Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows
Abstract Background Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing pa...
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-017-0497-2 |
| _version_ | 1811334692811571200 |
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| author | Telma Pereira Luís Lemos Sandra Cardoso Dina Silva Ana Rodrigues Isabel Santana Alexandre de Mendonça Manuela Guerreiro Sara C. Madeira |
| author_facet | Telma Pereira Luís Lemos Sandra Cardoso Dina Silva Ana Rodrigues Isabel Santana Alexandre de Mendonça Manuela Guerreiro Sara C. Madeira |
| author_sort | Telma Pereira |
| collection | DOAJ |
| description | Abstract Background Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”. Results The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments. |
| first_indexed | 2024-04-13T17:11:45Z |
| format | Article |
| id | doaj.art-4995a095c3774ea2854c2be27557950e |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-13T17:11:45Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-4995a095c3774ea2854c2be27557950e2022-12-22T02:38:15ZengBMCBMC Medical Informatics and Decision Making1472-69472017-07-0117111510.1186/s12911-017-0497-2Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windowsTelma Pereira0Luís Lemos1Sandra Cardoso2Dina Silva3Ana Rodrigues4Isabel Santana5Alexandre de Mendonça6Manuela Guerreiro7Sara C. Madeira8Instituto Superior Técnico, Universidade de LisboaInstituto Superior Técnico, Universidade de LisboaLaboratório de Neurociências, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de LisboaCognitive Neuroscience Research Group, Department of Psychology and Educational Sciences and Centre for Biomedical Research (CBMR), University of AlgarveFaculdade de Medicina, Universidade de CoimbraFaculdade de Medicina, Universidade de CoimbraLaboratório de Neurociências, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de LisboaLaboratório de Neurociências, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de LisboaINESC-IDAbstract Background Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”. Results The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.http://link.springer.com/article/10.1186/s12911-017-0497-2Neurodegenerative diseasesMild cognitive impairmentPrognostic predictionTime windowsSupervised learningNeuropsychological data |
| spellingShingle | Telma Pereira Luís Lemos Sandra Cardoso Dina Silva Ana Rodrigues Isabel Santana Alexandre de Mendonça Manuela Guerreiro Sara C. Madeira Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows BMC Medical Informatics and Decision Making Neurodegenerative diseases Mild cognitive impairment Prognostic prediction Time windows Supervised learning Neuropsychological data |
| title | Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
| title_full | Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
| title_fullStr | Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
| title_full_unstemmed | Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
| title_short | Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
| title_sort | predicting progression of mild cognitive impairment to dementia using neuropsychological data a supervised learning approach using time windows |
| topic | Neurodegenerative diseases Mild cognitive impairment Prognostic prediction Time windows Supervised learning Neuropsychological data |
| url | http://link.springer.com/article/10.1186/s12911-017-0497-2 |
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