Hearing, listening and reading: A complex interplay of factors that contribut...HEARnet _
Research Aims:
1.Systematically map the auditory, cognitive, and linguistic abilities of children with listening concerns (as reported by parent/teacher).
2.Investigate how the ability to attend to and process incoming auditory information affects word reading and reading comprehension in school-aged children.
LANGUAGE CHARACTERISTICS SUPPORTING EARLY ALZHEIMER'S DIAGNOSIS THROUGH MACHI...hiij
Alzheimer's dementia (AD) is the most common incurable neurodegenerative disease worldwide. Apart
from memory loss, AD leads to speech disorders. Timely diagnosis is crucial to halt the progression of the
disease. However, current diagnostic procedures are costly, invasive, and distressing. Early-stage AD
manifests itself in speech disorders, which implies examining those. Machine Learning (ML) represents a
promising instrument in this context.
Nevertheless, no genuine consensus on the language characteristics to be analyzed exists. To counteract
this deficit and provide topic-related researchers with a better basis for decision-making, we present,
based on a literature review, favourable speech characteristics for the appliance toward AD detection via
ML. Research trends to apply spontaneous speech, gained from image descriptions, as analysis basis, and
points out that the combined use of acoustic, linguistic, and demographic features positively influences
recognition accuracy. In total, we have identified 97 overarching acoustic, linguistic and demographic
features.
LANGUAGE CHARACTERISTICS SUPPORTING EARLY ALZHEIMER'S DIAGNOSIS THROUGH MACHI...hiij
Alzheimer's dementia (AD) is the most common incurable neurodegenerative disease worldwide. Apart from memory loss, AD leads to speech disorders. Timely diagnosis is crucial to halt the progression of the disease. However, current diagnostic procedures are costly, invasive, and distressing. Early-stage AD manifests itself in speech disorders, which implies examining those. Machine Learning (ML) represents a promising instrument in this context.
Nevertheless, no genuine consensus on the language characteristics to be analyzed exists. To counteract this deficit and provide topic-related researchers with a better basis for decision-making, we present, based on a literature review, favourable speech characteristics for the appliance toward AD detection via ML. Research trends to apply spontaneous speech, gained from image descriptions, as analysis basis, and points out that the combined use of acoustic, linguistic, and demographic features positively influences recognition accuracy. In total, we have identified 97 overarching acoustic, linguistic and demographic features.
Hearing, listening and reading: A complex interplay of factors that contribut...HEARnet _
Research Aims:
1.Systematically map the auditory, cognitive, and linguistic abilities of children with listening concerns (as reported by parent/teacher).
2.Investigate how the ability to attend to and process incoming auditory information affects word reading and reading comprehension in school-aged children.
LANGUAGE CHARACTERISTICS SUPPORTING EARLY ALZHEIMER'S DIAGNOSIS THROUGH MACHI...hiij
Alzheimer's dementia (AD) is the most common incurable neurodegenerative disease worldwide. Apart
from memory loss, AD leads to speech disorders. Timely diagnosis is crucial to halt the progression of the
disease. However, current diagnostic procedures are costly, invasive, and distressing. Early-stage AD
manifests itself in speech disorders, which implies examining those. Machine Learning (ML) represents a
promising instrument in this context.
Nevertheless, no genuine consensus on the language characteristics to be analyzed exists. To counteract
this deficit and provide topic-related researchers with a better basis for decision-making, we present,
based on a literature review, favourable speech characteristics for the appliance toward AD detection via
ML. Research trends to apply spontaneous speech, gained from image descriptions, as analysis basis, and
points out that the combined use of acoustic, linguistic, and demographic features positively influences
recognition accuracy. In total, we have identified 97 overarching acoustic, linguistic and demographic
features.
LANGUAGE CHARACTERISTICS SUPPORTING EARLY ALZHEIMER'S DIAGNOSIS THROUGH MACHI...hiij
Alzheimer's dementia (AD) is the most common incurable neurodegenerative disease worldwide. Apart from memory loss, AD leads to speech disorders. Timely diagnosis is crucial to halt the progression of the disease. However, current diagnostic procedures are costly, invasive, and distressing. Early-stage AD manifests itself in speech disorders, which implies examining those. Machine Learning (ML) represents a promising instrument in this context.
