{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T02:06:37Z","timestamp":1792461997260,"version":"4.5.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100013365","name":"West China Hospital, Sichuan University","doi-asserted-by":"publisher","award":["ZYJC18028"],"award-info":[{"award-number":["ZYJC18028"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2023NSFSC1696"],"award-info":[{"award-number":["2023NSFSC1696"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2021YFG0136"],"award-info":[{"award-number":["2021YFG0136"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and technology project of Chengdu Health Commission","award":["2022020"],"award-info":[{"award-number":["2022020"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Clin Pharm"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Older adults with dementia often face the risk of potentially inappropriate medication (PIM) use. The quality of PIM evaluation is hindered by researchers' unfamiliarity with evaluation criteria for inappropriate drug use. While traditional machine learning algorithms can enhance evaluation quality, they struggle with the multilabel nature of prescription data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Aim<\/jats:title>\n                <jats:p>This study aimed to combine six machine learning algorithms and three multilabel classification models to identify correlations in prescription information and develop an optimal model to identify PIMs in older adults with dementia.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Method<\/jats:title>\n                <jats:p>This study was conducted from January 1, 2020, to December 31, 2020. We used cluster sampling to obtain prescription data from patients 65 years\u00a0and older with dementia. We assessed PIMs using the 2019 Beers criteria, the most authoritative and widely recognized standard for PIM detection. Our modeling process used three problem transformation methods (binary relevance, label powerset, and classifier chain) and six classification algorithms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We identified 18,338 older dementia patients and 36 PIMs types. The classifier chain\u2009+\u2009categorical boosting (CatBoost) model demonstrated superior performance, with the highest accuracy (97.93%), precision (95.39%), recall (94.07%), F1 score (95.69%), and subset accuracy values (97.41%), along with the lowest Hamming loss value (0.0011) and an acceptable duration of the operation (371s).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This research introduces a pioneering CC\u2009+\u2009CatBoost warning model for PIMs in older dementia patients, utilizing machine-learning techniques. This model enables a quick and precise identification of PIMs, simplifying the manual evaluation process.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11096-024-01730-0","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T11:01:59Z","timestamp":1720522919000},"page":"937-946","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A model for identifying potentially inappropriate medication used in older people with dementia: a machine learning study"],"prefix":"10.1007","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9957-1089","authenticated-orcid":false,"given":"Qiaozhi","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengnan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Teng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gongchao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8727-9259","authenticated-orcid":false,"given":"Ting","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"issue":"4","key":"1730_CR1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.5582\/bst.2019.01213","volume":"13","author":"R Chen","year":"2019","unstructured":"Chen R, Xu P, Song P, et al. China has faster pace than Japan in population aging in next 25 years. Biosci Trends. 2019;13(4):287\u201391.","journal-title":"Biosci Trends"},{"key":"1730_CR2","unstructured":"National Bureau of Statistics. National data. National Bureau of Statistics. Available from: http:\/\/data.stats.gov.cn\/easyquery.htm?cn=C01. Accessed 20 Aug 2023."},{"key":"1730_CR3","doi-asserted-by":"crossref","unstructured":"Rajan KB, Weuve J, Barnes LL, et al. Population estimate of people with clinical Alzheimer\u2019s disease and mild cognitive impairment in the United States (2020\u20132060). Alzheimers Dement. 2021;17(12):1966\u201375.","DOI":"10.1002\/alz.12362"},{"issue":"12","key":"1730_CR4","doi-asserted-by":"crossref","first-page":"e661","DOI":"10.1016\/S2468-2667(20)30185-7","volume":"5","author":"L Jia","year":"2020","unstructured":"Jia L, Du Y, Chu L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661\u201371.","journal-title":"Lancet Public Health"},{"issue":"12","key":"1730_CR5","first-page":"V10160767","volume":"179","author":"H Pedersen","year":"2017","unstructured":"Pedersen H, Klinkby KS, Waldorff FB. Treatment of chronic diseases in patients with dementia. Ugeskr Laeger. 2017;179(12):V10160767.","journal-title":"Ugeskr Laeger"},{"issue":"5","key":"1730_CR6","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1007\/s11096-022-01447-y","volume":"44","author":"AEMJH Linkens","year":"2022","unstructured":"Linkens AEMJH, Janssen MJM, van Nie N, et al. Additional value of a triggerlist as selection criterion in identifying patients at high risk of medication-related hospital admission: a retrospective cohort study. Int J Clin Pharm. 2022;44(5):1205\u201310.","journal-title":"Int J Clin Pharm"},{"issue":"19","key":"1730_CR7","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1001\/jama.2012.36918","volume":"308","author":"LN Gitlin","year":"2012","unstructured":"Gitlin LN, Kales HC, Lyketsos CG. Nonpharmacologic management of behavioral symptoms in dementia. JAMA. 2012;308(19):2020\u20139.","journal-title":"JAMA"},{"issue":"5","key":"1730_CR8","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1111\/j.1532-5415.2009.02220.x","volume":"57","author":"HG Bloom","year":"2009","unstructured":"Bloom HG, Ahmed I, Alessi CA, et al. Evidence-based recommendations for the assessment and management of sleep disorders in older persons. J Am Geriatr Soc. 2009;57(5):761\u201389.","journal-title":"J Am Geriatr Soc"},{"issue":"6","key":"1730_CR9","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1590\/1806-9282.20220015","volume":"68","author":"RMAF Oliveira","year":"2022","unstructured":"Oliveira RMAF, Gorzoni ML, Rosa RF. Potentially inappropriate medication use in hospitalized elderly patients. Rev Assoc Med Bras. 2022;68(6):797\u2013801.","journal-title":"Rev Assoc Med Bras"},{"issue":"12","key":"1730_CR10","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s00228-015-1950-8","volume":"71","author":"K Hedna","year":"2015","unstructured":"Hedna K, Hakkarainen KM, Gyllensten H, et al. Potentially inappropriate prescribing and adverse drug reactions in the elderly: a population-based study. Eur J Clin Pharmacol. 2015;71(12):1525\u201333.","journal-title":"Eur J Clin Pharmacol"},{"key":"1730_CR11","doi-asserted-by":"crossref","unstructured":"Fick DM, Cooper JW, Wade WE, et al. Updating the Beers criteria for potentially inappro-priate medication use in older adults: results of a US consensuspanel of experts. Arch Intern Med. 2003;163:2716\u201324.","DOI":"10.1001\/archinte.163.22.2716"},{"issue":"4","key":"1730_CR12","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1111\/jgs.15767","volume":"67","author":"2019 American Geriatrics Society Beers Criteria\u00ae Update Expert Panel","year":"2019","unstructured":"2019 American Geriatrics Society Beers Criteria\u00ae Update Expert Panel. American geriatrics society 2019 updated AGS Beers criteria\u00ae for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2019;67(4):674\u201394.","journal-title":"J Am Geriatr Soc"},{"key":"1730_CR13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2014","unstructured":"Kourou K, Exarchos TP, Exarchos KP, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2014;13:8\u201317.","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"1730_CR14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1038\/s41746-021-00510-8","volume":"4","author":"M Radha","year":"2021","unstructured":"Radha M, Fonseca P, Moreau A, et al. A deep transfer learning approach for wearable sleep stage classification with photoplethysmography. NPJ Digit Med. 2021;4(1):135.","journal-title":"NPJ Digit Med"},{"issue":"2","key":"1730_CR15","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s40266-019-00728-y","volume":"37","author":"LGM Mulder-Wildemors","year":"2020","unstructured":"Mulder-Wildemors LGM, Heringa M, Floor-Schreudering A, et al. Reducing inappropriate drug use in older patients by use of clinical decision support in community pharmacy: a mixed-methods evaluation. Drugs Aging. 