Autism Spectrum Disorder (ASD) is characterized by difficulties in areas of social communication, reciprocal social interaction, restricted or repetitive patterns of behavior and interests, and cognitive or significant delays in early language development. Although we are seeing consistent research being done on understanding the genetic and biological aspects of ASD, diagnosing ASD patients is solely based on behavioral symptoms.
In this thesis, we leverage unsupervised machine learning techniques to better understand ASD patients and the challenging behaviors they present. We used Doc2Vec to create neural word embedding vectors on the clinical notes presented and K-means clustering to group the patients based on similarities in the notes. The clusters will give us greater insight into the examinations done by clinicians in ABA therapy, the challenging behaviors presented, and the similarities between patients in the cluster.
","abstract_html":"<p>Autism Spectrum Disorder (ASD) is characterized by difficulties in areas of social communication, reciprocal social interaction, restricted or repetitive patterns of behavior and interests, and cognitive or significant delays in early language development. Although we are seeing consistent research being done on understanding the genetic and biological aspects of ASD, diagnosing ASD patients is solely based on behavioral symptoms.</p> <p>In this thesis, we leverage unsupervised machine learning techniques to better understand ASD patients and the challenging behaviors they present. We used Doc2Vec to create neural word embedding vectors on the clinical notes presented and K-means clustering to group the patients based on similarities in the notes. The clusters will give us greater insight into the examinations done by clinicians in ABA therapy, the challenging behaviors presented, and the similarities between patients in the cluster.</p>","abstract_has_math":false,"creators":["Pirzadeh, Raha"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Erik Linstead","Elia Eiroa Lledo","Dennis Dixon"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08-01T07:00:00Z","date_published":"2022-08-01T07:00:00Z","updated_at":"2026-07-24T01:38:24Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_theses/15","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Erik Linstead","Elia Eiroa Lledo","Dennis Dixon"]},{"key":"dc:creator","label":"Author","values":["Pirzadeh, Raha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-08-31T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_theses/15"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Autism Spectrum Disorder (ASD) is characterized by difficulties in areas of social communication, reciprocal social interaction, restricted or repetitive patterns of behavior and interests, and cognitive or significant delays in early language development. Although we are seeing consistent research being done on understanding the genetic and biological aspects of ASD, diagnosing ASD patients is solely based on behavioral symptoms.
In this thesis, we leverage unsupervised machine learning techniques to better understand ASD patients and the challenging behaviors they present. We used Doc2Vec to create neural word embedding vectors on the clinical notes presented and K-means clustering to group the patients based on similarities in the notes. The clusters will give us greater insight into the examinations done by clinicians in ABA therapy, the challenging behaviors presented, and the similarities between patients in the cluster.
"]},{"key":"dc:source","label":"Dc Source","values":["R. Pirzadeh, \"Modeling similarities among autism spectrum patients using word embeddings on clinical notes,\" M. S. thesis, Chapman University, Orange, CA, 2022. https://doi.org/10.36837/chapman.000394"]},{"key":"dc:title","label":"Title","values":["Modeling Similarities Among Autism Spectrum Patients Using Word Embeddings on Clinical Notes"]}]}],"canonical_facts":{"dc:contributor":["Erik Linstead","Elia Eiroa Lledo","Dennis Dixon"],"dc:creator":["Pirzadeh, Raha"],"dc:date.available":["2023-08-31T07:00:00Z"],"dc:description.abstract":["Autism Spectrum Disorder (ASD) is characterized by difficulties in areas of social communication, reciprocal social interaction, restricted or repetitive patterns of behavior and interests, and cognitive or significant delays in early language development. Although we are seeing consistent research being done on understanding the genetic and biological aspects of ASD, diagnosing ASD patients is solely based on behavioral symptoms.
In this thesis, we leverage unsupervised machine learning techniques to better understand ASD patients and the challenging behaviors they present. We used Doc2Vec to create neural word embedding vectors on the clinical notes presented and K-means clustering to group the patients based on similarities in the notes. The clusters will give us greater insight into the examinations done by clinicians in ABA therapy, the challenging behaviors presented, and the similarities between patients in the cluster.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_theses/15"],"dc:source":["R. Pirzadeh, \"Modeling similarities among autism spectrum patients using word embeddings on clinical notes,\" M. S. thesis, Chapman University, Orange, CA, 2022. https://doi.org/10.36837/chapman.000394"],"dc:title":["Modeling Similarities Among Autism Spectrum Patients Using Word Embeddings on Clinical Notes"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:38:24Z"}