Nevertheless, no genuine consensus on the language characteristics to be analyzed exists. To counteract this deficit and provide topic-related researchers with a better basis for decision-making, we present, based on a literature review, favourable speech characteristics for the appliance toward AD detection via ML. Research trends to apply spontaneous speech, gained from image descriptions, as analysis basis, and points out that the combined use of acoustic, linguistic, and demographic features positively influences recognition accuracy. In total, we have identified 97 overarching acoustic, linguistic and demographic features.
1. Lara Quesada
3207 Ehrlich Rd, Tampa, Fl 33618
Phone: 813-503-4840 / E-Mail: lquesada@mail.usf.edu
Objective
Obtain a Clinical Fellowship position in the medical setting.
Education
Furman University
2007-2011
Bachelor of Arts in Psychology
The University of South Florida
2012-2014
Bachelor of Arts in Communication Sciences and Disorders
The University of South Florida
2014-2016
Master of Science in Speech-Language Pathology
Experience
Speech-Language Pathology Intern at Medical Center of Trinity
Summer 2016
Worked with adults with CVA, Bell’s Palsy, AMS/Dementia/Alzheimer’s, and voice disorders such as
Spasmodic Dysphonia and Paradoxical Vocal Fold Motion on the acute inpatient neurology team.
Worked with adults with head and neck cancer status post chemoradiation in outpatient rehab.
Conducted bedside speech-language, cognitive, and swallowing evaluations.
Performed Modified Barium Swallow (MBS) studies.
Treated patients with aphasia, apraxia, dysarthria, and voice disorders utilizing functional tasks and evidence
based approaches.
Treated patients with dysphagia utilizing new approaches in evidence based practice for oral and
pharyngeal strengthening exercises to target specific physiological deficits for the patient’s individualized
deficit.
Performed Passy-Muir Valve (PMV) trials for patients on tracheostomy collar.
Provided education and training for tracheostomy and PEG tube care for patients and caregivers.
Observed feeding evaluations in the NICU.
Speech-Language Pathology Intern at Tampa General Hospital
Spring 2016
Worked in the acute setting, primarily with high acuity patients on the cardiothoracic intensive care unit
(lung transplants, heart transplants, total artificial heart implants) and head and neck cancer patients status
post laryngectomy.
Performed bedside swallow evaluations, modified barium swallow studies, cognition/language assessment,
electrolarynx training, and Passy-Muir Valve evaluations and treatment.
2. 2
Assisted in videostroboscopy, TEP changes, and VitalStim treatment.
Observed a laryngectomy and feeding evaluations in the NICU.
Speech-Language Pathology Graduate Clinician at USF Speech Clinic 2015 -
2016
Worked with a client with Global Aphasia and Apraxia, administered the Western Aphasia Battery-Revised
(WAB-R)
Administered MoCA screenings at an Assisted Living Facility in general population and memory wards
Lead a technology group designed to aid adults with aphasia by utilizing technology (e.g., applications for
AAC, text-to-speech, text-prediction, and memory/cognitive aids) to expand communication outlets and
opportunities.
Worked with adults and children with fluency disorders in advanced practicum, using the Gradual Increase
of Length and Complexity of Utterance (GILCU) approach, MindUP mindfulness curriculum, and paren t
counseling to create a communication-friendly environment at home.
Speech-Language Pathology Shadowing Experience
2014
Shadowed at St. Joseph’s Hospital (adult outpatient) and Moffitt Cancer Center. Observed assessment and
treatment of voice, dysphagia, cognition, and language in populations such as: degenerative disease, traumatic
brain injury, stroke, and head and neck cancer.
Case Manager at The Children’s Home Society of Florida
2011-2012
Worked as a liaison between the Department of Children and Families & children to attain a safe, permanent
living environment for children who were dependents of the state.
Skills and Other Related Experience
Bilingual (Spanish), experience with care of tracheostomy tubes, vents, PEG tube feeding and care.
