Summary of Challenges in the Differential Classification Of Individual Diagnoses From Co-occurring Autism and Adhd Using Survey Data, by Aditi Jaiswal et al.
Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data
by Aditi Jaiswal, Dennis P. Wall, Peter Washington
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes a machine learning approach to distinguish between autism and attention-deficit hyperactivity disorder (ADHD) in childhood, given their high co-occurrence rate. The authors identify a core set of features from the National Survey of Children’s Health that can be used for automated clinical decision support systems. They train two models: one for binary classification (developmental delay vs. none), achieving 92% sensitivity and 94% specificity; and another for four-way classification (autism, ADHD, both, or none), with 65% sensitivity and 66% specificity. While the binary model performs well, the differential classification of autism and ADHD remains a challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps develop tools to better diagnose autism and ADHD in children, which often occur together. It uses machine learning models to identify important behaviors from a large survey. The results show that these models can correctly diagnose developmental delays most of the time, but need improvement for diagnosing specific conditions like autism or ADHD. This study is an important step towards creating digital screening tools for childhood developmental delays. |
Keywords
* Artificial intelligence * Attention * Classification * Machine learning
