{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T11:19:32Z","timestamp":1768216772558,"version":"3.49.0"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"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. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Concentration difficulty is recognized as a hallmark of various neurologic and neuropsychiatric disorders. However, an accurate estimation of epidemiological risk factors for concentration difficulty remains severely limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>The study aimed to develop an interpretable machine-learning (ML) model to predict risk factors of concentration difficulty among adults in the United States.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 9,971 participants were included from the 2015\u20132016 cycle of the National Health and Nutrition Examination Survey (NHANES). Six ML algorithms, including Logistic Regression, ExtraTrees classifier, Bagging, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Random Forest (RF), were applied in this study. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, decision curve analysis (DCA), and calibration plots. Finally, a nomogram was constructed based on the best performing model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of these, 2,146 participants aged 20\u2009years and older were analyzed. Logistic regression exhibited the best clinical predictive value in both internal and external validation sets, with AUCs of 0.881 and 0.818, respectively. The DCA curve revealed that logistic regression exhibited the greatest net benefits in the internal cohort, whereas the RF model provided the largest net benefits in the external cohort (threshold: 0.2\u20130.3).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Logistic regression exhibited the highest clinical value in predicting concentration difficulty. These findings provide valuable insights for the recognition, management, and effective interference strategies for concentration difficulty.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1704576","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:18:41Z","timestamp":1768205921000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Constructing a risk screen for attention difficulty in U.S. adults using six machine learning methods"],"prefix":"10.3389","volume":"8","author":[{"given":"Ying","family":"Song","sequence":"first","affiliation":[]},{"given":"Yansun","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zedan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Li","family":"Yi","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1038\/oby.2007.634","article-title":"Association of childhood sexual abuse with obesity in a community sample of lesbians","volume":"15","author":"Aaron","year":"2007","journal-title":"Obesity (Silver Spring)"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/s41746-019-0193-y","article-title":"Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences","volume":"2","author":"Alber","year":"2019","journal-title":"NPJ Digit Med"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s40265-018-0887-5","article-title":"Contemporary drug treatment of hypertension: focus on recent guidelines","volume":"78","author":"Aronow","year":"2018","journal-title":"Drugs"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1002\/hep.22032","article-title":"Navigation skill impairment: another dimension of the driving difficulties in minimal hepatic encephalopathy","volume":"47","author":"Bajaj","year":"2008","journal-title":"Hepatology"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"e173","DOI":"10.1016\/S1470-2045(14)71116-7","article-title":"Nomograms in oncology: more than meets the eye","volume":"16","author":"Balachandran","year":"2015","journal-title":"Lancet Oncol."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.omtn.2020.08.022","article-title":"An interpretable prediction model for identifying N(7)-Methylguanosine sites based on XGBoost and SHAP","volume":"22","author":"Bi","year":"2020","journal-title":"Mol Ther Nucleic Acids"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e3001487","DOI":"10.1371\/journal.pbio.3001487","article-title":"Locus coeruleus neurons encode the subjective difficulty of triggering and executing actions","volume":"19","author":"Bornert","year":"2021","journal-title":"PLoS Biol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neurol.2019.01.394","article-title":"Attention disorders in adults with epilepsy. Determinants and therapeutic strategies","volume":"175","author":"Brissart","year":"2019","journal-title":"Rev. Neurol."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1007\/s10753-023-01827-0","article-title":"Performance of machine learning algorithms for predicting disease activity in inflammatory bowel disease","volume":"46","author":"Cai","year":"2023","journal-title":"Inflammation"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s41398-023-02536-w","article-title":"Machine learning in attention-deficit\/hyperactivity disorder: new approaches toward understanding the neural mechanisms","volume":"13","author":"Cao","year":"2023","journal-title":"Transl. Psychiatry"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1016\/j.biopsych.2016.02.018","article-title":"Medication for attention-deficit\/hyperactivity disorder and risk for depression: a Nationwide longitudinal cohort study","volume":"80","author":"Chang","year":"2016","journal-title":"Biol. Psychiatry"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e36477","DOI":"10.2196\/36477","article-title":"A machine learning approach to support urgent stroke triage using administrative data and social determinants of health at hospital presentation: retrospective study","volume":"25","author":"Chen","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1001\/jama.2023.0636","article-title":"Trends in dietary vitamin a intake among US adults by race and ethnicity, 2003-2018","volume":"329","author":"Cheng","year":"2023","journal-title":"JAMA"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/BF03324887","article-title":"Attention and aging","volume":"20","author":"Commodari","year":"2008","journal-title":"Aging Clin. Exp. Res."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1089\/jwh.2021.0420","article-title":"Preconception health and disability status among women of reproductive age participating in the National Health and nutrition examination surveys, 2013-2018","volume":"31","author":"Deierlein","year":"2022","journal-title":"J Womens Health (Larchmt)"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1146\/annurev-clinpsy-032816-045037","article-title":"Machine learning approaches for clinical psychology and psychiatry","volume":"14","author":"Dwyer","year":"2018","journal-title":"Annu. Rev. Clin. Psychol."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1034\/j.1600-0447.2003.00146.x","article-title":"Attention profile in schizophrenia compared with depression: differential effects of processing speed, selective attention and vigilance","volume":"108","author":"Egeland","year":"2003","journal-title":"Acta Psychiatr. Scand."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/s41568-021-00399-1","article-title":"Artificial intelligence in cancer research, diagnosis and therapy","volume":"21","author":"Elemento","year":"2021","journal-title":"Nat. Rev. Cancer"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.schres.2014.04.035","article-title":"Executive attention impairment in adolescents with schizophrenia who have used cannabis","volume":"157","author":"Epstein","year":"2014","journal-title":"Schizophr. Res."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/s00520-023-07710-w","article-title":"Anxiety, depression, and concentration in cancer survivors: National Health and nutrition examination survey results","volume":"31","author":"Fardell","year":"2023","journal-title":"Support Care Cancer"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1053\/apmr.2002.31179","article-title":"Vision, attention, and self-reported driving behaviors in community-dwelling stroke survivors","volume":"83","author":"Fisk","year":"2002","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"3973","DOI":"10.3390\/jcm11143973","article-title":"Prediction of intracranial infection in patients under external ventricular drainage and neurological intensive care: a multicenter retrospective cohort study","volume":"11","author":"Fu","year":"2022","journal-title":"J. Clin. Med."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neubiorev.2018.09.025","article-title":"Relating constructs of attention and working memory to social withdrawal in Alzheimer's disease and schizophrenia: issues regarding paradigm selection","volume":"97","author":"Gilmour","year":"2019","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1002\/ejp.1904","article-title":"The subsequent interruptive effects of pain on attention","volume":"26","author":"Gong","year":"2022","journal-title":"Eur. J. Pain"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"2413923","DOI":"10.1080\/07853890.2024.2413923","article-title":"Development and validation of a clinical-radiomics nomogram for the early prediction of Klebsiella pneumoniae liver abscess","volume":"56","author":"Gu","year":"2024","journal-title":"Ann. Med."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13321-024-00837-w","article-title":"Mind your prevalence!","volume":"16","author":"Guesn\u00e9","year":"2024","journal-title":"J Cheminform"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.janxdis.2017.10.007","article-title":"Difficulty concentrating in generalized anxiety disorder: an evaluation of incremental utility and relationship to worry","volume":"53","author":"Hallion","year":"2018","journal-title":"J. Anxiety Disord."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.jad.2024.12.095","article-title":"Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: evidence from resting-state EEG microstate analysis","volume":"373","author":"Hao","year":"2025","journal-title":"J. Affect. Disord."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1016\/j.jad.2021.09.005","article-title":"The effect of triglycerides in the associations between physical activity, sedentary behavior and depression: an interaction and mediation analysis","volume":"295","author":"Huang","year":"2021","journal-title":"J. Affect. Disord."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.canlet.2019.12.007","article-title":"Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges","volume":"471","author":"Huang","year":"2020","journal-title":"Cancer Lett."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s41398-019-0616-1","article-title":"Paying attention to attention in depression","volume":"9","author":"Keller","year":"2019","journal-title":"Transl. Psychiatry"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1017\/S0033291717000460","article-title":"Veterans with post-traumatic stress disorder exhibit altered emotional processing and attentional control during an emotional Stroop task","volume":"47","author":"Khanna","year":"2017","journal-title":"Psychol. Med."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1001\/jamasurg.2015.4516","article-title":"Nomograms to predict recurrence-free and overall survival after curative resection of adrenocortical carcinoma","volume":"151","author":"Kim","year":"2016","journal-title":"JAMA Surg."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1093\/jamia\/ocac007","article-title":"Closing the loop: automatically identifying abnormal imaging results in scanned documents","volume":"29","author":"Kumar","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"e38082","DOI":"10.2196\/38082","article-title":"Predicting mortality in intensive care unit patients with heart failure using an interpretable machine learning model: retrospective cohort study","volume":"24","author":"Li","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"1305836","DOI":"10.3389\/fonc.2024.1305836","article-title":"Multiparametric MRI-based radiomics approach with deep transfer learning for preoperative prediction of Ki-67 status in sinonasal squamous cell carcinoma","volume":"14","author":"Lin","year":"2024","journal-title":"Front. Oncol."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"864393","DOI":"10.3389\/fpsyt.2022.864393","article-title":"An end-to-end depression recognition method based on EEGNet","volume":"13","author":"Liu","year":"2022","journal-title":"Front. Psych."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1093\/schbul\/sbz045","article-title":"Is attentional filtering impaired in schizophrenia?","volume":"45","author":"Luck","year":"2019","journal-title":"Schizophr. Bull."