Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study
المؤلفون المشاركون
Salvatore, Christian
Castiglioni, Isabella
Battista, Petronilla
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-01-31
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
الملخص EN
Subjects with Alzheimer’s disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers.
A neuropsychological assessment plays a crucial role in detecting such changes from normal conditions.
However, despite the existence of clinical measures that are used to classify and diagnose AD, a large amount of subjectivity continues to exist.
Our aim was to assess the potential of machine learning in quantifying this process and optimizing or even reducing the amount of neuropsychological tests used to classify AD patients, also at an early stage of impairment.
We investigated the role of twelve state-of-the-art neuropsychological tests in the automatic classification of subjects with none, mild, or severe impairment as measured by the clinical dementia rating (CDR).
Data were obtained from the ADNI database.
In the groups of measures used as features, we included measures of both cognitive domains and subdomains.
Our findings show that some tests are more frequently best predictors for the automatic classification, namely, LM, ADAS-Cog, AVLT, and FAQ, with a major role of the ADAS-Cog measures of delayed and immediate memory and the FAQ measure of financial competency.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Battista, Petronilla& Salvatore, Christian& Castiglioni, Isabella. 2017. Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study. Behavioural Neurology،Vol. 2017, no. 2017, pp.1-19.
https://search.emarefa.net/detail/BIM-1139756
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Battista, Petronilla…[et al.]. Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study. Behavioural Neurology No. 2017 (2017), pp.1-19.
https://search.emarefa.net/detail/BIM-1139756
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Battista, Petronilla& Salvatore, Christian& Castiglioni, Isabella. Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study. Behavioural Neurology. 2017. Vol. 2017, no. 2017, pp.1-19.
https://search.emarefa.net/detail/BIM-1139756
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139756
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر