Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study
Background: Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. Ho...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Karger Publishers
2021-02-01
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| Series: | Digital Biomarkers |
| Subjects: | |
| Online Access: | https://www.karger.com/Article/FullText/514105 |
| _version_ | 1818864353127956480 |
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| author | Karsten Gielis Marie-Elena Vanden Abeele Katrien Verbert Jos Tournoy Maarten De Vos Vero Vanden Abeele |
| author_facet | Karsten Gielis Marie-Elena Vanden Abeele Katrien Verbert Jos Tournoy Maarten De Vos Vero Vanden Abeele |
| author_sort | Karsten Gielis |
| collection | DOAJ |
| description | Background: Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. However, due to time and resource constraints, this follow-up is conducted at separate moments in time with large intervals in between. Casual games, embedded into the daily life of older adults, may prove to be a less resource-intensive medium that yields continuous and rich data on a patient’s cognition. Objective: To explore whether digital biomarkers of cognitive performance, found in the casual card game Klondike Solitaire, can be used to train machine-learning models to discern games played by older adults living with MCI from their healthy counterparts. Methods: Digital biomarkers of cognitive performance were captured from 23 healthy older adults and 23 older adults living with MCI, each playing 3 games of Solitaire with 3 different deck shuffles. These 3 deck shuffles were identical for each participant. Using a supervised stratified, 5-fold, cross-validated, machine-learning procedure, 19 different models were trained and optimized for F1 score. Results: The 3 best performing models, an Extra Trees model, a Gradient Boosting model, and a Nu-Support Vector Model, had a cross-validated F1 training score on the validation set of ≥0.792. The F1 score and AUC of the test set were, respectively, >0.811 and >0.877 for each of these models. These results indicate psychometric properties comparative to common cognitive screening tests. Conclusion: The results suggest that commercial card games, not developed to address specific mental processes, may be used for measuring cognition. The digital biomarkers derived from Klondike Solitaire show promise and may prove useful to fill the current blind spot between consultations. |
| first_indexed | 2024-12-19T10:30:18Z |
| format | Article |
| id | doaj.art-4adfc0c8016c483f9f50d17ab1750a58 |
| institution | Directory Open Access Journal |
| issn | 2504-110X |
| language | English |
| last_indexed | 2024-12-19T10:30:18Z |
| publishDate | 2021-02-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Digital Biomarkers |
| spelling | doaj.art-4adfc0c8016c483f9f50d17ab1750a582022-12-21T20:25:47ZengKarger PublishersDigital Biomarkers2504-110X2021-02-0151445210.1159/000514105514105Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning StudyKarsten GielisMarie-Elena Vanden AbeeleKatrien VerbertJos TournoyMaarten De VosVero Vanden AbeeleBackground: Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. However, due to time and resource constraints, this follow-up is conducted at separate moments in time with large intervals in between. Casual games, embedded into the daily life of older adults, may prove to be a less resource-intensive medium that yields continuous and rich data on a patient’s cognition. Objective: To explore whether digital biomarkers of cognitive performance, found in the casual card game Klondike Solitaire, can be used to train machine-learning models to discern games played by older adults living with MCI from their healthy counterparts. Methods: Digital biomarkers of cognitive performance were captured from 23 healthy older adults and 23 older adults living with MCI, each playing 3 games of Solitaire with 3 different deck shuffles. These 3 deck shuffles were identical for each participant. Using a supervised stratified, 5-fold, cross-validated, machine-learning procedure, 19 different models were trained and optimized for F1 score. Results: The 3 best performing models, an Extra Trees model, a Gradient Boosting model, and a Nu-Support Vector Model, had a cross-validated F1 training score on the validation set of ≥0.792. The F1 score and AUC of the test set were, respectively, >0.811 and >0.877 for each of these models. These results indicate psychometric properties comparative to common cognitive screening tests. Conclusion: The results suggest that commercial card games, not developed to address specific mental processes, may be used for measuring cognition. The digital biomarkers derived from Klondike Solitaire show promise and may prove useful to fill the current blind spot between consultations.https://www.karger.com/Article/FullText/514105mild cognitive impairmentmachine learningdigital biomarkersklondike solitairecognitive monitoringdementia |
| spellingShingle | Karsten Gielis Marie-Elena Vanden Abeele Katrien Verbert Jos Tournoy Maarten De Vos Vero Vanden Abeele Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study Digital Biomarkers mild cognitive impairment machine learning digital biomarkers klondike solitaire cognitive monitoring dementia |
| title | Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study |
| title_full | Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study |
| title_fullStr | Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study |
| title_full_unstemmed | Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study |
| title_short | Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study |
| title_sort | detecting mild cognitive impairment via digital biomarkers of cognitive performance found in klondike solitaire a machine learning study |
| topic | mild cognitive impairment machine learning digital biomarkers klondike solitaire cognitive monitoring dementia |
| url | https://www.karger.com/Article/FullText/514105 |
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