Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches
Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improveme...
Saved in:
| Published in | BMC medical informatics and decision making Vol. 19; no. 1; pp. 248 - 9 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
02.12.2019
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1472-6947 1472-6947 |
| DOI | 10.1186/s12911-019-0991-9 |
Cover
| Abstract | Background
Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP.
Methods
We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination.
Results
The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing.
Conclusions
Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. |
|---|---|
| AbstractList | Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP.BACKGROUNDIdentifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP.We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination.METHODSWe used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination.The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing.RESULTSThe final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing.Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time.CONCLUSIONSOur model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. Keywords: Dementia, General practice, Diagnosis, Prediction, Machine learning, Early detection, Primary care, Electronic health records Abstract Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time. |
| ArticleNumber | 248 |
| Audience | Academic |
| Author | Oliver, Seb Hoile, Richard van Marwijk, Harm Ford, Elizabeth Rooney, Philip Hurley, Peter Cassell, Jackie Banerjee, Sube |
| Author_xml | – sequence: 1 givenname: Elizabeth orcidid: 0000-0001-5613-8509 surname: Ford fullname: Ford, Elizabeth email: e.m.ford@bsms.ac.uk, E.m.ford@bsms.ac.uk organization: Department of Primary Care and Public Health, Brighton and Sussex Medical School – sequence: 2 givenname: Philip surname: Rooney fullname: Rooney, Philip organization: Department of Physics and Astronomy, University of Sussex – sequence: 3 givenname: Seb surname: Oliver fullname: Oliver, Seb organization: Department of Physics and Astronomy, University of Sussex – sequence: 4 givenname: Richard surname: Hoile fullname: Hoile, Richard organization: Department of Primary Care and Public Health, Brighton and Sussex Medical School – sequence: 5 givenname: Peter surname: Hurley fullname: Hurley, Peter organization: Department of Physics and Astronomy, University of Sussex – sequence: 6 givenname: Sube surname: Banerjee fullname: Banerjee, Sube organization: Faculty of Health, University of Plymouth – sequence: 7 givenname: Harm surname: van Marwijk fullname: van Marwijk, Harm organization: Department of Primary Care and Public Health, Brighton and Sussex Medical School – sequence: 8 givenname: Jackie surname: Cassell fullname: Cassell, Jackie organization: Department of Primary Care and Public Health, Brighton and Sussex Medical