{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:41:04Z","timestamp":1776732064040,"version":"3.51.2"},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:00:00Z","timestamp":1776729600000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Geriatric syndromes (GS) are complex conditions that affect older adults and often require multidisciplinary assessment. Natural language processing (NLP) has emerged as a promising tool for extracting relevant clinical information from unstructured text in electronic health records (EHRs). However, the application of NLP in detecting and monitoring GS remains an evolving area of research. This systematic review explores the role of NLP in the identification and analysis of GS, examining its applications, methodologies, and effectiveness. Furthermore, this review discusses the existing challenges, limitations, and future directions to advance NLP applications in the GS research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a systematic literature search across ten databases to identify studies that applied NLP to GS detection. Articles were screened using predefined inclusion and exclusion criteria, and relevant studies were evaluated for quality using PROBAST. Data were extracted on study characteristics, datasets, annotation processes, NLP approaches, performance metrics, population demographics, and clinical applications. A PRISMA flow diagram was used to illustrate the study selection process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 65 studies were included, where the majority of the studies used traditional rule-based and machine learning approaches. Publicly available datasets were scarce, and most studies used their private dataset, leading to significant variability in data sources and formats. Annotation methodologies differed across studies, with minimal shared guidelines or standards, making direct comparisons challenging. Performance metrics varied across syndromes, with F1-score, precision, and recall as the most commonly reported. Key challenges included the lack of dataset uniformity, differences in annotation practices, and the absence of external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>NLP has shown potential in GS analysis, particularly for the detection of syndromes and epidemiological research. However, the majority of studies only focused on one syndrome, and variability in dataset availability, annotation processes, and model performance present challenges to broader implementation. Future research should focus on improving the comprehensiveness of GS identification, dataset standardisation, enhancing model generalisability, and integrating NLP approaches into clinical workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03417-0","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T16:15:04Z","timestamp":1773332104000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges"],"prefix":"10.1186","volume":"26","author":[{"given":"Fahrurrozi","family":"Rahman","sequence":"first","affiliation":[]},{"given":"Imane","family":"Guellil","sequence":"additional","affiliation":[]},{"given":"Abul","family":"Hasan","sequence":"additional","affiliation":[]},{"given":"Huayu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Mat\u00fa\u0161","family":"Falis","sequence":"additional","affiliation":[]},{"given":"Arlene","family":"Casey","sequence":"additional","affiliation":[]},{"given":"Honghan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Bruce","family":"Guthrie","sequence":"additional","affiliation":[]},{"given":"Beatrice","family":"Alex","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"issue":"5","key":"3417_CR1","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1111\/j.1532-5415.2007.01156.x","volume":"55","author":"SK Inouye","year":"2007","unstructured":"Inouye SK, Studenski S, Tinetti ME, Kuchel GA. Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. J Am Geriatr Soc. 2007;55(5):780\u201391. https:\/\/agsjournals.onlinelibrary.wiley.com\/doi\/abs\/10.1111\/j.1532-5415.2007.01156.x.","journal-title":"J Am Geriatr Soc"},{"issue":"23","key":"3417_CR2","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1001\/jama.2012.5265","volume":"307","author":"ME Tinetti","year":"2012","unstructured":"Tinetti ME, Fried TR, Boyd CM. Designing health care for the most common chronic condition\u2014multimorbidity. JAMA. 2012;307(23):2493\u201394. https:\/\/jamanetwork.com\/journals\/jama\/articlepdf\/1187936\/jvp120020_2493_2494.pdf.","journal-title":"JAMA"},{"issue":"6","key":"3417_CR3","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.jamda.2013.03.022","volume":"14","author":"JE Morley","year":"2013","unstructured":"Morley JE, Vellas B, Abellan van Kan G, Anker SD, Bauer JM, Bernabei R, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14(6):392\u201397. https:\/\/doi.org\/10.1016\/j.jamda.2013.03.022.","journal-title":"J Am Med Dir Assoc"},{"key":"3417_CR4","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1186\/s12911-024-02420-7","volume":"22","author":"Y Deng","year":"2024","unstructured":"Deng Y, Pacheco JA, Ghosh A, Chung A, Mao C, Smith JC, et al. Natural language processing to identify lupus nephritis phenotype in electronic health records. BMC Med Inf Decis. 2024;22:348. https:\/\/doi.org\/10.1186\/s12911-024-02420-7.","journal-title":"Bmc Med Inf Decis"},{"key":"3417_CR5","doi-asserted-by":"publisher","first-page":"46267","DOI":"10.2196\/46267","volume":"11","author":"F Chang","year":"2023","unstructured":"Chang F, Krishnan J, Hurst JH, Yarrington ME, Anderson DJ, O\u2019Brien EC, et al. Comparing natural language processing and structured medical data to develop a computable phenotype for patients hospitalized due to covid-19: retrospective analysis. JMIR Med Inf. 2023;11:46267. https:\/\/doi.org\/10.2196\/46267.","journal-title":"JMIR Med Inf"},{"key":"3417_CR6","doi-asserted-by":"publisher","unstructured":"Pan J, Zhang Z, Peters SR, Vatanpour S, Walker RL, Lee S, et al. Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing. Brain Inf. 2023;10(1):22. https:\/\/doi.org\/10.1186\/s40708-023-00203-w.","DOI":"10.1186\/s40708-023-00203-w"},{"issue":"1","key":"3417_CR7","first-page":"34","volume":"10","author":"I Ugboma","year":"2008","unstructured":"Ugboma I, Syddall HE, Cox V, Cooper C, Briggs R, Sayer AA. Coding geriatric syndromes: how good are we? CME J Geriatric Med. 2008;10(1):34.","journal-title":"CME J Geriatric Med"},{"issue":"2","key":"3417_CR8","doi-asserted-by":"publisher","first-page":"83","DOI":"10.12788\/jhm.2685","volume":"12","author":"R Romero-Ortuno","year":"2017","unstructured":"Romero-Ortuno R, Forsyth DR, Wilson KJ, Cameron E, Wallis S, Biram R, et al. The association of geriatric syndromes with hospital outcomes. J Educ Chang Hosp Med. 2017;12(2):83\u201389. https:\/\/shmpublications.onlinelibrary.wiley.com\/doi\/abs\/10.12788\/jhm.2685.","journal-title":"J Educ Chang Hosp Med"},{"issue":"3\u20134","key":"3417_CR9","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1111\/jocn.12624","volume":"24","author":"HA Stallinga","year":"2015","unstructured":"Stallinga HA, ten Napel H, Jansen GJ, Geertzen JH, de Vries Robb\u00e9 PF, Roodbol PF. Does language ambiguity in clinical practice justify the introduction of standard terminology? An integrative review. J Clin Nurs. 2015;24(3\u20134):344\u201352. https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/jocn.12624.","journal-title":"J Clin Nurs"},{"issue":"3","key":"3417_CR10","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1093\/jamia\/ocaa269","volume":"28","author":"D Newman-Griffis","year":"2020","unstructured":"Newman-Griffis D, Divita G, Desmet B, Zirikly A, Ros\u00e9 CP, Fosler-Lussier E. Ambiguity in medical concept normalization: an analysis of types and coverage in electronic health record datasets. J Am Med Inf Assoc. 2020;28(3):516\u201332. https:\/\/arxiv.org\/abs\/https:\/\/academic.oup.com\/jamia\/article-pdf\/28\/3\/516\/36428822\/ocaa269.pdf.","journal-title":"J Am Med Inf Assoc"},{"issue":"12","key":"3417_CR11","doi-asserted-by":"publisher","first-page":"941","DOI":"10.7326\/0003-4819-113-12-941","volume":"113","author":"SK Inouye","year":"1990","unstructured":"Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. Ann Intern Med. 1990;113(12):941\u201348. https:\/\/doi.org\/10.7326\/0003-4819-113-12-941. PMID: 2240918.","journal-title":"Ann Intern Med"},{"issue":"2","key":"3417_CR12","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1176\/jnp.13.2.229","volume":"13","author":"PT Trzepacz","year":"2001","unstructured":"Trzepacz PT, Mittal D, Torres R, Kanary K, Norton J, Jimerson N. Validation of the delirium rating scale-revised-98: comparison with the delirium rating scale and the cognitive test for delirium. J Neuropsychiatry Clin Neurosci. 2001;13(2):229\u201342.","journal-title":"J Neuropsychiatry Clin Neurosci"},{"issue":"5","key":"3417_CR13","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1111\/j.1532-5415.2012.03942.x","volume":"60","author":"RL Kane","year":"2012","unstructured":"Kane RL, Shamliyan T, Talley K, Pacala J. The association between geriatric syndromes and survival. J Am Geriatr Soc. 2012;60(5):896\u2013904. https:\/\/agsjournals.onlinelibrary.wiley.com\/doi\/abs\/10.1111\/j.1532-5415.2012.03942.x.","journal-title":"J Am Geriatr Soc"},{"issue":"4","key":"3417_CR14","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1093\/jamia\/ocy173","volume":"26","author":"TA Koleck","year":"2019","unstructured":"Koleck TA, Dreisbach C, Bourne PE, Bakken S. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J Am Med Inf Assoc. 2019;26(4):364\u201379. https:\/\/arxiv.org\/abs\/https:\/\/academic.oup.com\/jamia\/article-pdf\/26\/4\/364\/34151341\/ocy173.pdf.","journal-title":"J Am Med Inf Assoc"},{"key":"3417_CR15","doi-asserted-by":"crossref","unstructured":"Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inf. 2019;7(2):e12239. http:\/\/medinform.jmir.org\/2019\/2\/e12239.","DOI":"10.2196\/12239"},{"key":"3417_CR16","doi-asserted-by":"crossref","unstructured":"Spasic I, Nenadic G. Clinical text data in machine learning: systematic review. JMIR Med Inf. 2020;8(3):e17984. http:\/\/medinform.jmir.org\/2020\/3\/17984.","DOI":"10.2196\/17984"},{"key":"3417_CR17","doi-asserted-by":"crossref","unstructured":"Garg R, Gupta A. A systematic review of NLP applications in clinical healthcare:Advancement and challenges. In: Advances in data-driven computing and intelligent systems, lecture notes in networks and systems. Singapore: Springer; 2024. p. 31\u201344.","DOI":"10.1007\/978-981-99-9521-9_3"},{"issue":"2","key":"3417_CR18","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1148\/radiol.16142770","volume":"279","author":"E Pons","year":"2016","unstructured":"Pons E, Braun LMM, Hunink MGM, Kors JA. Natural language processing in radiology: a systematic review. Radiology. 2016; 279(2):329\u201343. https:\/\/doi.org\/10.1148\/radiol.16142770.","journal-title":"Radiology"},{"issue":"1","key":"3417_CR19","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1186\/s12911-021-01533-7","volume":"21","author":"A Casey","year":"2021","unstructured":"Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMS Med Inf Decis. 2021;21(1):179. https:\/\/doi.org\/10.1186\/s12911-021-01533-7.","journal-title":"Bmc Med Inf Decis"},{"issue":"1","key":"3417_CR20","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1186\/s12859-023-05480-0","volume":"24","author":"M Gholipour","year":"2023","unstructured":"Gholipour M, Khajouei R, Amiri P, Hajesmaeel Gohari S, Ahmadian L. Extracting cancer concepts from clinical notes using natural language processing: a systematic review. BMC Bioinf. 2023;24(1):405.","journal-title":"BMC Bioinf"},{"issue":"12","key":"3417_CR21","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1136\/heartjnl-2021-319769","volume":"108","author":"M Reading Turchioe","year":"2022","unstructured":"Reading Turchioe M, Volodarskiy A, Pathak J, Wright DN, Tcheng JE, Slotwiner D. Systematic review of current natural language processing methods and applications in cardiology. Heart. 2022;108(12):909\u201316. https:\/\/heart.bmj.com\/content\/108\/12\/909.full.pdf.","journal-title":"Heart"},{"key":"3417_CR22","doi-asserted-by":"publisher","first-page":"104282","DOI":"10.1016\/j.jbi.2023.104282","volume":"138","author":"F Pethani","year":"2023","unstructured":"Pethani F, Dunn AG. Natural language processing for clinical notes in dentistry: a systematic review. J Retailing Biomed Inf. 2023;138:104282. https:\/\/doi.org\/10.1016\/j.jbi.2023.104282.","journal-title":"J Retailing Biomed Inf"},{"key":"3417_CR23","doi-asserted-by":"crossref","unstructured":"Osman M, Cooper R, Sayer AA, Witham MD. The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review. Age Ageing. 