2020;37(2):115\u201323.","journal-title":"Drugs Aging"},{"issue":"3","key":"1730_CR16","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1111\/jep.13065","volume":"25","author":"V Prasert","year":"2019","unstructured":"Prasert V, Shono A, Chanjaruporn F, et al. Effect of a computerized decision support system on potentially inappropriate medication prescriptions for elderly patients in Thailand. J Eval Clin Pract. 2019;25(3):514\u201320.","journal-title":"J Eval Clin Pract"},{"key":"1730_CR17","first-page":"475","volume":"294","author":"E Frutos","year":"2022","unstructured":"Frutos E, Kakazu M, Tajerian M, et al. Clinical decision support system for PIM in elderly patients: implementation and initial evaluation in ambulatory care. Stud Health Technol Inform. 2022;294:475\u20139.","journal-title":"Stud Health Technol Inform"},{"issue":"6","key":"1730_CR18","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1007\/s11096-022-01468-7","volume":"44","author":"Q Hu","year":"2022","unstructured":"Hu Q, Wu B, Wu J, et al. Predicting adverse drug events in older inpatients: a machine learning study. Int J Clin Pharm. 2022;44(6):1304\u201311.","journal-title":"Int J Clin Pharm"},{"issue":"16","key":"1730_CR19","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1093\/bioinformatics\/bty179","volume":"34","author":"D Veltri","year":"2018","unstructured":"Veltri D, Kamath U, Shehu A. Deep learning improves antimicrobial peptide recognition. Bioinformatics. 2018;34(16):2740\u20137.","journal-title":"Bioinformatics"},{"issue":"1","key":"1730_CR20","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s13755-022-00192-w","volume":"10","author":"C Li","year":"2022","unstructured":"Li C, Sun L, Peng D, et al. A multi-label classification system for anomaly classification in electrocardiogram. Health Inf Sci Syst. 2022;10(1):19.","journal-title":"Health Inf Sci Syst"},{"issue":"9","key":"1730_CR21","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell MR, Luo J, Shen X, et al. Learning multi-label scene classification. Pattern Recogn. 2004;37(9):1757\u201371.","journal-title":"Pattern Recogn"},{"key":"1730_CR22","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neunet.2019.10.002","volume":"122","author":"G Wu","year":"2020","unstructured":"Wu G, Zheng R, Tian Y, et al. Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification. Neural Netw. 2020;122:24\u201339.","journal-title":"Neural Netw"},{"key":"1730_CR23","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13321-016-0121-y","volume":"8","author":"F Montanari","year":"2016","unstructured":"Montanari F, Zdrazil B, Digles D, et al. Selectivity profiling of BCRP versus P-gp inhibition: from automated collection of polypharmacology data to multi-label learning. J Cheminform. 2016;8:7.","journal-title":"J Cheminform"},{"issue":"10","key":"1730_CR24","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.3390\/e22101143","volume":"22","author":"Z Wang","year":"2020","unstructured":"Wang Z, Wang T, Wan B, et al. Partial classifier chains with feature selection by exploiting label correlation in multi-label classification. Entropy (Basel). 2020;22(10):1143.","journal-title":"Entropy (Basel)"},{"key":"1730_CR25","doi-asserted-by":"crossref","first-page":"929584","DOI":"10.3389\/fphar.2022.929584","volume":"13","author":"M Zhao","year":"2022","unstructured":"Zhao M, Chen Z, Tian F, et al. Potentially inappropriate medication among people with dementia in China: a nationwide cross-sectional study. Front Pharmacol. 