School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31791325$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUstu1TAQjVARfcAHsEGR2LBJsfNwYhZIVcXjikps6Nqa2ONbV7l2sJNW_R2-lAn30pcAIUuJMz7nODPnHGZ7PnjMspecHXPeibeJl5LzgnFZMCl5IZ9kB7xuy0LIut27t9_PDlO6ZIy3XdU8y_Yr3kpelc1B9mNl0E_O3ji_zmdvcEI9ockNbpY65M7n51_yMboNxJtcQ8R8hMnRYXqXQx5xiiGNRHJXSMcJCx081YY8TbMhRtiMEBf1DegL57EYEKJfCuANgegJ0eQ4OrrThSGsnYYhh3GMgRiYnmdPLQwJX-zeR9n5xw_fTj8XZ18_rU5PzgotWDUVldDQN7bpbNWjtBWAKRFBWyZba7Ru-1bquisNGA49LeRY2Yax1mDfa14dZautrglwqXYdqwBO_SqEuFYQJ6cHVHXL-4qJ3tZM1NoaWXYS6bupO6sbI0mr3GrNfoSbaxiGW0HO1GKe2pqnyDy1mKcW0vstaZz7DRpNM44wPPiThyfeXah1uFKi66SoSxJ4sxOI4fuMaVIblzQOA3gMc1JlVbKuFYIt0NePoJdhjp7mu6C6SvCaN3eoNVDXzttA9-pFVJ0I1jFGOEGo4z-gaC2GUhrQOqo_ILy63-hth79jSYB2C9AUrhTRKu0mSt2SLHDDP2fIHzH_Z-47s9K4BBXj3Sz-TvoJkPcavA |
| CitedBy_id | crossref_primary_10_1016_j_ccc_2023_02_005 crossref_primary_10_56294_mw2023125 crossref_primary_10_1016_j_iswa_2024_200388 crossref_primary_10_1016_j_cmpb_2020_105765 crossref_primary_10_1016_j_lanwpc_2024_101106 crossref_primary_10_3390_app131910630 crossref_primary_10_1371_journal_pone_0310712 crossref_primary_10_12688_wellcomeopenres_15903_1 crossref_primary_10_1016_j_jagp_2024_10_016 crossref_primary_10_1186_s12916_024_03464_2 crossref_primary_10_3389_fpubh_2020_00054 crossref_primary_10_3233_JAD_220316 crossref_primary_10_2196_20298 crossref_primary_10_3390_app131810470 crossref_primary_10_7189_jogh_14_04088 crossref_primary_10_1002_widm_1492 crossref_primary_10_1186_s12911_021_01557_z crossref_primary_10_1192_bjo_2024_23 crossref_primary_10_2196_48320 crossref_primary_10_3389_fbioe_2022_1082794 crossref_primary_10_1109_EMR_2022_3230820 crossref_primary_10_1245_s10434_022_12955_6 crossref_primary_10_1007_s40520_023_02573_x crossref_primary_10_1111_aos_14634 crossref_primary_10_1002_ajmg_b_32979 crossref_primary_10_1186_s12889_024_21081_9 crossref_primary_10_1016_j_jpsychores_2023_111553 crossref_primary_10_1186_s12877_023_04477_x crossref_primary_10_1186_s12911_022_02004_3 crossref_primary_10_1371_journal_pone_0274276 crossref_primary_10_1136_bmjopen_2020_039248 crossref_primary_10_1097_MD_0000000000032670 crossref_primary_10_1146_annurev_devpsych_120920_023709 crossref_primary_10_1016_j_jclinepi_2021_06_026 crossref_primary_10_1038_s41467_022_35157_w crossref_primary_10_2196_23934 crossref_primary_10_1080_03091902_2020_1853839 crossref_primary_10_1080_17538157_2023_2299881 crossref_primary_10_2196_20840 |
| Cites_doi | 10.3233/JAD-141413 10.1097/JGP.0b013e3181b2075e 10.1136/bmj.c3584 10.1093/ije/dyv098 10.1159/000107594 10.1111/j.2517-6161.1996.tb02080.x 10.1186/s12916-016-0549-y 10.1093/fampra/cml068 10.1371/journal.pone.0136181 10.1016/j.arcmed.2012.10.006 10.3399/bjgpopen18X101589 10.1016/j.cppeds.2010.10.006 10.1186/1472-6947-9-6 10.1177/2042098619854010 10.1177/2042098611435911 10.1371/journal.pone.0194735 10.2139/ssrn.3356631 10.1136/bmjopen-2013-004023 10.1016/S0738-3991(99)00034-8 10.1097/JTO.0b013e3181ec173d 10.1038/nrneurol.2010.54 10.1080/13607863.2011.596805 10.1371/journal.pone.0016852 10.1097/YCO.0000000000000235 10.1136/bmj.300.6732.1092 |
| ContentType | Journal Article |
