{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:06:43Z","timestamp":1788955603459,"version":"build-2803163510"},"reference-count":78,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jns-journal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the Neurological Sciences"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1016\/j.jns.2022.120335","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T23:44:48Z","timestamp":1657323888000},"page":"120335","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"special_numbering":"C","title":["Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records"],"prefix":"10.1016","volume":"440","author":[{"given":"Jundong","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth L.","family":"Chou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kui Kai","family":"Lau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter Y.M.","family":"Woo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kei Hang Katie","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jns.2022.120335_bb0005","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1016\/S0895-7061(00)01249-8","article-title":"Etiology and pathophysiology of stroke as a complex trait","volume":"13","author":"Rubattu","year":"2000","journal-title":"Am. J. Hypertens."},{"key":"10.1016\/j.jns.2022.120335_bb0010","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1161\/STROKEAHA.108.530725","article-title":"Stroke risk profiles","volume":"40","author":"Wolf","year":"2009","journal-title":"Stroke."},{"key":"10.1016\/j.jns.2022.120335_bb0015","doi-asserted-by":"crossref","first-page":"A153","DOI":"10.1161\/str.51.suppl_1.153","article-title":"Novel machine learning proves stroke risk is not linear","volume":"51","author":"Orfanoudaki","year":"2020","journal-title":"Stroke."},{"key":"10.1016\/j.jns.2022.120335_bb0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0213653","article-title":"Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants","volume":"14","author":"Alaa","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.jns.2022.120335_bb0025","series-title":"ArXiv Prepr. ArXiv1802.07207","article-title":"AutoPrognosis: automated clinical prognostic modeling via Bayesian optimization with structured kernel learning","author":"Alaa","year":"2018"},{"key":"10.1016\/j.jns.2022.120335_bb0030","first-page":"1","article-title":"Principles of tumors: a translational approach to foundations","author":"Bignold","year":"2019","journal-title":"Princ. Tumors A Transl Approach Found."},{"key":"10.1016\/j.jns.2022.120335_bb0035","series-title":"ArXiv Prepr. ArXiv1708.07480","article-title":"An ensemble classifier for predicting the onset of type II diabetes","author":"Semerdjian","year":"2017"},{"key":"10.1016\/j.jns.2022.120335_bb0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1472-6947-10-16","article-title":"Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes","volume":"10","author":"Yu","year":"2010","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"10.1016\/j.jns.2022.120335_bb0045","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1186\/s12911-019-0918-5","article-title":"A data-driven approach to predicting diabetes and cardiovascular disease with machine learning","volume":"19","author":"Dinh","year":"2019","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"10.1016\/j.jns.2022.120335_bb0050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1136\/bmjopen-2019-032703","article-title":"Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data","volume":"9","author":"Rigdon","year":"2019","journal-title":"BMJ Open"},{"key":"10.1016\/j.jns.2022.120335_bb0055","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s12603-020-1430-4","article-title":"Risk factors for stroke based on the National Health and nutrition examination survey","volume":"24","author":"Mai","year":"2020","journal-title":"J. Nutr. Health Aging"},{"issue":"5","key":"10.1016\/j.jns.2022.120335_bb0060","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1016\/j.numecd.2021.01.011","article-title":"Associations between calf, thigh, and arm circumference and cardiovascular and all-cause mortality in NHANES 1999-2004","volume":"31","author":"Abreo","year":"2021","journal-title":"Nutr. Metab. Cardiovasc. Dis."},{"key":"10.1016\/j.jns.2022.120335_bb0065","first-page":"19007872","article-title":"Estimating youth diabetes risk using NHANES data and machine learning","author":"Vangeepuram","year":"2020","journal-title":"MedRxiv."},{"key":"10.1016\/j.jns.2022.120335_bb0070","author":"U.K. Biobank"},{"key":"10.1016\/j.jns.2022.120335_bb0075","first-page":"518","article-title":"Evaluation of enzymes in serum and cerebrospinal fluid in cases of stroke","volume":"50","author":"Parakh","year":"2002","journal-title":"Neurol. India"},{"key":"10.1016\/j.jns.2022.120335_bb0080","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.2147\/TCRM.S174961","article-title":"Hematocrit and the incidence of stroke: a prospective, population-based cohort study","volume":"14","author":"Yang","year":"2018","journal-title":"Ther. Clin. Risk Manag."},{"key":"10.1016\/j.jns.2022.120335_bb0085","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12944-018-0950-y","article-title":"Assessing the performance of monocyte to high-density lipoprotein ratio for predicting ischemic stroke: insights from a population-based Chinese cohort","volume":"18","author":"Wang","year":"2019","journal-title":"Lipids Health Dis."