2022;13:929584.","journal-title":"Front Pharmacol"},{"key":"1730_CR26","first-page":"1","volume":"114","author":"P El Kafrawy","year":"2015","unstructured":"El Kafrawy P, Mausad A, Esmail H. Experimental comparison of methods for multi-label classification in different application domains. Int J Comput Appl. 2015;114:1\u20139.","journal-title":"Int J Comput Appl"},{"key":"1730_CR27","doi-asserted-by":"crossref","first-page":"e896","DOI":"10.7717\/peerj-cs.896","volume":"8","author":"N Ashraf","year":"2022","unstructured":"Ashraf N, Khan L, Butt S, et al. Multi-label emotion classification of Urdu tweets. PeerJ Comput Sci. 2022;8:e896.","journal-title":"PeerJ Comput Sci"},{"issue":"1","key":"1730_CR28","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1002\/nur.20232","volume":"31","author":"DM Fick","year":"2008","unstructured":"Fick DM, Mion LC, Beers MH, et al. Health outcomes associated with potentially inappropriate medication use in older adults. Res Nurs Health. 2008;31(1):42\u201351.","journal-title":"Res Nurs Health"},{"key":"1730_CR29","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony D, O\u2019Sullivan D, Byrne S, et al. STOPP\/START criteria for potentially inappropriate prescribing in older people: version 2. Age Ageing. 2015;44(2):213\u20138.","DOI":"10.1093\/ageing\/afu145"},{"issue":"7","key":"1730_CR30","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1007\/s00228-015-1860-9","volume":"71","author":"A Renom-Guiteras","year":"2015","unstructured":"Renom-Guiteras A, Meyer G, Th\u00fcrmann PA. The EU(7)-PIM list: a list of potentially inappropriate medications for older people consented by experts from seven European countries. Eur J Clin Pharmacol. 2015;71(7):861\u201375.","journal-title":"Eur J Clin Pharmacol"},{"issue":"8","key":"1730_CR31","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1111\/j.1532-5415.2011.03497.x","volume":"59","author":"MS Dimitrow","year":"2011","unstructured":"Dimitrow MS, Airaksinen MS, Kivel\u00e4 SL, et al. Comparison of prescribing criteria to evaluate the appropriateness of drug treatment in individuals aged 65 and older: a systematic review. J Am Geriatr Soc. 2011;59(8):1521\u201330.","journal-title":"J Am Geriatr Soc"},{"issue":"9","key":"1730_CR32","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1177\/2042098618784809","volume":"9","author":"IA Scott","year":"2018","unstructured":"Scott IA, Pillans PI, Barras M, et al. Using EMR-enabled computerized decision support systems to reduce prescribing of potentially inappropriate medications: a narrative review. Ther Adv Drug Saf. 2018;9(9):559\u201373.","journal-title":"Ther Adv Drug Saf"},{"key":"1730_CR33","doi-asserted-by":"crossref","unstructured":"Terrell KM, Perkins AJ, Dexter PR, et al. Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patients: a randomized, controlled trial. J Am Geriatr Soc. 2009;57(8):1388\u201394.","DOI":"10.1111\/j.1532-5415.2009.02352.x"},{"key":"1730_CR34","doi-asserted-by":"crossref","unstructured":"Peterson JF, Kripalani S, Danciu I, et al. Electronic surveillance and pharmacist intervention for vulnerable older inpatients on high-risk medication regimens. J Am Geriatr Soc. 2014;62(11):2148\u201352.","DOI":"10.1111\/jgs.13057"},{"issue":"3","key":"1730_CR35","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.jacr.2018.01.028","volume":"15","author":"T Syeda-Mahmood","year":"2018","unstructured":"Syeda-Mahmood T. Role of big data and machine learning in diagnostic decision support in radiology. J Am Coll Radiol. 2018;15(3):569\u201376.","journal-title":"J Am Coll Radiol"},{"key":"1730_CR36","doi-asserted-by":"crossref","first-page":"893966","DOI":"10.3389\/fonc.2022.893966","volume":"12","author":"Z Yu","year":"2022","unstructured":"Yu Z, Ye X, Liu H, et al. Predicting Lapatinib dose regimen using machine learning and deep learning techniques based on a real-world study. Front Oncol. 2022;12:893966.","journal-title":"Front Oncol"},{"key":"1730_CR37","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189\u2013232.","journal-title":"Ann Stat"},{"issue":"16","key":"1730_CR38","doi-asserted-by":"crossref","first-page":"8530","DOI":"10.3390\/ijerph18168530","volume":"18","author":"GW Cha","year":"2021","unstructured":"Cha GW, Moon HJ, Kim YC. Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. Int J Environ Res Public Health. 2021;18(16):8530.","journal-title":"Int J Environ Res Public Health"},{"issue":"10","key":"1730_CR39","doi-asserted-by":"crossref","first-page":"608","DOI":"10.3390\/toxins15100608","volume":"15","author":"JM Ahn","year":"2023","unstructured":"Ahn JM, Kim J, Kim K. Ensemble machine learning of gradient boosting (XGBoost, LightGBM, CatBoost) and attention-based CNN-LSTM for harmful algal blooms forecasting. Toxins (Basel). 2023;15(10):608.","journal-title":"Toxins (Basel)"},{"issue":"8","key":"1730_CR40","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s11920-019-1049-5","volume":"21","author":"LJ Bessey","year":"2019","unstructured":"Bessey LJ, Walaszek A. Management of behavioral and psychological symptoms of dementia. Curr Psychiatry Rep. 2019;21(8):66.","journal-title":"Curr Psychiatry Rep"},{"issue":"9459","key":"1730_CR41","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/S0140-6736(05)70801-1","volume":"365","author":"ZX Zhang","year":"2005","unstructured":"Zhang ZX, Roman GC, Hong Z, et al. Parkinson\u2019s disease in China: prevalence in Beijing, Xian, and Shanghai. Lancet. 2005;365(9459):595\u20137.","journal-title":"Lancet"},{"issue":"3","key":"1730_CR42","doi-asserted-by":"crossref","first-page":"e190828","DOI":"10.1001\/jamanetworkopen.2019.0828","volume":"2","author":"I Yunusa","year":"2019","unstructured":"Yunusa I, Alsumali A, Garba AE, et al. Assessment of reported comparative effectiveness and safety of atypical antipsychotics in the treatment of behavioral and psychological symptoms of dementia: a network meta-analysis. JAMA Netw Open. 2019;2(3):e190828.","journal-title":"JAMA Netw Open"},{"issue":"3","key":"1730_CR43","doi-asserted-by":"crossref","first-page":"725","DOI":"10.3233\/JAD-161247","volume":"58","author":"V Orgeta","year":"2017","unstructured":"Orgeta V, Tabet N, Nilforooshan R, et al. Efficacy of antidepressants for depression in Alzheimer\u2019s disease: systematic review and meta-analysis. J Alzheimers Dis. 2017;58(3):725\u201333.","journal-title":"J Alzheimers Dis"},{"key":"1730_CR44","doi-asserted-by":"crossref","first-page":"k1315","DOI":"10.1136\/bmj.k1315","volume":"361","author":"K Richardson","year":"2018","unstructured":"Richardson K, Fox C, Maidment I, et al. Anticholinergic drugs and risk of dementia: case-control study. BMJ. 2018;361:k1315.","journal-title":"BMJ"},{"issue":"10","key":"1730_CR45","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1097\/JGP.0b013e31824afbc4","volume":"20","author":"K Delbaere","year":"2012","unstructured":"Delbaere K, Kochan NA, Close JC, et al. Mild cognitive impairment as a predictor of falls in community-dwelling older people. Am J Geriatr Psychiatry. 2012;20(10):845\u201353.","journal-title":"Am J Geriatr Psychiatry"},{"issue":"26","key":"1730_CR46","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1056\/NEJM198812293192604","volume":"319","author":"ME Tinetti","year":"1988","unstructured":"Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988;319(26):1701\u20137.","journal-title":"N Engl J Med"},{"issue":"8","key":"1730_CR47","first-page":"644","volume":"42","author":"D Inaguma","year":"2000","unstructured":"Inaguma D, Kitagawa W, Hayashi H, et al. Three cases of severe hyponatremia under taking selective serotonin reuptake inhibitor (SSRI). Nihon Jinzo Gakkai Shi. 2000;42(8):644\u20138.","journal-title":"Nihon Jinzo Gakkai Shi"},{"issue":"1","key":"1730_CR48","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1093\/ageing\/30.1.94","volume":"30","author":"WE Schouten","year":"2001","unstructured":"Schouten WE, Sepers JM. Hyponatraemia associated with the use of a selective serotonin-reuptake inhibitor in an older patient. Age Ageing. 