دورية أكاديمية
An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel.
| العنوان: | An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel. |
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| المؤلفون: | Ho SY; Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan.; Department of Geriatrics and Gerontology, Chi Mei Medical Center, Tainan, Taiwan., Chien TW; Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan., Lin ML; Department of Examination Room, Chi Mei Medical Center, Tainan, Taiwan., Tsai KT; Department of Geriatrics and Gerontology, Chi Mei Medical Center, Tainan, Taiwan.; Center for Integrative Medicine, Chi Mei Medical Center, Tainan, Taiwan.; Department of Nursing, Chung Hwa University of Medical Technology, Tainan, Taiwan. |
| المصدر: | Medicine [Medicine (Baltimore)] 2023 Jan 27; Vol. 102 (4), pp. e32670. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 2985248R Publication Model: Print Cited Medium: Internet ISSN: 1536-5964 (Electronic) Linking ISSN: 00257974 NLM ISO Abbreviation: Medicine (Baltimore) Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Hagerstown, Md : Lippincott Williams & Wilkins |
| مواضيع طبية MeSH: | Mobile Applications* , Dementia*/diagnosis, Humans ; Neural Networks, Computer ; Taiwan |
| مستخلص: | Background: Dementia is a progressive disease that worsens over time as cognitive abilities deteriorate. Effective preventive interventions require early detection. However, there are no reports in the literature concerning apps that have been developed and designed to predict patient dementia classes (DCs). This study aimed to develop an app that could predict DC automatically and accurately for patients responding to the clinical dementia rating (CDR) instrument. Methods: A CDR was applied to 366 outpatients in a hospital in Taiwan, with assessments on 25 and 49 items endorsed by patients and family members, respectively. The 2 models of convolutional neural networks (CNN) and artificial neural networks (ANN) were applied to examine the prediction accuracy based on 5 classes (i.e., no cognitive decline, very mild, mild, moderate, and severe) in 4 scenarios, consisting of 74 (items) in total, 25 in patients, 49 in family, and a combination strategy to select the best in the aforementioned scenarios using the forest plot. Using CDR scores in patients and their families on both axes, patients were dispersed on a radar plot. An app was developed to predict patient DC. Results: We found that ANN had higher accuracy rates than CNN with a ratio of 3:1 in the 4 scenarios. The highest accuracy rate (=93.72%) was shown in the combination scenario of ANN. A significant difference was observed between the CNN and ANN in terms of the accuracy rate. An available ANN-based app for predicting DC in patients was successfully developed and demonstrated in this study. Conclusion: On the basis of a combination strategy and a decision rule, a 74-item ANN model with 285 estimated parameters was developed and included. The development of an app that will assist clinicians in predicting DC in clinical settings is required in the near future. Competing Interests: The authors have no conflicts of interest to disclose. (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.) |
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| تواريخ الأحداث: | Date Created: 20230127 Date Completed: 20230130 Latest Revision: 20230202 |
| رمز التحديث: | 20240513 |
| مُعرف محوري في PubMed: | PMC9875960 |
| DOI: | 10.1097/MD.0000000000032670 |
| PMID: | 36705387 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1536-5964 |
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| DOI: | 10.1097/MD.0000000000032670 |