ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2020
Feature Selection and Dimension Reduction of Social Autism Data.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Behavioral Assessment Reliability in Clinical Phenotyping and Biomarker Research for Autism.medRxiv : the preprint server for health sciences · 2026Article
- Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.Scientific reports · 2025Article
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- Research on a machine learning-based adaptive and efficient screening model for psychological symptoms of community correctional prisoners.Scientific reports · 2024Article
- Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping.Bioengineering (Basel, Switzerland) · 2023Article
- A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.Annual review of biomedical data science · 2023Review
- Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study.JMIR formative research · 2023Article
- Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study.JMIR pediatrics and parenting · 2022Article
- Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study.JMIR pediatrics and parenting · 2022Article
- Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections.Intelligence-based medicine · 2022Article
- A Local Community-Based Social Network for Mental Health and Well-being (Quokka): Exploratory Feasibility Study.JMIRx med · 2021Article
- Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.Cognitive computation · 2021Article
- Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening.Diagnostics (Basel, Switzerland) · 2021Article
- Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2021Article
- A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder.Brain sciences · 2020Review
- Feature replacement methods enable reliable home video analysis for machine learning detection of autism.Scientific reports · 2020Article
- Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition.Journal of personalized medicine · 2020Article
- Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Autism Spectrum Disorder (ASD) is a complex neuropsychiatric condition with a highly heterogeneous phenotype. Following the work of Duda et al., which uses a reduced feature set from the Social Responsiveness Scale, Second Edition (SRS) to distinguish ASD from ADHD, we performed item-level question selection on answers to the SRS to determine whether ASD can be distinguished from non-ASD using a similarly small subset of questions. To explore feature redundancies between the SRS questions, we performed filter, wrapper, and embedded feature selection analyses. To explore the linearity of the SRS-related ASD phenotype, we then compressed the 65-question SRS into low-dimension representations using PCA, t-SNE, and a denoising autoencoder. We measured the performance of a multilayer perceptron (MLP) classifier with the top-ranking questions as input. Classification using only the top-rated question resulted in an AUC of over 92% for SRS-derived diagnoses and an AUC of over 83% for dataset-specific diagnoses. High redundancy of features have implications towards replacing the social behaviors that are targeted in behavioral diagnostics and interventions, where digital quantification of certain features may be obfuscated due to privacy concerns. We similarly evaluated the performance of an MLP classifier trained on the low-dimension representations of the SRS, finding that the denoising autoencoder achieved slightly higher performance than the PCA and t-SNE representations.
Indexed as
Identifiers
31797640PMC6927820What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.