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1186\/s12957-021-02445-6","article-title":"A nomogram model for predicting prognosis of obstructive colorectal cancer","volume":"19","author":"Lv","year":"2021","journal-title":"World J. Surg. Oncol."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1186\/s12911-024-02645-6","article-title":"Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study","volume":"24","author":"Mehrbakhsh","year":"2024","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1002\/da.20900","article-title":"Attentional impairment in anxiety: inefficiency in expanding the scope of attention","volume":"29","author":"Najmi","year":"2012","journal-title":"Depress. Anxiety"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.jad.2005.09.006","article-title":"Attention and executive functions in remitted major depression patients","volume":"89","author":"Paelecke-Habermann","year":"2005","journal-title":"J. Affect. Disord."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"ii23","DOI":"10.1093\/ndt\/gfab262","article-title":"Cognitive disorders in patients with chronic kidney disease: specificities of clinical assessment","volume":"37","author":"P\u00e9pin","year":"2021","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s13054-019-2351-7","article-title":"Emergency department triage prediction of clinical outcomes using machine learning models","volume":"23","author":"Raita","year":"2019","journal-title":"Crit. Care"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.psychres.2019.02.049","article-title":"Impairment in planning tasks of children and adolescents with anxiety disorders","volume":"274","author":"Rodrigues","year":"2019","journal-title":"Psychiatry Res."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"3202","DOI":"10.1093\/brain\/awz258","article-title":"Assessing and mapping language, attention and executive multidimensional deficits in stroke aphasia","volume":"142","author":"Schumacher","year":"2019","journal-title":"Brain"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"fcad149","DOI":"10.1093\/braincomms\/fcad149","article-title":"Altered directional functional connectivity underlies post-stroke cognitive recovery","volume":"5","author":"Soleimani","year":"2023","journal-title":"Brain Commun"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"1058779","DOI":"10.3389\/fimmu.2022.1058779","article-title":"Systemic immune-inflammation index is associated with hepatic steatosis: evidence from NHANES 2015-2018","volume":"13","author":"Song","year":"2022","journal-title":"Front. Immunol."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jad.2021.06.018","article-title":"Low BDNF levels in serum are associated with cognitive impairments in medication-na\u00efve patients with current depressive episode in BD II and MDD","volume":"293","author":"Teng","year":"2021","journal-title":"J. Affect. Disord."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-023-10472-w","article-title":"A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques","volume":"12","author":"Tiwari","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1002\/uog.21878","article-title":"Development and validation of a machine-learning model for prediction of shoulder dystocia","volume":"56","author":"Tsur","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref52","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.jpeds.2014.12.075","article-title":"Low and high birth weight and the risk of child attention problems","volume":"166","author":"van Mil","year":"2015","journal-title":"J. Pediatr."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1038\/s41581-020-0266-9","article-title":"Mechanisms of cognitive dysfunction in CKD","volume":"16","author":"Viggiano","year":"2020","journal-title":"Nat. Rev. Nephrol."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s11011-005-7919-z","article-title":"Attention, memory, and cognitive function in hepatic encephalopathy","volume":"20","author":"Weissenborn","year":"2005","journal-title":"Metab. Brain Dis."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"672","DOI":"10.3390\/bioengineering11070672","article-title":"An ensemble machine learning and data mining approach to enhance stroke prediction","volume":"11","author":"Wijaya","year":"2024","journal-title":"Bioengineering"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"2322031","DOI":"10.1080\/0886022X.2024.2322031","article-title":"External validation of the prediction model of intradialytic hypotension: a multicenter prospective cohort study","volume":"46","author":"Xiang","year":"2024","journal-title":"Ren. Fail."},{"key":"ref57","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1080\/14737175.2022.2155515","article-title":"The effect of transcranial magnetic stimulation on the recovery of attention and memory impairment following stroke: a systematic review and meta-analysis","volume":"22","author":"Xu","year":"2022","journal-title":"Expert. Rev. Neurother."},{"key":"ref58","doi-asserted-by":"publisher","first-page":"e0195938","DOI":"10.1371\/journal.pone.0195938","article-title":"Associations of obesity with tracheal intubation success on first attempt and adverse events in the emergency department: an analysis of the multicenter prospective observational study in Japan","volume":"13","author":"Yakushiji","year":"2018","journal-title":"PLoS One"},{"key":"ref59","doi-asserted-by":"publisher","first-page":"2337739","DOI":"10.1080\/07853890.2024.2337739","article-title":"Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study","volume":"56","author":"Zhang","year":"2024","journal-title":"Ann. Med."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1186\/s12967-023-04499-4","article-title":"A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications","volume":"21","author":"Zheng","year":"2023","journal-title":"J. Transl. Med."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"18301","DOI":"10.1038\/s41598-023-45438-z","article-title":"Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning","volume":"13","author":"Zhong","year":"2023","journal-title":"Sci. Rep."},{"key":"ref62","doi-asserted-by":"publisher","first-page":"1365580","DOI":"10.3389\/fnut.2024.1365580","article-title":"Linear association of compound dietary antioxidant index with hyperlipidemia: a cross-sectional study","volume":"11","author":"Zhou","year":"2024","journal-title":"Front. 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