| Copyright | The Author(s). 2019 COPYRIGHT 2019 BioMed Central Ltd. 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s). 2019 – notice: COPYRIGHT 2019 BioMed Central Ltd. – notice: 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7X7 7XB 88C 88E 8AL 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M0T M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/s12911-019-0991-9 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Healthcare Administration Database (Alumni) Medical Database (Alumni Edition) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Computer Science Database (NC LIVE) ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Healthcare Administration Database Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall en accès libre |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest Health Management ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 6 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Public Health |
| EISSN | 1472-6947 |
| EndPage | 9 |
| ExternalDocumentID | oai_doaj_org_article_471b306bf4064cfd9289e06b548fc5d9 10.1186/s12911-019-0991-9 PMC6889642 A608003616 31791325 10_1186_s12911_019_0991_9 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Comparative Study |
| GeographicLocations | United Kingdom United Kingdom--UK |
| GeographicLocations_xml | – name: United Kingdom – name: United Kingdom--UK |
| GrantInformation_xml | – fundername: Wellcome Trust (GB) grantid: 202133/Z/16/Z – fundername: Wellcome Trust – fundername: ; grantid: 202133/Z/16/Z |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACIWK ACPRK ACUHS ADBBV ADUKV AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS AQUVI ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAO IHR INH INR ITC K6V K7- KQ8 LK8 M0T M1P M48 M7P M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF M0N NPM 7QO 7SC 7XB 8AL 8FD 8FK EJD FR3 JQ2 K9. L7M L~C L~D P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM 123 2VQ 4.4 ADRAZ ADTOC AHSBF C1A IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c603t-36cab5f58f3be9f3aad2eeacf097fdcc7b79c482dad1ababae1e3f5007debbc13 |
| IEDL.DBID | M48 |
| ISSN | 1472-6947 |
| IngestDate | Fri Oct 03 12:53:37 EDT 2025 Sun Oct 26 04:11:07 EDT 2025 Tue Sep 30 16:59:30 EDT 2025 Fri Sep 05 08:54:26 EDT 2025 Tue Oct 07 05:22:41 EDT 2025 Mon Oct 20 22:37:58 EDT 2025 Mon Oct 20 16:32:18 EDT 2025 Thu Jan 02 22:40:26 EST 2025 Wed Oct 01 04:44:14 EDT 2025 Thu Apr 24 23:01:36 EDT 2025 Sat Sep 06 07:31:30 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Prediction Machine learning General practice Diagnosis Primary care Electronic health records Early detection Dementia |
| Language | English |
| License | Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c603t-36cab5f58f3be9f3aad2eeacf097fdcc7b79c482dad1ababae1e3f5007debbc13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
| ORCID | 0000-0001-5613-8509 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-019-0991-9 |
| PMID | 31791325 |
| PQID | 2328361415 |
| PQPubID | 42572 |
| PageCount | 9 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_471b306bf4064cfd9289e06b548fc5d9 unpaywall_primary_10_1186_s12911_019_0991_9 pubmedcentral_primary_oai_pubmedcentral_nih_gov_6889642 proquest_miscellaneous_2320876602 proquest_journals_2328361415 gale_infotracmisc_A608003616 gale_infotracacademiconefile_A608003616 pubmed_primary_31791325 crossref_citationtrail_10_1186_s12911_019_0991_9 crossref_primary_10_1186_s12911_019_0991_9 springer_journals_10_1186_s12911_019_0991_9 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2019-12-02 |
