Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time

Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Niamh Mccombe [verfasserIn] Xuemei Ding [verfasserIn] Girijesh Prasad [verfasserIn] Paddy Gillespie [verfasserIn] David P. Finn [verfasserIn] Stephen Todd [verfasserIn] Paula L. Mcclean [verfasserIn] Kongfatt Wong-Lin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application

Übergeordnetes Werk:	In: IEEE Journal of Translational Engineering in Health and Medicine - IEEE, 2014, 10(2022), Seite 9
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:9

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JTEHM.2022.3164806

Katalog-ID:	DOAJ041655702

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ041655702
003	DE-627
005	20230308051640.0
007	cr uuu---uuuuu
008	230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/JTEHM.2022.3164806 \|2 doi
035			\|a (DE-627)DOAJ041655702
035			\|a (DE-599)DOAJcb7234639bf343c88575840a84bb0abd
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a R858-859.7
050		0	\|a R855-855.5
100	0		\|a Niamh Mccombe \|e verfasserin \|4 aut
245	1	0	\|a Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.
650		4	\|a Cost-sensitive feature selection
650		4	\|a dementia and Alzheimer’s disease diagnosis
650		4	\|a assessment speed-accuracy trade-off
650		4	\|a cognitive and functional assessments
650		4	\|a sandbox GUI application
653		0	\|a Computer applications to medicine. Medical informatics
653		0	\|a Medical technology
700	0		\|a Xuemei Ding \|e verfasserin \|4 aut
700	0		\|a Girijesh Prasad \|e verfasserin \|4 aut
700	0		\|a Paddy Gillespie \|e verfasserin \|4 aut
700	0		\|a David P. Finn \|e verfasserin \|4 aut
700	0		\|a Stephen Todd \|e verfasserin \|4 aut
700	0		\|a Paula L. Mcclean \|e verfasserin \|4 aut
700	0		\|a Kongfatt Wong-Lin \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Journal of Translational Engineering in Health and Medicine \|d IEEE, 2014 \|g 10(2022), Seite 9 \|w (DE-627)733363032 \|w (DE-600)2696555-0 \|x 21682372 \|7 nnns
773	1	8	\|g volume:10 \|g year:2022 \|g pages:9
856	4	0	\|u https://doi.org/10.1109/JTEHM.2022.3164806 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/cb7234639bf343c88575840a84bb0abd \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/9750036/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2168-2372 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 10 \|j 2022 \|h 9

