Machine Learning Differentiation of Autism Spectrum Sub-Classifications.
R Thapa, A Garikipati, M Ciobanu and 7 others
PMID 37751097WHAT IT FOUND
A machine learning model using routine questionnaire and demographic data classified autism spectrum subtypes with high accuracy.
It correctly identified 80.5% of cases in the main dataset, though positive predictive value for PDD-NOS was low at 0.221.
Key findings
01The model achieved an AUROC of 0.980 for identifying non-spectrum individuals and 0.932 for autistic disorder in the primary dataset.
02Positive predictive value for PDD-NOS was low (0.221) in the primary testing dataset, indicating many false positives for this specific subtype.
03The model correctly classified 5,951 individuals (80.5%) in the primary testing dataset, with most errors occurring between autism subtypes rather than misclassifying autistic individuals as non-spectrum.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
Read the rest of this summary
You get three full summaries a month, free, and we do not ask for a card. Search, the TL;DRs and your library stay unlimited either way.
What it does not show
The study used retrospective data, so real-world clinical performance is unknown. The model was trained on DSM-IV criteria, which are no longer the standard of care (DSM-5 is used), limiting direct applicability to current diagnoses. Positive predictive value for PDD-NOS was very low (0.221), meaning the tool generates many false positives for this group. The datasets did not allow for cross-validation between SPARK and ABIDE due to differing feature sets. SPARK data did not include DSM-5 diagnoses, so concordance with current standards could not be fully assessed.
Declared interests
No specific funding sources or conflicts of interest were declared in the provided text.
The easy way to misread this
Do not use this model for diagnosis. It was trained on outdated DSM-IV criteria and has a high false-positive rate for PDD-NOS (PPV 0.221). Furthermore, it has never been tested in a prospective clinical setting.