| PublicationDateYYYYMMDD | 2019-12-02 |
| PublicationDate_xml | – month: 12 year: 2019 text: 2019-12-02 day: 02 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC medical informatics and decision making |
| PublicationTitleAbbrev | BMC Med Inform Decis Mak |
| PublicationTitleAlternate | BMC Med Inform Decis Mak |
| PublicationYear | 2019 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | C Bamford (991_CR12) 2007; 24 991_CR21 991_CR22 RE Ghosh (991_CR11) 2019; 10 IH Ramakers (991_CR13) 2007; 24 991_CR4 991_CR2 H van Hout (991_CR37) 2000; 39 BC Stephan (991_CR16) 2016; 29 A Connolly (991_CR7) 2011; 15 991_CR9 991_CR18 J Chisholm (991_CR24) 1990; 300 S Banerjee (991_CR3) 2012; 43 991_CR8 991_CR5 G Rait (991_CR27) 2010; 341 991_CR34 991_CR35 S Cahill (991_CR36) 2008; 23 B Stephan (991_CR15) 2014; 42 Paul Russell (991_CR26) 2013; 3 T Williams (991_CR23) 2012; 3 E Herrett (991_CR10) 2015; 44 MH Trivedi (991_CR33) 2009; 9 S Banerjee (991_CR1) 2009 Eugene Y. H. Tang (991_CR17) 2015; 10 AG Fiks (991_CR32) 2011; 41 991_CR29 JN Mandrekar (991_CR31) 2010; 5 Robert Tibshirani (991_CR30) 1996; 58 M Pentzek (991_CR6) 2009; 17 Blossom C. M. Stephan (991_CR14) 2010; 6 K Walters (991_CR19) 2016; 14 991_CR25 E Ford (991_CR28) 2018; 13 Frank Jessen (991_CR20) 2011; 6 |
| References_xml | – ident: 991_CR9 – ident: 991_CR35 – volume: 42 start-page: S329 issue: s4 year: 2014 ident: 991_CR15 publication-title: J Alzheimers Dis doi: 10.3233/JAD-141413 – volume: 17 start-page: 965 issue: 11 year: 2009 ident: 991_CR6 publication-title: The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry doi: 10.1097/JGP.0b013e3181b2075e – volume: 341 start-page: c3584 year: 2010 ident: 991_CR27 publication-title: Br Med J doi: 10.1136/bmj.c3584 – volume: 44 start-page: 827 issue: 3 year: 2015 ident: 991_CR10 publication-title: Int J Epidemiol doi: 10.1093/ije/dyv098 – volume: 24 start-page: 300 issue: 4 year: 2007 ident: 991_CR13 publication-title: Dement Geriatr Cogn Disord doi: 10.1159/000107594 – ident: 991_CR29 – volume: 58 start-page: 267 issue: 1 year: 1996 ident: 991_CR30 publication-title: Journal of the Royal Statistical Society: Series B (Methodological) doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: 991_CR5 – ident: 991_CR25 – volume: 23 start-page: 663 issue: 7 year: 2008 ident: 991_CR36 publication-title: J Psychiatry Late Life Allied Sci – volume: 14 start-page: 1 issue: 1 year: 2016 ident: 991_CR19 publication-title: BMC Med doi: 10.1186/s12916-016-0549-y – volume: 24 start-page: 108 year: 2007 ident: 991_CR12 publication-title: Fam Pract doi: 10.1093/fampra/cml068 – volume: 10 start-page: e0136181 issue: 9 year: 2015 ident: 991_CR17 publication-title: PLOS ONE doi: 10.1371/journal.pone.0136181 – ident: 991_CR2 – volume: 43 start-page: 705 issue: 8 year: 2012 ident: 991_CR3 publication-title: Arch Med Res doi: 10.1016/j.arcmed.2012.10.006 – ident: 991_CR21 doi: 10.3399/bjgpopen18X101589 – ident: 991_CR18 – volume: 41 start-page: 60 