Neuropsychology’s machine assistant : predicting functional outcomes with machine learning
department or school: Arts and Social Sciences, Irving K. Barber Faculty of (Okanagan)
department or school: Psychology, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Armstrong, Graham
issued: 2023-02-01T20:43:48Z
issued: 2023
related degree: Master of Arts - MA
degree grantor: University of British Columbia
description: Stroke affects 50,000 Canadians every year and millions of people worldwide. Stroke occurs when the brain tissue is damaged by being deprived of essential compounds from the blood stream. Patients with stroke frequently experience a variety of impairments including prolonged cognitive deficits. Neuropsychological assessment is the most effective way of measuring the nature and magnitude of cognitive impairments. One of the main functions of neuropsychological assessment in a rehabilitation setting is to inform clinical decision-making regarding patient treatment trajectories. Recommendations provided by a neuropsychologist depend on the utility of the neuropsychological battery for predicting the patient’s levels of daily functioning. Daily functioning assesses an individual’s ability to complete tasks of daily life. However, this research has lacked the necessary specificity to capture the comprehensive association between cognition and functional abilities. Further, domain specific score practices, regularly employed by clinicians, can lead to substantial levels of misclassification. The goal of the present study was to evaluate machine learning multiple linear regression relative to ordinary least squares (OLS) on a multivariate level as well as at the level of individual predictors, the clinical utility of a comprehensive neuropsychological battery, lastly the potential impact of an adjunctive clinical decision algorithm in a rehabilitation setting was examined. Data were taken from 167 neuropsychological assessments from patients with stroke who participated in rehabilitation at Kelowna General Hospital. The results indicate that a broad neuropsychological assessment accounts for a significant level of post stroke daily functioning scores. Machine learning is a more powerful tool for identifying individual cognitive predictors of stroke than traditional OLS methods. Machine learning did not provide incremental improvement over OLS methods at a multivariate leve…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83775
note: Neuropsychology’s Machine Assistant: Predicting Functional Outcomes with Machine Learning By Graham Armstrong A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE COLLEGE OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) January 2023 © Graham Armstrong, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Neuropsychology’s Machine Assistant: Predicting Functional Outcomes with Machine Learning submitted by Graham Armstrong in partial fulfillment of the requirements of the degree of Master of Arts. Dr. Brian O’Connor, Department of Psychology Supervisor Dr. Maya Libben, Department of Psychology Supervisory Committee Member Dr. Harry Miller, Department of Psychology Supervisory Committee Member Dr. Brian Dalton, School of Health and Exercises Science University Examiner Dr. Zoë Soon, Department of Biology Neutral Chair iii Abstract Stroke affects 50,000 Canadians every year and millions of people worldwide. Stroke occurs when the brain tissue is damaged by being deprived of essential compounds from the blood stream. Patients with stroke frequently experience a variety of impairments including prolonged cognitive deficits. Neuropsychological assessment is the most effective way of measuring the nature and magnitude of cognitive impairments. One of the main functions of neuropsychological assessment in a rehabilitation setting is to inform clinical decision-making regarding patient treatment trajectories. Recommendations provided by a neuropsychologist depend on the utility of the neuropsychological battery for predicting the patient’s levels of daily functioning. Daily functioning assesses an individual’s ability to complete tasks of daily life. However, this research has lacked the necessary specificity to capture the comprehensive association between cognition and functional abilities. Further, dom…
has type: Thesis/Dissertation
date issued: 2022-02
is shown at: 10.14288/1.0423849
language: eng
degree discipline: Psychology
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: O'Connor, Brian
type: Text
identifier URI: http://hdl.handle.net/2429/83775