Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

Abstract

Background: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management. Objective: Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodicmemory screening and assessing cognitive impairment. Methods: We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions. Results: ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both %responses and %correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648-0.680) and selected general health questions (0.713-0.769). Conclusion: Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD.

Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital

“…As a consequence, the incidence of AD has been reported to be higher in those individuals with lower education (16). Previous studies showed that digital health data, cognitive performance such as memory, and neuropsychiatric symptoms can help identify those with dementia from normal subjects (17)(18)(19)(20)(21). Some research groups (19) have suggested that a diagnosis of dementia can be made from health recording data.…”

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

“…The implementation and demonstrated utility of MemTrax and these models in China, where the language and culture are drastically different from other regions of established utility (e.g., France, Netherlands, and United States) [ 7, 8, 27 ], further underscores the potential for widespread global acceptance and clinical value of a MemTrax-based platform. This is a demonstrable example in striving toward data harmonization and developing practical international norms and modeling resources for cognitive screening that are standardized and easily adapted for use worldwide.…”

“…In practice, valid clinical diagnoses and costly procedures for advanced imaging, genetic profiling, and measuring promising biomarkers are not always readily available or even feasible for many providers. Thus, in many instances, initial overall cognitive health status classification may have to be derived from models using other simple metrics provided by the patient (e.g., self-reported memory problems, current medications, and routine activity limitations) and common demographic features [ 7 ]. Registries such as the University of California Brain Health Registry (https://www.brainhealthregistry.org/) [ 27 ] and others with an inherent greater breadth of self-reported symptoms, qualitative measures (e.g., sleep and every day cognition), medications, health status, and history, and more detailed demographics will be instrumental in developing and validating the practical application of these more primitive models in the clinic.…”

“…memtrax.com) is a simple and brief continuous recognition assessment that can be self-administered online to measure challenging timed episodic memory performance where the user responds to repeated images and not to an initial presentation [5,6]. Recent research and resulting practical implications are beginning to progressively and collectively demonstrate the clinical efficacy of MemTrax in early AD and MCI screening [5][6][7]. However, direct comparison of clinical utility to existing cognitive health assessment and conventional standards is warranted to inform professional perspective and corroborate MemTrax utility in early detection and diagnostic support.…”

“…The advent and evolving practical application of artificial intelligence and machine learning in screening/detection have already demonstrated distinct practical advantages, with predictive modeling effectively guiding clinicians in the challenging assessment of cognitive/brain health and patient management [7,[9][10][11]. In our study, we chose a similar approach in MCI classification modeling and cognitive impairment severity discrimination as confirmed by clinical diagnosis from three datasets representing selected volunteer inpatients and outpatients from two hospitals in China.…”