},{"key":"10.1016\/j.jns.2022.120335_bb0090","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.amjmed.2008.03.045","article-title":"Serum total bilirubin level, prevalent stroke, and stroke outcomes: NHANES 1999-2004","volume":"121","author":"Perlstein","year":"2008","journal-title":"Am. J. Med."},{"key":"10.1016\/j.jns.2022.120335_bb0095","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0124957","article-title":"Red cell distribution width in relation to incidence of stroke and carotid atherosclerosis: a population-based cohort study","volume":"10","author":"S\u00f6derholm","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.jns.2022.120335_bb0100","doi-asserted-by":"crossref","first-page":"551","DOI":"10.4093\/dmj.2011.35.5.551","article-title":"Higher glycated hemoglobin level is associated with increased risk for ischemic stroke in non-diabetic Korean male adults","volume":"35","author":"Oh","year":"2011","journal-title":"Diabetes Metab. J."},{"key":"10.1016\/j.jns.2022.120335_bb0105","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1161\/01.STR.0000012344.35312.13","article-title":"Serum gamma-glutamyltransferase as a risk factor of ischemic stroke might be independent of alcohol consumption","volume":"33","author":"Emdin","year":"2002","journal-title":"Stroke."},{"key":"10.1016\/j.jns.2022.120335_bb0110","first-page":"1","article-title":"DataWig: missing value imputation for tables","volume":"20","author":"Bie\u00dfmann","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.jns.2022.120335_bb0115","series-title":"BoostARoota","author":"DeHan","year":"2017"},{"key":"10.1016\/j.jns.2022.120335_bb0120","doi-asserted-by":"crossref","first-page":"7225","DOI":"10.1039\/C6AY01574C","article-title":"Representative subset selection and outlier detection via isolation forest","volume":"8","author":"Chen","year":"2016","journal-title":"Anal. Methods"},{"key":"10.1016\/j.jns.2022.120335_bb0125","series-title":"Machine Learning Interpretability with H2O Driverless AI: First Edition Machine Learning Interpretability with H2O Driverless AI, H2O. Ai","first-page":"1","author":"Hall","year":"2019"},{"key":"10.1016\/j.jns.2022.120335_bb0130","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/TKDE.2006.17","article-title":"Training cost-sensitive neural networks with methods addressing the class imbalance problem","volume":"18","author":"Zhou","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.jns.2022.120335_bb0135","series-title":"ICML","first-page":"448","article-title":"Learning with positive and unlabeled examples using weighted logistic regression","author":"Lee","year":"2003"},{"key":"10.1016\/j.jns.2022.120335_bb0140","series-title":"Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min","first-page":"2623","article-title":"Optuna: a next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"issue":"13","key":"10.1016\/j.jns.2022.120335_bb0145","first-page":"54","article-title":"Evaluation of classification algorithms with solutions to class imbalance problem on bank marketing dataset using WEKA, IJACSA","volume":"5","author":"Verma","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.jns.2022.120335_bb0150","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.bdr.2015.12.001","article-title":"Finding the best classification threshold in imbalanced classification","volume":"5","author":"Zou","year":"2016","journal-title":"Big Data Res."},{"key":"10.1016\/j.jns.2022.120335_bb0155","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: an open-source package for R and S+ to analyze and compare ROC curves","volume":"12","author":"Robin","year":"2011","journal-title":"BMC Bioinformat."},{"key":"10.1016\/j.jns.2022.120335_bb0160","series-title":"Package \u2018PredictABEL\u2019","author":"Kundu","year":"2020"},{"key":"10.1016\/j.jns.2022.120335_bb0165","first-page":"3","article-title":"Resampling methods in R: the boot package","volume":"2","author":"Canty","year":"2002","journal-title":"Newsl. R Proj."},{"key":"10.1016\/j.jns.2022.120335_bb0170","first-page":"4766","article-title":"A unified approach to interpreting model predictions, Adv. Neural Inf","author":"Lundberg","year":"2017","journal-title":"Process. Syst."},{"key":"10.1016\/j.jns.2022.120335_bb0175","doi-asserted-by":"crossref","DOI":"10.21037\/atm.2017.04.01","article-title":"Drawing nomograms with R: applications to categorical outcome and survival data","volume":"5","author":"Zhang","year":"2017","journal-title":"Ann. Transl. Med."},{"key":"10.1016\/j.jns.2022.120335_bb0180","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1161\/01.STR.25.1.40","article-title":"Stroke risk profile: adjustment for antihypertensive medication the Framingham study","volume":"25","author":"D\u2019Agostino","year":"1994","journal-title":"Stroke."},{"key":"10.1016\/j.jns.2022.120335_bb0185","article-title":"Caret: classification and regression training","author":"Kuhn","year":"2015","journal-title":"Astrophys. Source Code Libr."