2024;53(7):afae135. https:\/\/academic.oup.com\/ageing\/article-pdf\/53\/7\/afae135\/58462839\/afae135.pdf.","DOI":"10.1093\/ageing\/afae135"},{"issue":"2","key":"3417_CR24","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1080\/02763869.2019.1588072","volume":"38","author":"JH Schiavo","year":"2019","unstructured":"Schiavo JH, Prospero. An international register of systematic review protocols. Med Reference Serv Q. 2019; 38(2):171\u201380. https:\/\/doi.org\/10.1080\/02763869.2019.1588072.","journal-title":"Med Reference Serv Q"},{"issue":"1","key":"3417_CR25","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST Group$$\\dagger$$, probast: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51\u201358. https:\/\/doi.org\/10.7326\/M18-1376.","journal-title":"Ann Intern Med"},{"key":"3417_CR26","unstructured":"Rahman F, Guellil I, Zhang H, Hasan A, Wu H, Guthrie B, et al. Natural language processing for detecting geriatric syndromes: a systematic review protocol. PROSPERO ID: CRD42024592024. 2024. https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD42024592024."},{"key":"3417_CR27","doi-asserted-by":"publisher","unstructured":"dos Santos HD, Silva AP, Maciel MCO, Burin HMV, Urbanetto JS, Vieira R. Fall detection in EHR using word embeddings and deep learning. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). 2019. p. 265\u201368. https:\/\/doi.org\/10.1109\/BIBE.2019.00054.","DOI":"10.1109\/BIBE.2019.00054"},{"key":"3417_CR28","doi-asserted-by":"publisher","unstructured":"Panahi S, Mayo J, Kennedy E, Christensen L, Kamineni S, Sagiraju HKR, et al. Identifying clinical phenotypes of frontotemporal dementia in post-9\/11 era veterans using natural language processing. Front Neurol. 2024;15. https:\/\/doi.org\/10.3389\/fneur.2024.1270688.","DOI":"10.3389\/fneur.2024.1270688"},{"key":"3417_CR29","doi-asserted-by":"publisher","unstructured":"Lorenzoni G, Rampazzo R, Buratin A, Berchialla P, Gregori D. Does the integration of pre-coded information with narratives improve in-hospital falls\u2019 surveillance? Appl Sci. 2021;11(10). https:\/\/doi.org\/10.3390\/app11104406.","DOI":"10.3390\/app11104406"},{"issue":"1","key":"3417_CR30","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37\u201346. https:\/\/doi.org\/10.1177\/001316446002000104.","journal-title":"Educ Psychol Meas"},{"issue":"5","key":"3417_CR31","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"J Fleiss","year":"1971","unstructured":"Fleiss J, et al. Measuring nominal scale agreement among many raters. Psychol Bull. 1971;76(5):378\u201382.","journal-title":"Psychol Bull"},{"issue":"3","key":"3417_CR32","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1197\/jamia.M1733","volume":"12","author":"G Hripcsak","year":"2005","unstructured":"Hripcsak G, Rothschild AS. Agreement, the f-measure, and reliability in information retrieval. J Am Med Inf Assoc. 2005;12(3):296\u201398. https:\/\/academic.oup.com\/jamia\/article-pdf\/12\/3\/296\/2429751\/12-3-296.pdf.","journal-title":"J Am Med Inf Assoc"},{"key":"3417_CR33","doi-asserted-by":"publisher","unstructured":"Du X, Novoa-Laurentiev J, Plasek JM, Chuang YW, Wang L, Marshall GA, et al. Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes. eBiomedicine. 2024;109. https:\/\/doi.org\/10.1016\/j.ebiom.2024.105401.","DOI":"10.1016\/j.ebiom.2024.105401"},{"key":"3417_CR34","doi-asserted-by":"publisher","unstructured":"Cheligeer C, Wu G, Lee S, Pan J, Southern DA, Martin EA, et al. Bert-based neural network for inpatient fall detection from electronic medical records: retrospective cohort study. JMIR Med Inf. 2024;12. https:\/\/doi.org\/10.1016\/j.ebiom.2024.105401.","DOI":"10.1016\/j.ebiom.2024.105401"},{"key":"3417_CR35","doi-asserted-by":"publisher","unstructured":"Laurentiev J, Kim DH, Mahesri M, Wang KY, Bessette LG, York C, et al. Identifying functional status impairment in people living with dementia through natural language processing of clinical documents: cross-sectional study. J Med Internet Res. 2024;26:e47739. https:\/\/doi.org\/10.2196\/47739.","DOI":"10.2196\/47739"},{"issue":"10","key":"3417_CR36","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1093\/jamia\/ocae177","volume":"31","author":"S Sivarajkumar","year":"2024","unstructured":"Sivarajkumar S, Tam TYC, Mohammad HA, Viggiano S, Oniani D, Visweswaran S, et al. Extraction of sleep information from clinical notes of alzheimer\u2019s disease patients using natural language processing. J Am Med Inf Assoc. 2024;31(10):2217\u201327. https:\/\/arxiv.org\/abs\/https:\/\/academic.oup.com\/jamia\/article-pdf\/31\/10\/2217\/59206318\/ocae177.pdf.","journal-title":"J Am Med Inf Assoc"},{"issue":"1","key":"3417_CR37","doi-asserted-by":"publisher","first-page":"2397578","DOI":"10.1080\/08941939.2024.2397578","volume":"37","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Zhao G, Zhao Z, Luo J, Feng P, Tong Y, et al. Quantitative analysis of the causes of falls in adult hospitalized patients based on the perspective of text mining. J Invest Surg. 2024; 37(1):2397578. https:\/\/doi.org\/10.1080\/08941939.2024.2397578.","journal-title":"J Invest Surg"},{"issue":"6","key":"3417_CR38","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1111\/1475-6773.14210","volume":"58","author":"W Wu","year":"2023","unstructured":"Wu W, Holkeboer KJ, Kolawole TO, Carbone L, Mahmoudi E. Natural language processing to identify social determinants of health in alzheimer\u2019s disease and related dementia from electronic health records. Health Serv Res. 2023;58(6):1292\u2013302. https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1475-6773.14210.","journal-title":"Health Serv Res"},{"issue":"1","key":"3417_CR39","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1186\/s12877-022-03474-w","volume":"22","author":"L Chen","year":"2022","unstructured":"Chen L, Li N, Zheng Y, Gao L, Ge N, Xie D, et al. A novel semiautomatic Chinese keywords instrument screening delirium based on electronic medical records. BMC Geriatrics. 