issue: 3 year: 2011 ident: 991_CR32 publication-title: Curr Probl Pediatr Adolesc Health Care doi: 10.1016/j.cppeds.2010.10.006 – volume: 9 start-page: 6 issue: 1 year: 2009 ident: 991_CR33 publication-title: BMC Medical Inform Decis Making doi: 10.1186/1472-6947-9-6 – ident: 991_CR8 – ident: 991_CR4 – volume: 10 start-page: 204209861985401 year: 2019 ident: 991_CR11 publication-title: Ther Adv Drug Saf doi: 10.1177/2042098619854010 – volume: 3 start-page: 89 issue: 2 year: 2012 ident: 991_CR23 publication-title: Ther Adv Drug Saf doi: 10.1177/2042098611435911 – volume: 13 issue: 3 year: 2018 ident: 991_CR28 publication-title: PLoS One doi: 10.1371/journal.pone.0194735 – ident: 991_CR34 doi: 10.2139/ssrn.3356631 – ident: 991_CR22 – volume: 3 start-page: e004023 issue: 12 year: 2013 ident: 991_CR26 publication-title: BMJ Open doi: 10.1136/bmjopen-2013-004023 – volume: 39 start-page: 219 issue: 2–3 year: 2000 ident: 991_CR37 publication-title: Patient Educ Couns doi: 10.1016/S0738-3991(99)00034-8 – volume: 5 start-page: 1315 issue: 9 year: 2010 ident: 991_CR31 publication-title: J Thorac Oncol doi: 10.1097/JTO.0b013e3181ec173d – volume: 6 start-page: 318 issue: 6 year: 2010 ident: 991_CR14 publication-title: Nature Reviews Neurology doi: 10.1038/nrneurol.2010.54 – volume-title: The use of antipsychotic medication for people with dementia: time for action year: 2009 ident: 991_CR1 – volume: 15 start-page: 978 issue: 8 year: 2011 ident: 991_CR7 publication-title: Aging Ment Health doi: 10.1080/13607863.2011.596805 – volume: 6 start-page: e16852 issue: 2 year: 2011 ident: 991_CR20 publication-title: PLoS ONE doi: 10.1371/journal.pone.0016852 – volume: 29 start-page: 174 issue: 2 year: 2016 ident: 991_CR16 publication-title: Curr Opin Psychiatry doi: 10.1097/YCO.0000000000000235 – volume: 300 start-page: 1092 year: 1990 ident: 991_CR24 publication-title: BMJ doi: 10.1136/bmj.300.6732.1092 |
| SSID | ssj0017835 |
| Score | 2.4465945 |
| Snippet | Background
Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately... Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a... Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately... Abstract Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 248 |
| SubjectTerms | Aged Algorithms Analysis Artificial intelligence Artificial neural networks Bayes Theorem Bayesian analysis Case-Control Studies Clinical decision-making Clinical medicine Codes Computational Biology Datasets Dementia Dementia - diagnosis Dementia disorders Development and progression Diagnosis Disorientation Early detection Electronic Health Records Electronic records Epidemiology Family medicine Female General practice General practitioners Health care Health care reform Health Informatics Health planning Humans Information Systems and Communication Service knowledge support systems Learning algorithms Logistic Models Machine Learning Male Management of Computing and Information Systems Medical diagnosis Medical practice Medical records Medical research Medicine Medicine & Public Health Memory Mental disorders Neural networks Neural Networks, Computer Patients Prediction Primary care Primary Health Care Public health Public health movements Regression analysis Regression models Research Article Retrospective Studies Risk Assessment Schizophrenia Socialized medicine State Medicine Support Vector Machine Support vector machines theory United Kingdom |
