Current Search:   Kleiman, Michael J. (x)

View All Items

  • CSV Spreadsheet
(1 - 2 of 2)
Eye Fixations of the Face Are Modulated by Perception of a Bidirectional Social Interaction
Title
Eye Fixations of the Face Are Modulated by Perception of a Bidirectional Social Interaction.
Creator
Kleiman, Michael J., Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description
Eye fixations of the face are normally directed towards either the eyes or the mouth, however the proportions of gaze to either of these regions are dependent on context. Previous studies of gaze behavior demonstrate a tendency to stare into a target’s eyes, however no studies investigate the differences between when participants believe they are engaging in a live interaction compared to knowingly watching a pre-recorded video, a distinction that may contribute to studies of memory encoding....
Show moreEye fixations of the face are normally directed towards either the eyes or the mouth, however the proportions of gaze to either of these regions are dependent on context. Previous studies of gaze behavior demonstrate a tendency to stare into a target’s eyes, however no studies investigate the differences between when participants believe they are engaging in a live interaction compared to knowingly watching a pre-recorded video, a distinction that may contribute to studies of memory encoding. This study examined differences in fixation behavior for when participants falsely believed they were engaging in a real-time interaction over the internet (“Real-time stimulus”) compared to when they knew they were watching a pre-recorded video (“Pre-recorded stimulus”). Results indicated that participants fixated significantly longer towards the eyes for the pre-recorded stimulus than for the real-time stimulus, suggesting that previous studies which utilize pre-recorded videos may lack ecological validity.
Show less
Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004701, http://purl.flvc.org/fau/fd/FA00004701
Subject Headings
Eye -- Movements, Eye tracking, Gaze -- Psychological aspects, Nonverbal communication, Optical pattern recognition, Perceptual motor processes, Visual perception
Format
Document (PDF)
STREAMLINING CLINICAL DETECTION OF ALZHEIMER’S DISEASE USING ELECTRONIC HEALTH RECORDS AND MACHINE LEARNING TECHNIQUES
Title
STREAMLINING CLINICAL DETECTION OF ALZHEIMER’S DISEASE USING ELECTRONIC HEALTH RECORDS AND MACHINE LEARNING TECHNIQUES.
Creator
Kleiman, Michael J., Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description
Alzheimer’s disease is typically detected using a combination of cognitive-behavioral assessment exams and interviews of both the patient and a family member or caregiver, both administered and interpreted by a trained physician. This procedure, while standard in medical practice, can be time consuming and expensive for both the patient and the diagnostician especially because proper training is required to interpret the collected information and determine an appropriate diagnosis. The use of...
Show moreAlzheimer’s disease is typically detected using a combination of cognitive-behavioral assessment exams and interviews of both the patient and a family member or caregiver, both administered and interpreted by a trained physician. This procedure, while standard in medical practice, can be time consuming and expensive for both the patient and the diagnostician especially because proper training is required to interpret the collected information and determine an appropriate diagnosis. The use of machine learning techniques to augment diagnostic procedures has been previously examined in limited capacity but to date no research examines real-world medical applications of predictive analytics for health records and cognitive exam scores. This dissertation seeks to examine the efficacy of detecting cognitive impairment due to Alzheimer’s disease using machine learning, including multi-modal neural network architectures, with a real-world clinical dataset used to determine the accuracy and applicability of the generated models. An in-depth analysis of each type of data (e.g. cognitive exams, questionnaires, demographics) as well as the cognitive domains examined (e.g. memory, attention, language) is performed to identify the most useful targets, with cognitive exams and questionnaires being found to be the most useful features and short-term memory, attention, and language found to be the most important cognitive domains. In an effort to reduce medical costs and streamline procedures, optimally predictive and efficient groups of features were identified and selected, with the best performing and economical group containing only three questions and one cognitive exam component, producing an accuracy of 85%. The most effective diagnostic scoring procedure was examined, with simple threshold counting based on medical documentation being identified as the most useful. Overall predictive analysis found that Alzheimer’s disease can be detected most accurately using a bimodal multi-input neural network model using separated cognitive domains and questionnaires, with a detection accuracy of 88% using the real-world testing set, and that the technique of analyzing domains separately serves to significantly improve model efficacy compared to models that combine them.
Show less
Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013326
Subject Headings
Alzheimer's disease, Electronic Health Records, Machine learning
Format
Document (PDF)