Using machine learning to modify and enhance the daily living questionnaire
The Daily Living Questionnaire (DLQ) constitutes one of a number of functional cognitive measures, commonly employed in a range of medical and rehabilitation settings. One of the drawbacks of the DLQ is its length which poses an obstacle to conducting efficient and widespread screening of the public...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-04-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231169818 |
| _version_ | 1797839075679404032 |
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| author | Peleg Panovka Yaron Salman Hagit Hel-Or Sara Rosenblum Joan Toglia Naomi Josman Tal Adamit |
| author_facet | Peleg Panovka Yaron Salman Hagit Hel-Or Sara Rosenblum Joan Toglia Naomi Josman Tal Adamit |
| author_sort | Peleg Panovka |
| collection | DOAJ |
| description | The Daily Living Questionnaire (DLQ) constitutes one of a number of functional cognitive measures, commonly employed in a range of medical and rehabilitation settings. One of the drawbacks of the DLQ is its length which poses an obstacle to conducting efficient and widespread screening of the public and which incurs inaccuracies due to the length and fatigue of the subjects. Objective This study aims to use Machine Learning (ML) to modify and abridge the DLQ without compromising its fidelity and accuracy. Method Participants were interviewed in two separate research studies conducted in the United States of America and Israel, and one unified file was created for ML analysis. An ML-based Computerized Adaptive Testing (ML-CAT) algorithm was applied to the DLQ database to create an adaptive testing instrument—with a shortened test form adapted to individual test scores. Results The ML-CAT approach was shown to reduce the number of tests required on average by 25% per individual when predicting each of the seven DLQ output scores independently and reduce by over 50% when predicting all seven scores concurrently using a single model. These results maintained an accuracy of 95% (5% error) across subject scores. The study pinpoints which DLQ items are more informative in predicting DLQ scores. Conclusions Applying the ML-CAT model can thus serve to modify, refine and even abridge the current DLQ, thereby enabling wider community screening while also enhancing clinical and research utility. |
| first_indexed | 2024-04-09T15:52:23Z |
| format | Article |
| id | doaj.art-cfb050d9940741bf95239a74aab928c5 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-04-09T15:52:23Z |
| publishDate | 2023-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-cfb050d9940741bf95239a74aab928c52023-04-26T06:03:36ZengSAGE PublishingDigital Health2055-20762023-04-01910.1177/20552076231169818Using machine learning to modify and enhance the daily living questionnairePeleg Panovka0Yaron Salman1Hagit Hel-Or2Sara Rosenblum3Joan Toglia4Naomi Josman5Tal Adamit6 Department of Computer Science, , Haifa, Israel Department of Computer Science, , Haifa, Israel Department of Computer Science, , Haifa, Israel Department of Occupational Therapy, , Haifa, Israel School of Health and Natural Sciences, , Dobbs Ferry, USA Department of Occupational Therapy, , Haifa, Israel Maccabi Health-Care Services, Tel-Aviv, IsraelThe Daily Living Questionnaire (DLQ) constitutes one of a number of functional cognitive measures, commonly employed in a range of medical and rehabilitation settings. One of the drawbacks of the DLQ is its length which poses an obstacle to conducting efficient and widespread screening of the public and which incurs inaccuracies due to the length and fatigue of the subjects. Objective This study aims to use Machine Learning (ML) to modify and abridge the DLQ without compromising its fidelity and accuracy. Method Participants were interviewed in two separate research studies conducted in the United States of America and Israel, and one unified file was created for ML analysis. An ML-based Computerized Adaptive Testing (ML-CAT) algorithm was applied to the DLQ database to create an adaptive testing instrument—with a shortened test form adapted to individual test scores. Results The ML-CAT approach was shown to reduce the number of tests required on average by 25% per individual when predicting each of the seven DLQ output scores independently and reduce by over 50% when predicting all seven scores concurrently using a single model. These results maintained an accuracy of 95% (5% error) across subject scores. The study pinpoints which DLQ items are more informative in predicting DLQ scores. Conclusions Applying the ML-CAT model can thus serve to modify, refine and even abridge the current DLQ, thereby enabling wider community screening while also enhancing clinical and research utility.https://doi.org/10.1177/20552076231169818 |
| spellingShingle | Peleg Panovka Yaron Salman Hagit Hel-Or Sara Rosenblum Joan Toglia Naomi Josman Tal Adamit Using machine learning to modify and enhance the daily living questionnaire Digital Health |
| title | Using machine learning to modify and enhance the daily living questionnaire |
| title_full | Using machine learning to modify and enhance the daily living questionnaire |
| title_fullStr | Using machine learning to modify and enhance the daily living questionnaire |
| title_full_unstemmed | Using machine learning to modify and enhance the daily living questionnaire |
| title_short | Using machine learning to modify and enhance the daily living questionnaire |
| title_sort | using machine learning to modify and enhance the daily living questionnaire |
| url | https://doi.org/10.1177/20552076231169818 |
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