| SummonAdditionalLinks | – databaseName: en accès libre dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQDzwOiDcpBRkJCYkq6iZOHJtbQVQVqJxYqTfLT1hpyVabXaH-HX4pM46TbkBqL2hPWdtR7BnPwzPzmZA3pa21E7bKy0aGvHIF7DlTO_Baha29bipj8Rzy7Cs_nVefz-vznau-MCeshwfuF-4IhKcBs9YE0DyVDU6Ch-DhGSztYGsXS_dmQg7OVIof4HlGimEWgh91oNVi_RimB8kilxMtFMH6_xXJOzrp73zJMWh6j9zZthf68pdeLnf00skDcj8ZlPS4n8hDcsu3j8jtsxQyf0x-96W4sZyJYsUYRg28oy6eCy40XbR0_oVe9KgTFDPBaAJb7d5TTdd-s14N9ZjQ3Pk8pbfTCE1L-yx2fPvPmJjp83QTxXeqW0eHswrqr-6iRcagA5q5756Q-cmnbx9P83QxQ275jG1yxq02dahFYMbLwLR2pQcJHmayCc7axjTSVqJ02hXawM8XnoUazBHnjbEFe0r22lXrnxNqYEBlBDfMiEo3Aby_wGXw4Odgya7NyGwglLIJtRwvz1iq6L0IrnraKqCtQtoqmZF345C0eNd1_oDUHzsi2nb8A3hQJR5UN_FgRt4i7yiUCfBxVqfSBpgiomupY452OeMFz8jBpCfsZTttHrhPJVnSKbB5BTSCpZWR12MzjsT8uNavtrEPYgvyWZmRZz2zjlNiiEDLShjdTNh4MudpS7v4EZHGuRASHNSMHA4Mf_VZ1yzp4bgnbibA_v8gwAtyt8TNjWlG5QHZ26y3_iUYixvzKsqFP2TUaSw priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3paxNBFB9qCh6IaK26WmUEQbAszV6zs4JIKy1FaRAx0G_DnDUQNzEH4r_jX-p7s7ObrkIkn5KZCTs775h3_R4hr1JdSMN1Hqdl5eLcJMBzqjBgtXJdWFnmSqMf8mLEzsf5x8vicoeM2loYTKtsZaIX1Gam0Ud-BJqfZ6BLkuL9_EeMXaMwutq20JChtYJ55yHGbpDdFJGxBmT35HT0-UsXV0A_R4htJpwdLUHb-boyTBuqkrjqaScP4v-vqL6mq_7Oo-yCqXfIrXU9l79-yun0mr46u0_uhYsmPW4o4wHZsfUeuXkRQul75G7jsKNNHdJD8rsp2fVlTxQryzC6YA013n84kXRS0_EnOm_QKShmjNEAyrp8SyVd2NVi1tZtwvDSxiENnnoIW9pku-O_f_cJnDYOHSuuqKwNbX0a1G561iIB0Rb13C73yfjs9OuH8zg0cIg1G2arOGNaqsIV3GXKVi6T0qQWJL0bVqUzWpeqrHTOUyNNIhV8bGIzV8C1xVildJI9IoN6VtsnhCpYkCvOVKZ4LksHVqJjlbNgD2Fpr47IsD04oQO6OTbZmApv5XAmmrMWcNYCz1pUEXnTLQkvb9vkE6SGbiKicvsfZosrEZhcgKJXYIIpB7ekXDtTgTVr4TtYhU4XBv7kNdKSQNkBD6dlKIGALSIKlzhmeH8HKmcROejNBJ7X_eGWGkWQOUux4ZCIvOyGcSXm0dV2tvZzEIOQDdOIPG6It9tShki1WQqryx5Z9_bcH6kn3zwiOeO8AkM2IoctA2wea8srPex45P8H8HT7lp-R2ymyMSYapQdksFqs7XO4Lq7UiyAD_gDt5GxC priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3di9QwEA96gh8P4rfVUyIIgkdx27Rp4tt5eBzK-eTCvYV86sLaPba7iP-Of6kzadrbqpzIPnUnCU1nkpnJzPxCyMvS1toJW-VlI0NeuQLWnKkdeK3C1l43lbF4Dnn6iZ_Mqw9n9VkCi8ZamN34fSH4mw70Uaz8wsQeWeTyKrkGOorHuCw_GgMGeICRgpZ_7TZROxGd_889eEcJ_Z4gOUZJb5Eb2_Zc__iul8sdRXR8h9xOFiQ97Fl-l1zx7T1y_TTFyO-Tn33tbaxfolgihmEC76iLB4ELTRctnX-k5z3MBMXUL5rQVbu3VNO136xXQwEmkDufp3x2GrFoaZ-2jqN_i5mYPk9XT3yhunV0OJyg_uLyWZQEOsCX--4BmR-__3x0kqebGHLLZ2yTM261qUMtAjNeBqa1Kz1s2WEmm-CsbUwjbSVKp12hDfx84Vmowf5w3hhbsIdkr121_jGhBjpURnDDjKh0E8DdC1wGD44N1ujajMwGRimbYMrxtoyliu6K4KrnrQLeKuStkhl5PXZJH--yxu-Q-2NDhNeOf4DUqbRaFWhsA76UCWDuVDY4CW6ph2dw74KtHQzyCmVH4SYAL2d1qmWAKSKcljrkaIgzXvCM7E9awuK1U_IgfSptHp0CI1cAEUyrjLwYydgTE-Jav9rGNggmyGdlRh71wjpOiSHkLCuhdzMR48mcp5R28TVCi3MhJHikGTkYBP7itS75pAfjmvg3A57819hPyc0SVzEmEJX7ZG-z3vpnYAZuzPO4AfwCw_tZVw priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3rixMxEA9nD3x88P1YPSWCIHhsr91HdtdvVTwO5Q4RC-enkOdZ2m5Lt0X0z_EvdSab3eueciJIv7RNsjTTmclMMr9fCHkRqVToXCVhlBU2TPQQbE6mGrLWXKVGZIlUuA95fMKOxsn70_R0h3xqsDByrvBQ2UFxNN4yMxfT_jYSfebcN7xR04OltrXV5-yggoXLQcSwAqgYhsUVsstSiM97ZHd88nH0xcGMsihkRZL5480_jussUI7H_3dvvbVcXSylbM9Tb5Brm3Ipvn8Ts9nWknV4i1TNZOtKlWl_s5Z99eMCD-T_lcZtctNHuHRUq-QdsmPKu-TqsT_Dv0d-1thgh6-iCGHDYwyjqXYblRNBJyUdf6DLmgaDYmka9eyv1Wsq6MqsV4sGIArNlQl9vT11XLm0LqvHp89dpagJ_dUYZ1SUmjabJ9ScX46LmkobenVT3Sfjw3ef3x6F_qaIULFBvA5jpoRMbZrbWJrCxkLoyMCSYgdFZrVSmcwKleSRFnooJLzM0MQ2hfhIGynVMH5AeuWiNI8IlTAgkTmTscwTkVlIRy0rrIHECzHEKiCDRj248jTqeJvHjLt0Kme8lj0H2XOUPS8C8qod4oV3Wec3qHNtR6T_dl8sVmfcexMOEYWEXE9aCMcSZXUBabOBz5B-WpVqeMhL1FiOmoPKITzWAqaIdF98xDBRiNmQBWSv0xOci-o2NzrPvXOrOAThOTRC6BeQ520zjsSCvdIsNq4Pkh2yQRSQh7WJtFOKkRI3jmB01jGezpy7LeXkq6M-Z3leQMYckP3GzM5_1iUi3W8t8e9_wON_6v2EXI_QyrDAKdojvfVqY55CmLqWz7zf-QUrBJI3 priority: 102 providerName: Unpaywall |
| Title | Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches |
| URI | https://link.springer.com/article/10.1186/s12911-019-0991-9 https://www.ncbi.nlm.nih.gov/pubmed/31791325 https://www.proquest.com/docview/2328361415 https://www.proquest.com/docview/2320876602 https://pubmed.ncbi.nlm.nih.gov/PMC6889642 https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-019-0991-9 https://doaj.org/article/471b306bf4064cfd9289e06b548fc5d9 |
| UnpaywallVersion | publishedVersion |
| Volume | 19 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: RBZ dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: KQ8 dateStart: 20010401 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: KQ8 dateStart: 20010101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: ABDBF dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: DIK dateStart: 20010101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: RPM dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: 8FG dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal: Open Access Journals [open access] customDbUrl: eissn: 1472-6947 dateEnd: 20250930 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: M48 dateStart: 20010401 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: AAJSJ dateStart: 20011201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: C6C dateStart: 20010112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjR1ri9NAcLkH-Pggvo2eZQVR8Ig2r00iiLTl6qG0HIeF0y9hn2ehpr0-0Ps7_lJnNpv0qscphUA6u0t2d2ZnZudFyPNQJlxlMvbDNDd-rAKgOZEo0FozmWiexkLiPeRgyA5H8ceT5GSL1NZzt4CLS1U7rCc1mk9e_zw7fw8E_84SfMbeLIBn2egwdP7JAz9_MTvzsa4U2l9dkY1tsgu8K8fiDoN4bWfAew8bf5SGPsvj1Nk9Lx11g3PZBP9_H-MX-NifPpaNofUmub4qZ_z8B59MLvCy_m1yywmhtFNhzR2ypcu75NrAmdnvkV9V-K4NgaIYZYaWBq2osneJY07HJR19orMqUwVF7zHqErQu3lJO53o5n9YxnABeaN-5xFObzpZWnu84-nfrzKl9V73ilPJS0fp-g-p1_VpEJlpnQNeL-2TUP_jcO_RdMQdfsna09CMmuUhMkplI6NxEnKtQw6lv2nlqlJSpSHMZZ6HiKuACfjrQkUlAhFFaCBlED8hOOS31I0IFdIhFxkQkspinBjRGw3KjQTfCMF_pkXa9UYV0mc6x4MaksBpPxopqbwvY2wL3tsg98qrp4hbvqsZd3P2mIWbotn9M56eFI_gCmL4AdUwYkJhiaVQOmq2Gd9AQjUwUDPIScadAzIaPk9yFQ8AUMSNX0WEoy0csYB7Z22gJ9C83wTX2FTX5FCAnZwAE6cwjzxow9kSfulJPV7YN5iNk7dAjDytkbaYUYdbaKITe6QYab8x5E1KOv9ns5CzLclBqPbJfI_z6s65Y0v2GJv69AY__Y05PyI0QaRc9j8I9srOcr_RTkB-XokW205MUnln_Q4vsdg-GR8fw1mO9lr2RadkjAp7H3a8AHw2POl9-A-uRdz0 |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3bahNBdKgVrCKi9bZadQRFsCzN3ncFkXopqWn61ELexrnWQNzEbELp7_gBfqPnzM5uugrxqeRpMxd29tzm3Al5FcqEq1zGfpgVxo9VADQnEgVaay4TzbNYSLRDDo_T_mn8dZSMNsjvJhcGwyobnmgZtZpKtJHvgeTPI5AlQfJh9tPHrlHoXW1aaNRoMdAX56CyVe8PPwN8X4fhwZeTT33fdRXwZdqLFn6USi4Sk-QmErowEecq1MB-TK_IjJIyE1kh4zxUXAVcwE8HOjIJyFKlhZBBBPteI9fjCHgJ0E82ahW8AK0oznMa5OleBbLUZq1hUFIR-EVH9tkWAf8KgkuS8O8ozdZVe4tsLcsZvzjnk8klaXhwl9xx11i6X-PdPbKhy21yY-gc9dvkdm0OpHWW033yq04ItklVFPPW0HehFVXWOjnmdFzS0wGd1bUvKMajUVfytXpHOZ3rxXzaZIXCcKV9F2RPbYFcWsfS4-4_bHio9l0_jDPKS0UbiwnVq464iJ60qamuqwfk9EoA-ZBsltNSPyZUwIJY5KmIRB7zzIAOatLCaNC2MHFYeqTXAI5JVzsdW3hMmNWh8pTVsGYAa4awZoVH3rZL3MdbN_kjYkM7EWt-2z-m8zPmWAiDa4QABU8YuIPF0qgCdGUNz6BzGpko2OQN4hJDzgQvJ7lLsIAjYo0vtp-idgA0lHpkpzMTOIrsDjfYyBxHq9iK_jzysh3GlRilV-rp0s7BCodpL_TIoxp52yNFWAc3CmF11kHrzpm7I-X4u613nuZ5AWqyR3YbAli91ppPutvSyP8B8GT9kV-Qrf7J8IgdHR4PnpKbIZI0hjSFO2RzMV_qZ3AxXYjnlhtQ8u2q2c8fff2naQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELdgSAMeEN8LDDASEhJTtObLcXgbhWowNvFApb1Z_hyVurRqWiH-Hf5S7hwnWwANoT6l_lCcu7PvfHe_I-RVqgtpuM7jtKxcnJsEZE4VBqxWrgsry1xpvIc8PmGH0_zTaXEa6pw2XbR755JscxoQpale7y-Na0Wcs_0GTimfD4bhPlUSV9fJjRwONyxhMGbj3o2A1xrBlfnXYYPDyGP2_7kzXzqafg-b7H2nt8nNTb2UP77L-fzS8TS5S-4EvZIetIxwj1yz9X2yfRw85w_IzzYj12c1UUwcQ-eBNdT468GZpLOaTo_osgWfoBgQRgPmavOWSrqy69WiS8uE5sbGIcqdeoRa2gaz4-znPj7TxqEgxRmVtaHdlQW1FyVpkT9oB2pum4dkOvnwdXwYh_oMsWajbB1nTEtVuIK7TNnKZVKa1MJG7kZV6YzWpSornfPUSJNIBT-b2MwVoJUYq5ROskdkq17UdodQBQNyxZnKFM9l6cAIdKxyFswdzNzVERl1hBI6gJdjDY258EYMZ6KlrQDaCqStqCLyph8SPt5Vnd8h9fuOCLrt_1iszkSQYQHnuAILSzlQgnLtTAXGqoVnMPqcLgxM8hp5R-DWAC-nZchwgCUiyJY4YKieZyxhEdkd9ASR1sPmjvtE2FIaAaovh0ZQuCLysm_GkRgmV9vFxvdBiEE2SiPyuGXWfkkZAtFmKYwuB2w8WPOwpZ5984DjjPMK7NSI7HUMf_FaV3zSvV4m_k2AJ_819wuy_eX9RHz-eHL0lNxKUaAxwijdJVvr1cY-Az1xrZ77veAXE2xkjQ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3rixMxEA9nD3x88P1YPSWCIHhsr91HdtdvVTwO5Q4RC-enkOdZ2m5Lt0X0z_EvdSab3eueciJIv7RNsjTTmclMMr9fCHkRqVToXCVhlBU2TPQQbE6mGrLWXKVGZIlUuA95fMKOxsn70_R0h3xqsDByrvBQ2UFxNN4yMxfT_jYSfebcN7xR04OltrXV5-yggoXLQcSwAqgYhsUVsstSiM97ZHd88nH0xcGMsihkRZL5480_jussUI7H_3dvvbVcXSylbM9Tb5Brm3Ipvn8Ts9nWknV4i1TNZOtKlWl_s5Z99eMCD-T_lcZtctNHuHRUq-QdsmPKu-TqsT_Dv0d-1thgh6-iCGHDYwyjqXYblRNBJyUdf6DLmgaDYmka9eyv1Wsq6MqsV4sGIArNlQl9vT11XLm0LqvHp89dpagJ_dUYZ1SUmjabJ9ScX46LmkobenVT3Sfjw3ef3x6F_qaIULFBvA5jpoRMbZrbWJrCxkLoyMCSYgdFZrVSmcwKleSRFnooJLzM0MQ2hfhIGynVMH5AeuWiNI8IlTAgkTmTscwTkVlIRy0rrIHECzHEKiCDRj248jTqeJvHjLt0Kme8lj0H2XOUPS8C8qod4oV3Wec3qHNtR6T_dl8sVmfcexMOEYWEXE9aCMcSZXUBabOBz5B-WpVqeMhL1FiOmoPKITzWAqaIdF98xDBRiNmQBWSv0xOci-o2NzrPvXOrOAThOTRC6BeQ520zjsSCvdIsNq4Pkh2yQRSQh7WJtFOKkRI3jmB01jGezpy7LeXkq6M-Z3leQMYckP3GzM5_1iUi3W8t8e9_wON_6v2EXI_QyrDAKdojvfVqY55CmLqWz7zf-QUrBJI3 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identifying+undetected+dementia+in+UK+primary+care+patients%3A+a+retrospective+case-control+study+comparing+machine-learning+and+standard+epidemiological+approaches&rft.jtitle=BMC+medical+informatics+and+decision+making&rft.au=d%2C+Elizabeth&rft.au=Rooney%2C+Philip&rft.au=Oliver%2C+Seb&rft.au=Hoile%2C+Richard&rft.date=2019-12-02&rft.issn=1472-6947&rft.eissn=1472-6947&rft.volume=19&rft.issue=1&rft.spage=248&rft_id=info:doi/10.1186%2Fs12911-019-0991-9&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1472-6947&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1472-6947&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1472-6947&client=summon |