2022;22(1):779. https:\/\/doi.org\/10.1186\/s12877-022-03474-w.","journal-title":"BMC Geriatrics"},{"key":"3417_CR40","doi-asserted-by":"publisher","first-page":"233372142095986","DOI":"10.1177\/2333721420959861","volume":"6","author":"M Topaz","year":"2020","unstructured":"Topaz M, Adams V, Wilson P, Woo K, Ryvicker M. Free-text documentation of dementia symptoms in home healthcare: a natural language processing study. Gerontol Geriatric Med. 2020; 6:2333721420959861. https:\/\/doi.org\/10.1177\/2333721420959861.","journal-title":"Gerontol Geriatric Med"},{"key":"3417_CR41","doi-asserted-by":"publisher","unstructured":"Shao Y, Todd K, Shutes-David A, Millard SP, Brown K, Thomas A, et al. Identifying probable dementia in undiagnosed black and white americans using machine learning in veterans health administration electronic health records. Big Data Cognit Comput. 2023;7(4). https:\/\/doi.org\/10.3390\/bdcc7040167.","DOI":"10.3390\/bdcc7040167"},{"key":"3417_CR42","doi-asserted-by":"publisher","unstructured":"Noori A, Magdamo C, Liu X, Tyagi T, Li Z, Kondepudi A, et al. Development and evaluation of a natural language processing annotation tool to facilitate phenotyping of cognitive status in electronic health records: diagnostic study. J Med Internet Res. 2022;24(8):e40384. https:\/\/doi.org\/10.2196\/40384.","DOI":"10.2196\/40384"},{"issue":"3","key":"3417_CR43","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1093\/gerona\/glaa275","volume":"77","author":"S Fu","year":"2020","unstructured":"Fu S, Lopes GS, Pagali SR, Thorsteinsdottir B, LeBrasseur NK, Wen A, et al. Ascertainment of delirium status using natural language processing from electronic health records. J Gerontol Ser A. 2020;77(3):524\u201330. https:\/\/academic.oup.com\/biomedgerontology\/article-pdf\/77\/3\/524\/42692057\/glaa275.pdf.","journal-title":"J Gerontol Ser A"},{"key":"3417_CR44","doi-asserted-by":"publisher","first-page":"109144","DOI":"10.1016\/j.compbiomed.2024.109144","volume":"182","author":"S Li","year":"2024","unstructured":"Li S, Dexter P, Ben-Miled Z, Boustani M. Dementia risk prediction using decision-focused content selection from medical notes. Comput Biol Med. 2024;182:109144. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109144.","journal-title":"Comput Biol Med"},{"issue":"11","key":"3417_CR45","doi-asserted-by":"publisher","first-page":"6918","DOI":"10.1109\/JBHI.2024.3438885","volume":"28","author":"H Tsai","year":"2024","unstructured":"Tsai H, Yang TW, Ou KH, Su TH, Lin C, Chou CF. Multimodal attention network for dementia prediction. IEEE J Biomed Health Inf. 2024;28(11):6918\u201330. https:\/\/doi.org\/10.1109\/JBHI.2024.3438885.","journal-title":"IEEE J Biomed Health Inf"},{"key":"3417_CR46","doi-asserted-by":"crossref","unstructured":"Dormosh N, Schut MC, Heymans MW, Maarsingh O, Bouman J, van der Velde N, et al. Predicting future falls in older people using natural language processing of general practitioners\u2019 clinical notes. Age Ageing. 2023;52(4):afad046. https:\/\/academic.oup.com\/ageing\/articlepdf\/52\/4\/afad046\/49743214\/afad046.pdf.","DOI":"10.1093\/ageing\/afad046"},{"key":"3417_CR47","doi-asserted-by":"publisher","first-page":"105146","DOI":"10.1016\/j.ijmedinf.2023.105146","volume":"177","author":"M Zolnoori","year":"2023","unstructured":"Zolnoori M, Barr\u00f3n Y, Song J, Noble J, Burgdorf J, Ryvicker M, et al. Homeadscreen: developing alzheimer\u2019s disease and related dementia risk identification model in home healthcare. Int J Multiling Med Inf. 2023;177:105146. https:\/\/doi.org\/10.1016\/j.ijmedinf.2023.105146.","journal-title":"Int J Multiling Med Inf"},{"key":"3417_CR48","doi-asserted-by":"publisher","unstructured":"Mishra AK, Chappell MJ, Emerson S, Skubic M. Fall risk prediction in older adults using free-text nursing notes and medications in electronic health records. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2023. p. 1\u20134. https:\/\/doi.org\/10.1109\/EMBC40787.2023.10341127.","DOI":"10.1109\/EMBC40787.2023.10341127"},{"key":"3417_CR49","doi-asserted-by":"publisher","unstructured":"Kawazoe Y, Shimamoto K, Shibata D, Shinohara E, Kawaguchi H, Yamamoto T. Impact of a clinical text\u2013based fall prediction model on preventing extended hospital stays for elderly inpatients: model development and performance evaluation. JMIR Med Inf. 2022;10(7). https:\/\/doi.org\/10.2196\/37913.","DOI":"10.2196\/37913"},{"key":"3417_CR50","doi-asserted-by":"publisher","unstructured":"Hane CA, Nori VS, Crown WH, Sanghavi DM, Bleicher P. Predicting onset of dementia using clinical notes and machine learning: case-control study. JMIR Med Inf. 2020;8(6). https:\/\/doi.org\/10.2196\/17819.","DOI":"10.2196\/17819"},{"key":"3417_CR51","doi-asserted-by":"crossref","unstructured":"Knapp M, Chua KC, Broadbent M, Chang CK, Fernandez JL, Milea D, et al. Predictors of care home and hospital admissions and their costs for older people with alzheimer\u2019s disease: findings from a large london case register. BMJ Open. 2016;6(11). https:\/\/bmjopen.bmj.com\/content\/6\/11\/e013591.full.pdf.","DOI":"10.1136\/bmjopen-2016-013591"},{"key":"3417_CR52","doi-asserted-by":"publisher","first-page":"103103","DOI":"10.1016\/j.jbi.2019.103103","volume":"90","author":"M Topaz","year":"2019","unstructured":"Topaz M, Murga L, Gaddis KM, McDonald MV, Bar-Bachar O, Goldberg Y, et al. Mining fall-related information in clinical notes: comparison of rule-based and novel word embedding-based machine learning approaches. J Retailing Biomed Inf. 2019;90:103103. https:\/\/doi.org\/10.1016\/j.jbi.2019.103103.","journal-title":"J Retailing Biomed Inf"},{"key":"3417_CR53","doi-asserted-by":"crossref","unstructured":"Ge W, Coelho LMG, Donahue MA, Rice HJ, Blacker D, Hsu J, et al. Automated identification of fall-related injuries in unstructured clinical notes. Am J Epidemiol. 2024. https:\/\/academic.oup.com\/aje\/advance-articlepdf\/doi\/10.1093\/aje\/kwae240\/58660482\/kwae240.pdf.","DOI":"10.2139\/ssrn.4398944"},{"key":"3417_CR54","doi-asserted-by":"publisher","first-page":"700","DOI":"10.3233\/SHTI231055","volume":"310","author":"D Vithanage","year":"2024","unstructured":"Vithanage D, Zhu Y, Zhang Z, Deng C, Yin M, Yu P. Extracting symptoms of agitation in dementia from free-text nursing notes using advanced natural language processing. Stud Health Technol Inf. 2024;310:700\u201304. https:\/\/doi.org\/10.3233\/SHTI231055.","journal-title":"Stud Health Technol Inf"},{"key":"3417_CR55","doi-asserted-by":"publisher","first-page":"57926","DOI":"10.2196\/57926","volume":"7","author":"R Prakash","year":"2024","unstructured":"Prakash R, Dupre ME, \u00d8stbye T, Xu H. Extracting critical information from unstructured clinicians\u2019 notes data to identify dementia severity using a rule-based approach: feasibility study. JMIR Aging. 2024;7:57926. https:\/\/doi.org\/10.2196\/57926.","journal-title":"JMIR Aging"},{"key":"3417_CR56","first-page":"100140","volume":"9","author":"S Amjad","year":"2024","unstructured":"Amjad S, Holmes NE, Kishore K, Young M, Bailey J, Bellomo R, et al. Advancing delirium classification: a clinical notes-based natural language processing-supported machine learning model. Intel-Based Med. 2024;9:100140. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666521224000073.","journal-title":"Intel-Based Med"},{"issue":"1","key":"3417_CR57","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1186\/s12911-024-02587-z","volume":"24","author":"Y Guo","year":"2024","unstructured":"Guo Y, Huang C, Sheng Y, Zhang W, Ye X, Lian H, et al. Improve the efficiency and accuracy of ophthalmologists\u2019 clinical decision-making based on AI technology. BMC Med Inf Decis. 2024;24(1):192. https:\/\/doi.org\/10.1186\/s12911-024-02587-z.","journal-title":"Bmc Med Inf Decis"},{"key":"3417_CR58","doi-asserted-by":"publisher","unstructured":"Sauver JS, Fu S, Sohn S, Weston S, Fan C, Olson J, et al. Identification of delirium from real-world electronic health record clinical notes. J Clin Transl Sci. 2023;7(1). https:\/\/doi.org\/10.1017\/cts.2023.610.","DOI":"10.1017\/cts.2023.610"},{"key":"3417_CR59","doi-asserted-by":"publisher","first-page":"84795","DOI":"10.1109\/ACCESS.2023.3299489","volume":"11","author":"A Millet","year":"2023","unstructured":"Millet A, Madrid A, Alonso-Weber JM, Rodr\u00edguez-Ma\u00f1as L, P\u00e9rez-Rodr\u00e1-Guez R. Machine learning techniques applied to the development of a fall risk index for older adults. IEEE Access. 2023;11:84795\u2013809. https:\/\/doi.org\/10.1109\/ACCESS.2023.3299489.","journal-title":"IEEE Access"},{"key":"3417_CR60","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-031-34344-5_20","volume-title":"Artificial intelligence in medicine","author":"JM Powell","year":"2023","unstructured":"Powell JM, Guo Y, Sarker A, McKay JL. Classification of fall types in Parkinson\u2019s disease from self-report data using natural language processing. In: Juarez JM, Marcos M, Stiglic G, Tucker A, editors. Artificial intelligence in medicine. Nature Switzerland, Cham: Springer; 2023. p. 163\u201372. https:\/\/doi.org\/10.1007\/978-3-031-34344-5_20."},{"issue":"1","key":"3417_CR61","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/s41666-023-00125-6","volume":"7","author":"LC Maclagan","year":"2023","unstructured":"Maclagan LC, Abdalla M, Harris DA, Stukel TA, Chen B, Candido E, et al. Can patients with dementia be identified in primary care electronic medical records using natural language processing? J Healthc Inf Res. 2023;7(1):42\u201358. https:\/\/doi.org\/10.1007\/s41666-023-00125-6.","journal-title":"J Healthc Inf Res"},{"key":"3417_CR62","doi-asserted-by":"crossref","unstructured":"Liu M, Beare R, Collyer T, Andrew N, Srikanth V. Leveraging natural language processing and clinical notes for dementia detection. In: Naumann, T., Ben Abacha, A., Bethard, S., Roberts, K., Rumshisky, A, editors. Proceedings of the 5th Clinical Natural Language Processing Workshop. Toronto, Canada: Association for Computational Linguistics; 2023. p. 150\u201355. https:\/\/aclanthology.org\/2023.clinicalnlp-1.20\/.","DOI":"10.18653\/v1\/2023.clinicalnlp-1.20"},{"issue":"1","key":"3417_CR63","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1186\/s12911-022-01864-z","volume":"22","author":"RB Penfold","year":"2022","unstructured":"Penfold RB, Carrell DS, Cronkite DJ, Pabiniak C, Dodd T, Glass AM, et al. Development of a machine learning model to predict mild cognitive impairment using natural language processing in the absence of screening. BMC Med Inf Decis. 2022;22(1):129. https:\/\/doi.org\/10.1186\/s12911-022-01864-z.","journal-title":"Bmc Med Inf Decis"},{"issue":"4","key":"3417_CR64","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1080\/17538157.2021.2019038","volume":"47","author":"H Tohira","year":"2022","unstructured":"Tohira H, Finn J, Ball S, Brink D, Buzzacott P. Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances. Inf Health Soc Care. 2022; 47(4):403\u201313. https:\/\/doi.org\/10.1080\/17538157.2021.2019038.","journal-title":"Inf Health Soc Care"},{"key":"3417_CR65","doi-asserted-by":"publisher","first-page":"104736","DOI":"10.1016\/j.ijmedinf.2022.104736","volume":"162","author":"S Fu","year":"2022","unstructured":"Fu S, Thorsteinsdottir B, Zhang X, Lopes GS, Pagali SR, LeBrasseur NK, et al. A hybrid model to identify fall occurrence from electronic health records. Int J Multiling Med Inf. 2022;162:104736. https:\/\/doi.org\/10.1016\/j.ijmedinf.2022.104736.","journal-title":"Int J Multiling Med Inf"},{"key":"3417_CR66","doi-asserted-by":"publisher","unstructured":"Wang L, Zhang Y, Chignell M, Shan B, Sheehan KA, Razak F, et al. Boosting delirium identification accuracy with sentiment-based natural language processing: mixed methods study. JMIR Med Inf. 2022;10(12):e38161. https:\/\/doi.org\/10.2196\/38161.","DOI":"10.2196\/38161"},{"issue":"6","key":"3417_CR67","doi-asserted-by":"publisher","first-page":"33834","DOI":"10.2196\/33834","volume":"6","author":"W Ge","year":"2022","unstructured":"Ge W, Alabsi H, Jain A, Ye E, Sun H, Fernandes M, et al. Identifying patients with delirium based on unstructured clinical notes: observational study. JMIR Formative Res. 2022;6(6):33834. https:\/\/doi.org\/10.2196\/33834.","journal-title":"JMIR Formative Res"},{"key":"3417_CR68","doi-asserted-by":"publisher","unstructured":"Nakatani H, Nakao M, Uchiyama H, Toyoshiba H, Ochiai C. Predicting inpatient falls using natural language processing of nursing records obtained from Japanese electronic medical records: case-control study. JMIR Med Inf. 2020;8(4). https:\/\/doi.org\/10.2196\/16970.","DOI":"10.2196\/16970"},{"issue":"8","key":"3417_CR69","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1111\/jgs.15411","volume":"66","author":"H Kharrazi","year":"2018","unstructured":"Kharrazi H, Anzaldi LJ, Hernandez L, Davison A, Boyd CM, Leff B, et al. The value of unstructured electronic health record data in geriatric syndrome case identification. J Am Geriatr Soc. 2018;66(8):1499\u2013507. https:\/\/agsjournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jgs.15411.","journal-title":"J Am Geriatr Soc"},{"key":"3417_CR70","doi-asserted-by":"publisher","unstructured":"Chen T, Dredze M, Weiner JP, Hernandez L, Kimura J, Kharrazi H. Extraction of geriatric syndromes from electronic health record clinical notes: assessment of statistical natural language processing methods. JMIR Med Inf. 2019;7(1):e13039. https:\/\/doi.org\/10.2196\/13039.","DOI":"10.2196\/13039"},{"issue":"8\u20139","key":"3417_CR71","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1093\/jamia\/ocz093","volume":"26","author":"T Chen","year":"2019","unstructured":"Chen T, Dredze M, Weiner JP, Kharrazi H. Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records. J Am Med Inf Assoc. 2019;26(8\u20139):787\u201395. https:\/\/academic.oup.com\/jamia\/article-pdf\/26\/8-9\/787\/34151692\/ocz093.pdf.","journal-title":"J Am Med Inf Assoc"},{"key":"3417_CR72","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.cmpb.2018.08.016","volume":"165","author":"LB Moreira","year":"2018","unstructured":"Moreira LB, Namen AA. A hybrid data mining model for diagnosis of patients with clinical suspicion of dementia. Comput Methods Programs Biomed. 2018;165:139\u201349. https:\/\/doi.org\/10.1016\/j.cmpb.2018.08.016.","journal-title":"Comput Methods Programs Biomed"},{"key":"3417_CR73","doi-asserted-by":"publisher","unstructured":"Patterson BW, Jacobsohn GC, Shah MN, Song Y, Maru A, Venkatesh AK, et al. Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department. BMC Med Inf Decis. 2019;19(1):138. https:\/\/doi.org\/10.1186\/s12911-019-0843-7.","DOI":"10.1186\/s12911-019-0843-7"},{"key":"3417_CR74","unstructured":"Zeng-Treitler Q, Shao Y, Cheng Y, Doing-Harris K, Shah RU, Weir CR, et al. Extracting frailty status for post surgical mortality prediction. In: IADIS International Conference E-Health 2018. 2018. https:\/\/www.iadisportal.org\/digital-library\/extracting-frailty-status-for-postsurgical-mortality-prediction."},{"issue":"1","key":"3417_CR75","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1186\/1472-6963-12-448","volume":"12","author":"SI Toyabe","year":"2012","unstructured":"Toyabe SI. Detecting inpatient falls by using natural language processing of electronic medical records. BMC Health Serv Res. 2012;12(1):448. https:\/\/doi.org\/10.1186\/1472-6963-12-448.","journal-title":"BMC Health Serv Res"},{"key":"3417_CR76","unstructured":"Tremblay MC, Berndt DJ, Foulis P, Luther SL. Utilizing text mining techniques to identify fall related injuries. Americas Conf Inf Syst. 2005. https:\/\/aisel.aisnet.org\/amcis2005\/109."},{"key":"3417_CR77","doi-asserted-by":"publisher","unstructured":"Tremblay MC, Berndt DJ, Luther SL, Foulis PR, DD. French, identifying fall-related injuries: text mining the electronic medical record. Inf Technol Manag. 2009;10(4):253\u201365. https:\/\/doi.org\/10.1007\/s10799-009-0061-6.","DOI":"10.1007\/s10799-009-0061-6"},{"issue":"1","key":"3417_CR78","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1097\/jmq.0000000000000090","volume":"38","author":"SR Pagali","year":"2023","unstructured":"Pagali SR, Kumar R, Fu S, Sohn S, Yousufuddin M. Natural language processing CAM algorithm improves delirium detection compared with conventional methods. Am J Med Qual. 2023;38(1):17\u201322. https:\/\/doi.org\/10.1097\/jmq.0000000000000090.","journal-title":"Am J Med Qual"},{"issue":"1","key":"3417_CR79","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1093\/jamia\/ocab248","volume":"29","author":"JA Martin","year":"2021","unstructured":"Martin JA, Crane-Droesch A, Lapite FC, Puhl JC, Kmiec TE, Silvestri JA, et al. Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians\u2019 encounter notes. J Am Med Inf Assoc. 2021;29(1):109\u201319. https:\/\/academic.oup.com\/jamia\/article-pdf\/29\/1\/109\/41955633\/ocab248.pdf.","journal-title":"J Am Med Inf Assoc"},{"issue":"12","key":"3417_CR80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/s0033291724001922","volume":"54","author":"LL Gibson","year":"2024","unstructured":"Gibson LL, Mueller C, Stewart R, Aarsland D. Characteristics associated with progression to probable dementia with lewy bodies in a cohort with very late-onset psychosis. Psychol Med. 2024;54(12):1\u201310. https:\/\/doi.org\/10.1017\/s0033291724001922.","journal-title":"Psychol Med"},{"key":"3417_CR81","doi-asserted-by":"crossref","unstructured":"Dormosh N, Abu-Hanna A, Calixto I, Schut MC, Heymans MW, van der Velde N. Topic evolution before fall incidents in new fallers through natural language processing of general practitioners\u2019 clinical notes. Age Ageing. 2024;53(2):afae016. https:\/\/academic.oup.com\/ageing\/article-pdf\/53\/2\/afae016\/56669437\/afae016.pdf.","DOI":"10.1093\/ageing\/afae016"},{"key":"3417_CR82","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1109\/ICHI61247.2024.00046","volume-title":"Identifying symptoms of delirium from clinical narratives using natural language processing, in 2024 IEEE 12th International conference on healthcare informatics (ICHI)","author":"A Chen","year":"2024","unstructured":"Chen A, Paredes D, Yu Z, Lou X, Brunson R, Thomas JN, et al. Identifying symptoms of delirium from clinical narratives using natural language processing. In: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). 2024. p. 305\u201311. https:\/\/doi.org\/10.1109\/ICHI61247.2024.00046."},{"issue":"10","key":"3417_CR83","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1177\/07334648241242321","volume":"43","author":"M Ryvicker","year":"2024","unstructured":"Ryvicker M, Barr\u00f3n Y, Song J, Zolnoori M, Shah S, Burgdorf JG, et al. Using natural language processing to identify home health care patients at risk for diagnosis of alzheimer\u2019s disease and related dementias. J Appl Gerontol. 2024; 43(10):1461\u201372. https:\/\/doi.org\/10.1177\/07334648241242321.","journal-title":"J Appl Gerontol"},{"issue":"1","key":"3417_CR84","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1111\/jnu.13038","volume":"57","author":"D Scharp","year":"2025","unstructured":"Scharp D, Song J, Hobensack M, Palmer MH, Barcelona V, Topaz M. Applying natural language processing to understand symptoms among older adult home healthcare patients with urinary incontinence. J Nurs Scholarsh. 2025;57(1):152\u201364. https:\/\/sigmapubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jnu.13038.","journal-title":"J Nurs Scholarsh"},{"issue":"1","key":"3417_CR85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0296760","volume":"19","author":"Y Miyazawa","year":"2024","unstructured":"Miyazawa Y, Katsuta N, Nara T, Nojiri S, Naito T, Hiki M, et al. Identification of risk factors for the onset of delirium associated with covid-19 by mining nursing records. PLoS One. 2024;19(1):1\u201314. https:\/\/doi.org\/10.1371\/journal.pone.0296760.","journal-title":"PLoS One"},{"key":"3417_CR86","doi-asserted-by":"crossref","unstructured":"Oh IY, Schindler SE, Ghoshal N, Lai AM, Payne PRO, Gupta A. Extraction of clinical phenotypes for alzheimer\u2019s disease dementia from clinical notes using natural language processing. J Am Med Inf Assoc Open. 2023;6(1):ooad014. https:\/\/academic.oup.com\/jamiaopen\/article-pdf\/6\/1\/ooad014\/49329019\/ooad014.pdf.","DOI":"10.1093\/jamiaopen\/ooad014"},{"key":"3417_CR87","doi-asserted-by":"publisher","unstructured":"Altuhaifa F, Tuhaifa DA, Ribh EA, Rebh EA. Identifying and defining entities associated with fall risk factors events found in fall risk assessment tools. Comput Methods Programs Biomed. 2023;3:100105. https:\/\/doi.org\/10.1016\/j.cmpbup.2023.100105.","DOI":"10.1016\/j.cmpbup.2023.100105"},{"key":"3417_CR88","doi-asserted-by":"publisher","first-page":"104973","DOI":"10.1016\/j.ijmedinf.2022.104973","volume":"170","author":"Z Chen","year":"2023","unstructured":"Chen Z, Zhang H, Yang X, Wu S, He X, Xu J, et al. Assess the documentation of cognitive tests and biomarkers in electronic health records via natural language processing for alzheimer\u2019s disease and related dementias. Int J Multiling Med Inf. 2023;170:104973. https:\/\/doi.org\/10.1016\/j.ijmedinf.2022.104973.","journal-title":"Int J Multiling Med Inf"},{"issue":"6","key":"3417_CR89","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.3233\/thc-230229","volume":"31","author":"M Alkhalaf","year":"2023","unstructured":"Alkhalaf M, Zhang Z, Chang HCR, Wei W, Yin M, Deng C, et al. Malnutrition and its contributing factors for older people living in residential aged care facilities: insights from natural language processing of aged care records. Technol Health Care. 2023;31(6):2267\u201378. https:\/\/doi.org\/10.3233\/thc-230229.","journal-title":"Technol Health Care"},{"issue":"2","key":"3417_CR90","doi-asserted-by":"publisher","first-page":"144","DOI":"10.51893\/2021.2.oa1","volume":"23","author":"M Young","year":"2021","unstructured":"Young M, Holmes N, Robbins R, Marhoon N, Amjad S, Neto AS, et al. Natural language processing to assess the epidemiology of delirium-suggestive behavioural disturbances in critically ill patients. Crit Care Resuscitation. 2021;23(2):144\u201353. https:\/\/doi.org\/10.51893\/2021.2.oa1.","journal-title":"Crit Care Resuscitation"},{"key":"3417_CR91","doi-asserted-by":"publisher","unstructured":"Anzaldi LJ, Davison A, Boyd CM, Leff B, Kharrazi H. Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study. BMC Geriatrics. 2017;17(1). https:\/\/doi.org\/10.1186\/s12877-017-0645-7.","DOI":"10.1186\/s12877-017-0645-7"},{"key":"3417_CR92","doi-asserted-by":"publisher","first-page":"103943","DOI":"10.1016\/j.ijmedinf.2019.08.003","volume":"130","author":"X Zhou","year":"2019","unstructured":"Zhou X, Wang Y, Sohn S, Therneau TM, Liu H, Knopman DS. Automatic extraction and assessment of lifestyle exposures for alzheimer\u2019s disease using natural language processing. Int J Multiling Med Inf. 2019;130:103943. https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.08.003.","journal-title":"Int J Multiling Med Inf"},{"key":"3417_CR93","doi-asserted-by":"crossref","unstructured":"Soysal P, Tan SG, Rogowska M, Jawad S, Smith L, Veronese N, et al. Weight loss in alzheimer\u2019s disease, vascular dementia and dementia with lewy bodies: impact on mortality and hospitalization by dementia subtype. Int J Geriatric Psychiatry. 2022;37(2). https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/gps.5659.","DOI":"10.1002\/gps.5659"},{"issue":"2","key":"3417_CR94","doi-asserted-by":"publisher","first-page":"100078","DOI":"10.1016\/j.ahr.2022.100078","volume":"2","author":"WL Leurs","year":"2022","unstructured":"Leurs WL, Lammers LA, Compagner WN, Groeneveld M, Korsten EH, van der Linden CM. Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: a case-control pilot study. Aging Health Res. 2022;2(2):100078. https:\/\/doi.org\/10.1016\/j.ahr.2022.100078.","journal-title":"Aging Health Res"},{"key":"3417_CR95","doi-asserted-by":"crossref","unstructured":"Guellil I, Andres S, Guthrie B, Anand A, Zhang H, Hasan AK, et al. Enhancing natural language processing capabilities in geriatric patient care: an annotation scheme and guidelines. In: Rapp A, Di Caro L, Meziane F, Sugumaran V, editors. Natural language processing and information systems. Cham: Springer; 2024. p. 207\u201317.","DOI":"10.1007\/978-3-031-70242-6_20"},{"key":"3417_CR96","doi-asserted-by":"publisher","first-page":"85","DOI":"10.12688\/wellcomeopenres.12600.1","volume":"2","author":"SM Kerr","year":"2017","unstructured":"Kerr SM, Campbell A, Marten J, Vitart V, McIntosh AM, Porteous DJ, et al. Electronic health record and genome-wide genetic data in generation Scotland participants. Wellcome Open Res. 2017;2:85.","journal-title":"Wellcome Open Res"},{"issue":"1","key":"3417_CR97","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3(1):160035.","journal-title":"Sci Data"},{"issue":"1","key":"3417_CR98","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1.","journal-title":"Sci Data"},{"key":"3417_CR99","doi-asserted-by":"publisher","unstructured":"Baxter R, Nind T, Sutherland J, McAllister G, Hardy D, Hume A, et al. The Scottish medical imaging archive: 57.3 million radiology studies linked to their medical records. Radiol Artif Intel. 2024; 6(1):e220266. https:\/\/doi.org\/10.1148\/ryai.220266.","DOI":"10.1148\/ryai.220266"},{"key":"3417_CR100","unstructured":"Camilleri M, Gouzou D, Al-Wasity S, Valdes Hernandez M, Alex B, Tsaftaris S, et al. A large dataset of brain imaging linked to health systems data: a whole system national cohort. In preparation."