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Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview.
Shuanglin Li1, Rajesh Nair2, Syed Mohsen Naqvi1
1Intelligent Sensing and Communications Group, School of EngineeringNewcastle University NE1 7RU Newcastle Upon Tyne U.K.
Summary
Machine learning effectively screens adult Attention Deficit Hyperactivity Disorder (ADHD) using speech and text analysis. This cost-effective approach aids early diagnosis, especially where psychiatric resources are limited.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Clinical Psychology
Background:
- Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder often persisting into adulthood, leading to delayed diagnoses due to psychiatric resource shortages.
- Traditional ADHD diagnostic methods using fMRI or EEG are costly and require specialized personnel.
- Speech and text analysis offer a cost-effective, non-invasive alternative for ADHD detection.
Purpose of the Study:
- To investigate the efficacy of machine learning models in detecting adult ADHD using speech and text data.
- To develop a more accessible and cost-effective screening tool for adult ADHD.
Main Methods:
- Audio data was collected from adult ADHD patients and controls using the Diagnostic Interview for ADHD in adults (DIVA).
- Speech data was converted to text using Google Cloud Speech API.
- Acoustic and linguistic features were extracted and analyzed using a support vector machine classifier.
Main Results:
- Machine learning models demonstrated promising results in classifying adult ADHD using combined acoustic and text features.
- The study highlights the potential of speech and text analysis for effective adult ADHD screening.
Conclusions:
- Speech and text analysis, powered by machine learning, offer a transformative and accessible approach to adult ADHD diagnosis.
- This method can significantly improve early detection rates and patient outcomes, particularly in underserved areas.