Indexfelder

author_variant	n m nm x d xd g p gp p g pg d p f dpf s t st p l m plm k w l kwl
matchkey_str	article:21682372:2022----::lhiesiesassmnspiiefrigotccua
hierarchy_sort_str	2022
callnumber-subject-code	R
publishDate	2022
allfields	10.1109/JTEHM.2022.3164806 doi (DE-627)DOAJ041655702 (DE-599)DOAJcb7234639bf343c88575840a84bb0abd DE-627 ger DE-627 rakwb eng R858-859.7 R855-855.5 Niamh Mccombe verfasserin aut Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists. Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application Computer applications to medicine. Medical informatics Medical technology Xuemei Ding verfasserin aut Girijesh Prasad verfasserin aut Paddy Gillespie verfasserin aut David P. Finn verfasserin aut Stephen Todd verfasserin aut Paula L. Mcclean verfasserin aut Kongfatt Wong-Lin verfasserin aut In IEEE Journal of Translational Engineering in Health and Medicine IEEE, 2014 10(2022), Seite 9 (DE-627)733363032 (DE-600)2696555-0 21682372 nnns volume:10 year:2022 pages:9 https://doi.org/10.1109/JTEHM.2022.3164806 kostenfrei https://doaj.org/article/cb7234639bf343c88575840a84bb0abd kostenfrei https://ieeexplore.ieee.org/document/9750036/ kostenfrei https://doaj.org/toc/2168-2372 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9
spelling	10.1109/JTEHM.2022.3164806 doi (DE-627)DOAJ041655702 (DE-599)DOAJcb7234639bf343c88575840a84bb0abd DE-627 ger DE-627 rakwb eng R858-859.7 R855-855.5 Niamh Mccombe verfasserin aut Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists. Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application Computer applications to medicine. Medical informatics Medical technology Xuemei Ding verfasserin aut Girijesh Prasad verfasserin aut Paddy Gillespie verfasserin aut David P. Finn verfasserin aut Stephen Todd verfasserin aut Paula L. Mcclean verfasserin aut Kongfatt Wong-Lin verfasserin aut In IEEE Journal of Translational Engineering in Health and Medicine IEEE, 2014 10(2022), Seite 9 (DE-627)733363032 (DE-600)2696555-0 21682372 nnns volume:10 year:2022 pages:9 https://doi.org/10.1109/JTEHM.2022.3164806 kostenfrei https://doaj.org/article/cb7234639bf343c88575840a84bb0abd kostenfrei https://ieeexplore.ieee.org/document/9750036/ kostenfrei https://doaj.org/toc/2168-2372 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9
allfields_unstemmed	10.1109/JTEHM.2022.3164806 doi (DE-627)DOAJ041655702 (DE-599)DOAJcb7234639bf343c88575840a84bb0abd DE-627 ger DE-627 rakwb eng R858-859.7 R855-855.5 Niamh Mccombe verfasserin aut Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists. Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application Computer applications to medicine. Medical informatics Medical technology Xuemei Ding verfasserin aut Girijesh Prasad verfasserin aut Paddy Gillespie verfasserin aut David P. Finn verfasserin aut Stephen Todd verfasserin aut Paula L. Mcclean verfasserin aut Kongfatt Wong-Lin verfasserin aut In IEEE Journal of Translational Engineering in Health and Medicine IEEE, 2014 10(2022), Seite 9 (DE-627)733363032 (DE-600)2696555-0 21682372 nnns volume:10 year:2022 pages:9 https://doi.org/10.1109/JTEHM.2022.3164806 kostenfrei https://doaj.org/article/cb7234639bf343c88575840a84bb0abd kostenfrei https://ieeexplore.ieee.org/document/9750036/ kostenfrei https://doaj.org/toc/2168-2372 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9
allfieldsGer	10.1109/JTEHM.2022.3164806 doi (DE-627)DOAJ041655702 (DE-599)DOAJcb7234639bf343c88575840a84bb0abd DE-627 ger DE-627 rakwb eng R858-859.7 R855-855.5 Niamh Mccombe verfasserin aut Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists. Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application Computer applications to medicine. Medical informatics Medical technology Xuemei Ding verfasserin aut Girijesh Prasad verfasserin aut Paddy Gillespie verfasserin aut David P. Finn verfasserin aut Stephen Todd verfasserin aut Paula L. Mcclean verfasserin aut Kongfatt Wong-Lin verfasserin aut In IEEE Journal of Translational Engineering in Health and Medicine IEEE, 2014 10(2022), Seite 9 (DE-627)733363032 (DE-600)2696555-0 21682372 nnns volume:10 year:2022 pages:9 https://doi.org/10.1109/JTEHM.2022.3164806 kostenfrei https://doaj.org/article/cb7234639bf343c88575840a84bb0abd kostenfrei https://ieeexplore.ieee.org/document/9750036/ kostenfrei https://doaj.org/toc/2168-2372 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9
allfieldsSound	10.1109/JTEHM.2022.3164806 doi (DE-627)DOAJ041655702 (DE-599)DOAJcb7234639bf343c88575840a84bb0abd DE-627 ger DE-627 rakwb eng R858-859.7 R855-855.5 Niamh Mccombe verfasserin aut Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists. Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application Computer applications to medicine. Medical informatics Medical technology Xuemei Ding verfasserin aut Girijesh Prasad verfasserin aut Paddy Gillespie verfasserin aut David P. Finn verfasserin aut Stephen Todd verfasserin aut Paula L. Mcclean verfasserin aut Kongfatt Wong-Lin verfasserin aut In IEEE Journal of Translational Engineering in Health and Medicine IEEE, 2014 10(2022), Seite 9 (DE-627)733363032 (DE-600)2696555-0 21682372 nnns volume:10 year:2022 pages:9 https://doi.org/10.1109/JTEHM.2022.3164806 kostenfrei https://doaj.org/article/cb7234639bf343c88575840a84bb0abd kostenfrei https://ieeexplore.ieee.org/document/9750036/ kostenfrei https://doaj.org/toc/2168-2372 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9
language	English
source	In IEEE Journal of Translational Engineering in Health and Medicine 10(2022), Seite 9 volume:10 year:2022 pages:9
sourceStr	In IEEE Journal of Translational Engineering in Health and Medicine 10(2022), Seite 9 volume:10 year:2022 pages:9
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application Computer applications to medicine. Medical informatics Medical technology
isfreeaccess_bool	true
container_title	IEEE Journal of Translational Engineering in Health and Medicine
authorswithroles_txt_mv	Niamh Mccombe @@aut@@ Xuemei Ding @@aut@@ Girijesh Prasad @@aut@@ Paddy Gillespie @@aut@@ David P. Finn @@aut@@ Stephen Todd @@aut@@ Paula L. Mcclean @@aut@@ Kongfatt Wong-Lin @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	733363032
id	DOAJ041655702
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ041655702</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308051640.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/JTEHM.2022.3164806</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ041655702</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJcb7234639bf343c88575840a84bb0abd</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R858-859.7</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R855-855.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Niamh Mccombe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cost-sensitive feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dementia and Alzheimer’s disease diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">assessment speed-accuracy trade-off</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cognitive and functional assessments</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sandbox GUI application</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer applications to medicine. Medical informatics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medical technology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuemei Ding</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Girijesh Prasad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paddy Gillespie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">David P. Finn</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Stephen Todd</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paula L. Mcclean</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kongfatt Wong-Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Translational Engineering in Health and Medicine</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">10(2022), Seite 9</subfield><subfield code="w">(DE-627)733363032</subfield><subfield code="w">(DE-600)2696555-0</subfield><subfield code="x">21682372</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/JTEHM.2022.3164806</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/cb7234639bf343c88575840a84bb0abd</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9750036/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2168-2372</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="h">9</subfield></datafield></record></collection>
callnumber-first	R - Medicine
author	Niamh Mccombe
spellingShingle	Niamh Mccombe misc R858-859.7 misc R855-855.5 misc Cost-sensitive feature selection misc dementia and Alzheimer’s disease diagnosis misc assessment speed-accuracy trade-off misc cognitive and functional assessments misc sandbox GUI application misc Computer applications to medicine. Medical informatics misc Medical technology Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time
authorStr	Niamh Mccombe
ppnlink_with_tag_str_mv	@@773@@(DE-627)733363032
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	R858-859
illustrated	Not Illustrated
issn	21682372
topic_title	R858-859.7 R855-855.5 Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time Cost-sensitive feature selection dementia and Alzheimer’s disease diagnosis assessment speed-accuracy trade-off cognitive and functional assessments sandbox GUI application
topic	misc R858-859.7 misc R855-855.5 misc Cost-sensitive feature selection misc dementia and Alzheimer’s disease diagnosis misc assessment speed-accuracy trade-off misc cognitive and functional assessments misc sandbox GUI application misc Computer applications to medicine. Medical informatics misc Medical technology
topic_unstemmed	misc R858-859.7 misc R855-855.5 misc Cost-sensitive feature selection misc dementia and Alzheimer’s disease diagnosis misc assessment speed-accuracy trade-off misc cognitive and functional assessments misc sandbox GUI application misc Computer applications to medicine. Medical informatics misc Medical technology
topic_browse	misc R858-859.7 misc R855-855.5 misc Cost-sensitive feature selection misc dementia and Alzheimer’s disease diagnosis misc assessment speed-accuracy trade-off misc cognitive and functional assessments misc sandbox GUI application misc Computer applications to medicine. Medical informatics misc Medical technology
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Journal of Translational Engineering in Health and Medicine
hierarchy_parent_id	733363032
hierarchy_top_title	IEEE Journal of Translational Engineering in Health and Medicine
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)733363032 (DE-600)2696555-0
title	Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time
ctrlnum	(DE-627)DOAJ041655702 (DE-599)DOAJcb7234639bf343c88575840a84bb0abd
title_full	Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time
author_sort	Niamh Mccombe
journal	IEEE Journal of Translational Engineering in Health and Medicine
journalStr	IEEE Journal of Translational Engineering in Health and Medicine
callnumber-first-code	R
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
container_start_page	9
author_browse	Niamh Mccombe Xuemei Ding Girijesh Prasad Paddy Gillespie David P. Finn Stephen Todd Paula L. Mcclean Kongfatt Wong-Lin
container_volume	10
class	R858-859.7 R855-855.5
format_se	Elektronische Aufsätze
author-letter	Niamh Mccombe
doi_str_mv	10.1109/JTEHM.2022.3164806
author2-role	verfasserin
title_sort	alzheimer’s disease assessments optimized for diagnostic accuracy and administration time
callnumber	R858-859.7
title_auth	Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time
abstract	Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.
abstractGer	Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.
abstract_unstemmed	Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time
url	https://doi.org/10.1109/JTEHM.2022.3164806 https://doaj.org/article/cb7234639bf343c88575840a84bb0abd https://ieeexplore.ieee.org/document/9750036/ https://doaj.org/toc/2168-2372
remote_bool	true
author2	Xuemei Ding Girijesh Prasad Paddy Gillespie David P. Finn Stephen Todd Paula L. Mcclean Kongfatt Wong-Lin
author2Str	Xuemei Ding Girijesh Prasad Paddy Gillespie David P. Finn Stephen Todd Paula L. Mcclean Kongfatt Wong-Lin
ppnlink	733363032
callnumber-subject	R - General Medicine
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/JTEHM.2022.3164806
callnumber-a	R858-859.7
up_date	2024-07-03T21:26:17.587Z
_version_	1803594736690266112
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ041655702</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308051640.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/JTEHM.2022.3164806</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ041655702</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJcb7234639bf343c88575840a84bb0abd</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R858-859.7</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R855-855.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Niamh Mccombe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cost-sensitive feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dementia and Alzheimer’s disease diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">assessment speed-accuracy trade-off</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cognitive and functional assessments</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sandbox GUI application</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer applications to medicine. Medical informatics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medical technology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuemei Ding</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Girijesh Prasad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paddy Gillespie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">David P. Finn</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Stephen Todd</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paula L. Mcclean</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kongfatt Wong-Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Translational Engineering in Health and Medicine</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">10(2022), Seite 9</subfield><subfield code="w">(DE-627)733363032</subfield><subfield code="w">(DE-600)2696555-0</subfield><subfield code="x">21682372</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/JTEHM.2022.3164806</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/cb7234639bf343c88575840a84bb0abd</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9750036/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2168-2372</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="h">9</subfield></datafield></record></collection>
score	7.400386

Nicht das Richtige dabei?

Schreiben Sie uns!

Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?