},{"key":"3417_CR101","unstructured":"Iveson M, Mukerjee M, Davidson E, Zhang H, Sherlock L, Ball E, et al. Clinically-reported covert cerebrovascular disease and risk of stroke, dementia and other neurological disease: a whole-population cohort of 413,264 people using natural language processing. In preparation."},{"issue":"1","key":"3417_CR102","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ijsu.2011.10.001","volume":"10","author":"D Moher","year":"2012","unstructured":"Moher D, Hopewell S, Schulz KF, Montori V, G\u00f8tzsche PC, Devereaux P, et al. Consort 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. Int J Surg. 2012;10(1):28\u201355. https:\/\/doi.org\/10.1016\/j.ijsu.2011.10.001.","journal-title":"Int J Surg"},{"key":"3417_CR103","doi-asserted-by":"crossref","unstructured":"Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, et al. Stard 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11). https:\/\/bmjopen.bmj.com\/content\/6\/11\/e012799.full.pdf.","DOI":"10.1136\/bmjopen-2016-012799"},{"key":"3417_CR104","doi-asserted-by":"publisher","unstructured":"Khoo W, Hsu LJ, Amon KJ, Chakilam PV, Chen WC, Kaufman Z, et al. Spill the tea: when robot conversation agents support well-being for older adults. In: Companion of the 2023 ACM\/IEEE International Conference on Human-Robot Interaction. HRI '23. New York, NY, USA: Association for Computing Machinery; 2023. https:\/\/doi.org\/10.1145\/3568294.3580067.","DOI":"10.1145\/3568294.3580067"},{"key":"3417_CR105","doi-asserted-by":"publisher","unstructured":"Hasan WU, Zaman KT, Shalan M, Wang X, Li J, Xie B, et al. CareCompanion: a personalized virtual Assistant for enhancing support and independence in ADRD patients and older adults. In: 2024 International Conference on Smart Applications, Communications and Networking (SmartNets). 2024. p. 1\u201310. https:\/\/doi.org\/10.1109\/SmartNets61466.2024.10577734.","DOI":"10.1109\/SmartNets61466.2024.10577734"},{"key":"3417_CR106","doi-asserted-by":"publisher","unstructured":"Lima MR. Home integration of conversational robots to enhance ageing and dementia care. In: Companion of the 2024 ACM\/IEEE International Conference on Human-Robot Interaction. HRI '24. New York, NY, USA: Association for Computing Machinery; 2024. p. 115\u201317. https:\/\/doi.org\/10.1145\/3610978.3638378.","DOI":"10.1145\/3610978.3638378"},{"key":"3417_CR107","doi-asserted-by":"crossref","unstructured":"Orimaye SO, Wong JSM, Golden KJ. Learning predictive linguistic features for Alzheimer\u2018s disease and related dementias using verbal utterances. In: Resnik P, Resnik R, Mitchell M, editors. Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Baltimore, Maryland, USA: Association for Computational Linguistics; 2014. https:\/\/aclanthology.org\/W14-3210\/.","DOI":"10.3115\/v1\/W14-3210"},{"key":"3417_CR108","doi-asserted-by":"crossref","unstructured":"Farzana S, Deshpande A, Parde N. How you say it matters: measuring the impact of verbal disfluency tags on automated dementia detection. In: Demner-Fushman D, Cohen KB, Ananiadou S, Tsujii J, editors. Proceedings of the 21st workshop on biomedical language processing. Dublin, Ireland: Association for Computational Linguistics; 2022. p. 37\u201348. https:\/\/aclanthology.org\/2022.bionlp-1.4\/.","DOI":"10.18653\/v1\/2022.bionlp-1.4"},{"key":"3417_CR109","doi-asserted-by":"crossref","unstructured":"de Arriba-P\u00e9rez F, Garc\u00eda-M\u00e9ndez S, Gonz\u00e1lez-Casta\u00f1o FJ, Costa-Montenegro E. Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with natural language processing capabilities. J Ambient Intell Humaniz Comput. 2022;1\u201316.","DOI":"10.1007\/s12652-022-03849-2"},{"key":"3417_CR110","doi-asserted-by":"crossref","unstructured":"Kumar MR, Vekkot S, Lalitha S, Gupta D, Govindraj VJ, Shaukat K, et al. Dementia detection from speech using machine learning and deep learning architectures. Sensors. 2022;22(23). https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9311.","DOI":"10.3390\/s22239311"},{"key":"3417_CR111","unstructured":"Ortiz-Perez D, Ruiz-Ponce P, Tom\u00e1s D, Garcia-Rodriguez J. Deep learning-based dementia prediction using multimodal data. In: Garc\u00eda Bringas P, P\u00e9rez Garc\u00eda H, Martinez-de Pison FJ, Villar Flecha JR, Troncoso Lora A, de la Cal EA, Herrero \u00c1, Mart\u00ednez \u00c1lvarez F, Psaila G, Quinti\u00e1n H, Corchado Rodriguez ES, editors. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). Cham: Springer; 2023. p. 260\u201369."},{"key":"3417_CR112","doi-asserted-by":"publisher","first-page":"126413","DOI":"10.1016\/j.neucom.2023.126413","volume":"548","author":"D Ortiz-Perez","year":"2023","unstructured":"Ortiz-Perez D, Ruiz-Ponce P, Tom\u00e1s D, Garcia-Rodriguez J, Vizcaya-Moreno MF, Leo M. A deep learning-based multimodal architecture to predict signs of dementia. Neurocomputing. 2023;548:126413. https:\/\/doi.org\/10.1016\/j.neucom.2023.126413.","journal-title":"Neurocomputing"},{"issue":"1","key":"3417_CR113","doi-asserted-by":"publisher","first-page":"13887","DOI":"10.1038\/s41598-024-64438-1","volume":"14","author":"K Lin","year":"2024","unstructured":"Lin K, Washington PY. Multimodal deep learning for dementia classification using text and audio. Sci Rep. 2024;14(1):13887.","journal-title":"Sci Rep"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03417-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03417-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03417-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T23:57:01Z","timestamp":1776729421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03417-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":113,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3417"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03417-0","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,12]]},"assertion":[{"value":"15 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This review is based solely on publicly available studies, therefore, specific ethical considerations and approvals were not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate."}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"128"}}