guidance on the treatment of antipsychotic-induced hyperprolactinemia when switching the antipsychotic is not an option","volume":"78","author":"Rusgis","year":"2021","journal-title":"Am J Health Syst Pharm"},{"key":"B76","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2019.00846","article-title":"Olanzapine-induced hyperprolactinemia: two case reports","volume":"10","author":"Barata","year":"2019","journal-title":"Front Pharmacol"},{"key":"B77","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1186\/s12888-020-02957-7","article-title":"Abnormally low prolactin levels in schizophrenia patients after switching to aripiprazole in a randomized trial: A biomarker for rebound in psychotic symptoms","volume":"20","author":"Jen","year":"2020","journal-title":"BMC Psychiatry"},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.3109\/00498254.2015.1078522","article-title":"Involvement of oxidative stress and mitochondrial\/lysosomal cross-talk in olanzapine cytotoxicity in freshly isolated rat hepatocytes","volume":"46","author":"Eftekhari","year":"2016","journal-title":"Xenobiotica"},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.3390\/molecules26227031","article-title":"Nanoantioxidants: pioneer types, advantages, limitations, and future insights","volume":"26","author":"Omran","year":"2021","journal-title":"Molecules"},{"key":"B80","doi-asserted-by":"publisher","DOI":"10.3390\/antiox9010024","article-title":"Nanoantioxidants: recent trends in antioxidant delivery applications","volume":"9","author":"Khalil","year":"2019","journal-title":"Antioxid (Basel)"},{"key":"B81","doi-asserted-by":"publisher","DOI":"10.22037\/ijpr.2019.1100758","article-title":"Comparative study of the effects of chamomile (matricaria chamomilla l.) and cabergoline on idiopathic hyperprolactinemia: A pilot randomized controlled trial","volume":"18","author":"Kabiri","year":"2019","journal-title":"Iran J Pharm Res"},{"key":"B82","doi-asserted-by":"publisher","DOI":"10.1177\/00368504211029777","article-title":"Machine learning on small size samples: a synthetic knowledge synthesis","volume":"105","author":"Kokol","year":"2022","journal-title":"Sci Prog"},{"key":"B83","doi-asserted-by":"publisher","DOI":"10.1038\/s41397-019-0142-9","article-title":"The effect of CYP2D6 variation on antipsychotic-induced hyperprolactinaemia: A systematic review and meta-analysis","volume":"20","author":"Calafato","year":"2020","journal-title":"Pharmacogenom J"},{"key":"B84","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2009.06.014","article-title":"Genetic associations of prolactin increase in olanzapine\/fluoxetine combination-treated patients","volume":"175","author":"Houston","year":"2010","journal-title":"Psychiatry Res"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2022.1011492\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T05:34:03Z","timestamp":1665639243000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2022.1011492\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"references-count":84,"alternative-id":["10.3389\/fendo.2022.1011492"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2022.1011492","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,13]]},"article-number":"1011492"}}