Interference effects from machine learning matched confusable object pairs in memory assessment tasks

Thesis

For early detection of neurodegeneration, objective cognitive tests provide a capable alternative to costly and invasive traditional biomarkers, as well as to short and infrequent in-clinic cognitive assessments. These tests, that are designed to target the cognitive processes first vulnerable to change in AD, can be remotely deployed in individuals’ lives over long durations for frequent and repeated measurements using widely accessible digital devices such as smartphones. A class of such...

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Authors: Batra, D

• DOI: 10.5287/ora-4rp1yvyp5
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: statistics; early detection; tetroactive interference; object similarity; human-in-the-loop; computer vision; neurodegeneration; machine learning in healthcare; digital biomarkers; Alzheimer's disease; objective cognitive tests; mixed-effects models; interference effects; deep learning; preclinical dementia; proactive interference
• Subjects: Computational neuroscience; Psychiatry; Deep learning (Machine learning); Statistics; Multilevel models (Statistics)