},{"key":"10.1016\/j.jns.2022.120335_bb0190","series-title":"ArXiv Prepr. ArXiv2012.06670","article-title":"Adaptive histogram-based gradient boosted trees for federated learning","author":"Ong","year":"2020"},{"key":"10.1016\/j.jns.2022.120335_bb0195","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","article-title":"Nearest neighbor selection for iteratively kNN imputation","volume":"85","author":"Zhang","year":"2012","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.jns.2022.120335_bb0200","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1002\/ana.25369","article-title":"Genome-wide meta-analysis identifies 3 novel loci associated with stroke","volume":"84","author":"Malik","year":"2018","journal-title":"Ann. Neurol."},{"key":"10.1016\/j.jns.2022.120335_bb0205","article-title":"Relationship of arterial stiffness index and pulse pressure with cardiovascular disease and mortality","volume":"7","author":"Abdullah Said","year":"2018","journal-title":"J. Am. Heart Assoc."},{"key":"10.1016\/j.jns.2022.120335_bb0210","series-title":"UK Biobank Phase 1 Outcomes Adjudication","article-title":"Definitions of asthma for UK Biobank phase 1 outcomes adjudication documentation prepared by: definitions of asthma","author":"Schnier","year":"2017"},{"key":"10.1016\/j.jns.2022.120335_bb0215","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable machine-learning predictions for the prevention of hypoxaemia during surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.jns.2022.120335_bb0220","first-page":"106","article-title":"Understanding waterfall plots","volume":"3","author":"Gillespie","year":"2012","journal-title":"J. Adv. Pract. Oncol."},{"key":"10.1016\/j.jns.2022.120335_bb0225","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0225253","article-title":"Visualising statistical models using dynamic nomograms","volume":"14","author":"Jalali","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.jns.2022.120335_bb0230","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","article-title":"Receiver operating characteristic curve in diagnostic test assessment","volume":"5","author":"Mandrekar","year":"2010","journal-title":"J. Thorac. Oncol."},{"key":"10.1016\/j.jns.2022.120335_bb0235","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1161\/STROKEAHA.119.028428","article-title":"Association of prediabetes and Type 2 diabetes with cognitive function after stroke: a STROKOG collaboration study","author":"Lo","year":"2020","journal-title":"Stroke."},{"key":"10.1016\/j.jns.2022.120335_bb0240","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1080\/10824669.2018.1523734","article-title":"Receiver operating characteristic (ROC) area under the curve (AUC): a diagnostic measure for evaluating the accuracy of predictors of education outcomes","volume":"24","author":"Bowers","year":"2019","journal-title":"J. Educ. Stud. Placed Risk"},{"key":"10.1016\/j.jns.2022.120335_bb0245","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40733-021-00069-x","article-title":"Asthma and stroke: a narrative review","volume":"7","author":"Corlateanu","year":"2021","journal-title":"Asthma Res. Pract."},{"issue":"5","key":"10.1016\/j.jns.2022.120335_bb0250","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1016\/j.chest.2020.05.615","article-title":"Self-reported snoring patterns predict stroke events in high-risk patients with obstructive sleep apnea: post-hoc analyses of the SAVE study","volume":"158","author":"Li","year":"2020","journal-title":"Chest."},{"key":"10.1016\/j.jns.2022.120335_bb0255","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1001\/jama.2017.0947","article-title":"Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States","volume":"317","author":"Micha","year":"2017","journal-title":"JAMA - J. Am. Med. Assoc."},{"key":"10.1016\/j.jns.2022.120335_bb0260","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0020416","article-title":"A comparative assessment of non-laboratory-based versus commonly used laboratory-based cardiovascular disease risk scores in the NHANES III population","volume":"6","author":"Pandya","year":"2011","journal-title":"PLoS One"},{"key":"10.1016\/j.jns.2022.120335_bb0265","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1016\/S0140-6736(08)60418-3","article-title":"Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I follow-up study cohort","volume":"371","author":"Gaziano","year":"2008","journal-title":"Lancet."},{"key":"10.1016\/j.jns.2022.120335_bb0270","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.dss.2018.06.011","article-title":"An investigation of bankruptcy prediction in imbalanced datasets","volume":"112","author":"Veganzones","year":"2018","journal-title":"Decis. Support. Syst."},{"key":"10.1016\/j.jns.2022.120335_bb0275","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1111\/ene.13411","article-title":"Post-stroke diastolic blood pressure and risk of recurrent vascular events","volume":"24","author":"Park","year":"2017","journal-title":"Eur. J. Neurol."},{"key":"10.1016\/j.jns.2022.120335_bb0280","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.jhydrol.2019.04.085","article-title":"Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions","volume":"574","author":"Huang","year":"2019","journal-title":"J. Hydrol."},{"key":"10.1016\/j.jns.2022.120335_bb0285","series-title":"Flexible Imputation of Missing Data","author":"Van Buuren","year":"2018"},{"key":"10.1016\/j.jns.2022.120335_bb0290","first-page":"1","article-title":"Mice: multivariate imputation by chained equations in R","volume":"45","author":"van Buuren","year":"2011","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.jns.2022.120335_bb0295","series-title":"Thirteen. Annu. Conf. Int. Speech Commun. Assoc","article-title":"LSTM neural networks for language modeling","author":"Sundermeyer","year":"2012"},{"key":"10.1016\/j.jns.2022.120335_bb0300","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1075\/ijcl.11.4.04che","article-title":"From n-gram to skipgram to concgram","volume":"11","author":"Cheng","year":"2006","journal-title":"Int. J. Corpus Linguist."},{"key":"10.1016\/j.jns.2022.120335_bb0305","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/14639220903470205","article-title":"A survey of methodologies for the treatment of missing values within datasets: limitations and benefits","volume":"12","author":"Young","year":"2011","journal-title":"Theor. Issues Ergon. Sci."},{"key":"10.1016\/j.jns.2022.120335_bb0310","first-page":"30","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer (Long Beach. Calif)."},{"key":"10.1016\/j.jns.2022.120335_bb0315","first-page":"1","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2015","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.jns.2022.120335_bb0320","series-title":"Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 13-17-Augu","first-page":"785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.jns.2022.120335_bb0325","first-page":"176","article-title":"Classification with class imbalance problem: a review","volume":"7","author":"Ali","year":"2015","journal-title":"Int. J. Adv. Soft Comput. Its Appl."},{"key":"10.1016\/j.jns.2022.120335_bb0330","series-title":"Proc. Int\u2019l Conf. Artif. Intell","article-title":"The class imbalance problem: Significance and strategies","author":"Japkowicz","year":"2000"},{"key":"10.1016\/j.jns.2022.120335_bb0335","doi-asserted-by":"crossref","first-page":"9776","DOI":"10.3390\/ijerph110909776","article-title":"Resampling methods improve the predictive power of modeling in class-imbalanced datasets","volume":"11","author":"Lee","year":"2014","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.jns.2022.120335_bb0340","series-title":"Iberoam. Congr. Pattern Recognit","first-page":"417","article-title":"A minority class feature selection method","author":"Cuaya","year":"2011"},{"key":"10.1016\/j.jns.2022.120335_bb0345","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1021\/acs.jcim.1c00160","article-title":"GHOST: adjusting the decision threshold to handle imbalanced data in machine learning","volume":"61","author":"Esposito","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.jns.2022.120335_bb0350","series-title":"ACM Int. Conf. Proceeding Ser. PartF16925","first-page":"72","article-title":"Threshold moving approaches for addressing the class imbalance problem and their application to multi-label classification","author":"Zhang","year":"2020"},{"key":"10.1016\/j.jns.2022.120335_bb0355","author":"Collell"},{"key":"10.1016\/j.jns.2022.120335_bb0360","doi-asserted-by":"crossref","first-page":"87","DOI":"10.37661\/1816-0301-2020-17-1-87-101","article-title":"Comparative study of quality estimation of binary classification","volume":"17","author":"Starovoitov","year":"2020","journal-title":"Informatics."},{"key":"10.1016\/j.jns.2022.120335_bb0365","series-title":"2020 IEEE 17th Int. Symp. Biomed. Imaging","first-page":"1","article-title":"J regularization improves imbalanced multiclass segmentation","author":"Pena","year":"2020"},{"key":"10.1016\/j.jns.2022.120335_bb0370","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-63259-2","article-title":"AFP-LSE: antifreeze proteins prediction using latent space encoding of composition of k-spaced amino acid pairs","volume":"10","author":"Usman","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jns.2022.120335_bb0375","series-title":"Nips","first-page":"426","article-title":"A unified approach to interpreting model predictions Scott","author":"Zhang","year":"2012"},{"key":"10.1016\/j.jns.2022.120335_bb0380","author":"Lundberg"},{"key":"10.1016\/j.jns.2022.120335_bb0385","series-title":"Adv. Neural Inf. Process. Syst","first-page":"4766","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.jns.2022.120335_bb0390","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."}],"container-title":["Journal of the Neurological Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022510X22001976?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022510X22001976?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T15:59:48Z","timestamp":1758383988000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022510X22001976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":78,"alternative-id":["S0022510X22001976"],"URL":"https:\/\/doi.org\/10.1016\/j.jns.2022.120335","relation":{},"ISSN":["0022-510X"],"issn-type":[{"value":"0022-510X","type":"print"}],"subject":[],"published":{"date-parts":[[2022,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records","name":"articletitle","label":"Article Title"},{"value":"Journal of the Neurological Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jns.2022.120335","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"120335"}}