2001;30(1):94.","journal-title":"Age Ageing"},{"issue":"4","key":"1730_CR49","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3928\/0279-3695-20030401-09","volume":"41","author":"DL Finfgeld","year":"2003","unstructured":"Finfgeld DL. SSRI-related hyponatremia among aging adults. J Psychosoc Nurs Ment Health Serv. 2003;41(4):12\u20136.","journal-title":"J Psychosoc Nurs Ment Health Serv"},{"issue":"12","key":"1730_CR50","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.4088\/JCP.v65n1219c","volume":"65","author":"E Nahshoni","year":"2004","unstructured":"Nahshoni E, Weizman A, Shefet D, et al. A case of hyponatremia associated with escitalopram. J Clin Psychiatry. 2004;65(12):1722.","journal-title":"J Clin Psychiatry"},{"issue":"9","key":"1730_CR51","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1345\/aph.1G293","volume":"40","author":"S Jacob","year":"2006","unstructured":"Jacob S, Spinler SA. Hyponatremia associated with selective serotonin-reuptake inhibitors in older adults. Ann Pharmacother. 2006;40(9):1618\u201322.","journal-title":"Ann Pharmacother"},{"key":"1730_CR52","doi-asserted-by":"crossref","first-page":"i90","DOI":"10.1136\/bmj.i90","volume":"352","author":"SL Gray","year":"2016","unstructured":"Gray SL, Dublin S, Yu O, et al. Benzodiazepine use and risk of incident dementia or cognitive decline: prospective population based study. BMJ. 2016;352:i90.","journal-title":"BMJ"},{"issue":"2","key":"1730_CR53","doi-asserted-by":"crossref","first-page":"801","DOI":"10.3233\/JAD-151006","volume":"54","author":"W Gomm","year":"2016","unstructured":"Gomm W, von Holt K, Thom\u00e9 F, et al. Regular benzodiazepine and Z-substance use and risk of dementia: an analysis of German claims data. J Alzheimers Dis. 2016;54(2):801\u20138.","journal-title":"J Alzheimers Dis"},{"issue":"5","key":"1730_CR54","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.jalz.2015.10.006","volume":"12","author":"D Shash","year":"2016","unstructured":"Shash D, Kurth T, Bertrand M, et al. Benzodiazepine, psychotropic medication, and dementia: a population-based cohort study. Alzheimers Dement. 2016;12(5):604\u201313.","journal-title":"Alzheimers Dement"},{"issue":"10","key":"1730_CR55","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s40264-015-0319-3","volume":"38","author":"P Imfeld","year":"2015","unstructured":"Imfeld P, Bodmer M, Jick SS, et al. Benzodiazepine use and risk of developing Alzheimer\u2019s disease or vascular dementia: a case-control analysis. Drug Saf. 2015;38(10):909\u201319.","journal-title":"Drug Saf"},{"issue":"12","key":"1730_CR56","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1007\/s40520-020-01496-1","volume":"32","author":"A Pourmand","year":"2020","unstructured":"Pourmand A, Lombardi KM, Roberson J, et al. Patterns of benzodiazepine administration and prescribing to older adults in U.S. emergency departments. Aging Clin Exp Res. 2020;32(12):2621\u20138.","journal-title":"Aging Clin Exp Res"},{"issue":"9","key":"1730_CR57","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1002\/gps.2623","volume":"26","author":"SG B\u00e9land","year":"2011","unstructured":"B\u00e9land SG, Pr\u00e9ville M, Dubois MF, et al. The association between length of benzodiazepine use and sleep quality in older population. Int J Geriatr Psychiatry. 2011;26(9):908\u201315.","journal-title":"Int J Geriatr Psychiatry"}],"container-title":["International Journal of Clinical Pharmacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11096-024-01730-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11096-024-01730-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11096-024-01730-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T13:28:54Z","timestamp":1722259734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11096-024-01730-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,9]]},"references-count":57,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1730"],"URL":"https:\/\/doi.org\/10.1007\/s11096-024-01730-0","relation":{},"ISSN":["2210-7703","2210-7711"],"issn-type":[{"value":"2210-7703","type":"print"},{"value":"2210-7711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,9]]},"assertion":[{"value":"21 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no conflicts of interest to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}