FASDetect as a machine learning-based screening app for FASD in youth with ADHD
- Fetal alcohol-spectrum disorder (FASD) is underdiagnosed and often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Here, we develop a screening tool for FASD in youth with ADHD symptoms. To develop the prediction model, medical record data from a German University outpatient unit are assessed including 275 patients aged 0-19 years old with FASD with or without ADHD and 170 patients with ADHD without FASD aged 0-19 years old. We train 6 machine learning models based on 13 selected variables and evaluate their performance. Random forest models yield the best prediction models with a cross-validated AUC of 0.92 (95% confidence interval [0.84, 0.99]). Follow-up analyses indicate that a random forest model with 6 variables - body length and head circumference at birth, IQ, socially intrusive behaviour, poor memory and sleep disturbance - yields equivalent predictive accuracy. We implement the prediction model in a web-based app called FASDetect - a user-friendly, clinically scalable FASD risk calculator that is freelyFetal alcohol-spectrum disorder (FASD) is underdiagnosed and often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Here, we develop a screening tool for FASD in youth with ADHD symptoms. To develop the prediction model, medical record data from a German University outpatient unit are assessed including 275 patients aged 0-19 years old with FASD with or without ADHD and 170 patients with ADHD without FASD aged 0-19 years old. We train 6 machine learning models based on 13 selected variables and evaluate their performance. Random forest models yield the best prediction models with a cross-validated AUC of 0.92 (95% confidence interval [0.84, 0.99]). Follow-up analyses indicate that a random forest model with 6 variables - body length and head circumference at birth, IQ, socially intrusive behaviour, poor memory and sleep disturbance - yields equivalent predictive accuracy. We implement the prediction model in a web-based app called FASDetect - a user-friendly, clinically scalable FASD risk calculator that is freely available at https://fasdetect.dhc-lab.hpi.de.…
Author details: | Lukas Ehrig, Ann-Christin WagnerGND, Heike WolterORCiDGND, Christoph U. CorrellORCiDGND, Olga GeiselGND, Stefan KonigorskiORCiDGND |
---|---|
DOI: | https://doi.org/10.1038/s41746-023-00864-1 |
ISSN: | 2398-6352 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/37468605 |
Title of parent work (English): | npj Digital Medicine |
Publisher: | Macmillan Publishers Limited |
Place of publishing: | Basingstoke |
Publication type: | Article |
Language: | English |
Date of first publication: | 2023/07/19 |
Publication year: | 2023 |
Release date: | 2024/07/03 |
Tag: | Medical research; Psychiatric disorders |
Volume: | 6 |
Issue: | 1 |
Article number: | 130 |
Number of pages: | 9 |
Funding institution: | Projekt DEAL; Deutsche Forschungsgemeinschaft (DFG, German Research; Foundation) [491466077] |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Peer review: | Referiert |
Grantor: | Publikationsfonds der Universität Potsdam |
Publishing method: | Open Access / Gold Open-Access |
DOAJ gelistet | |
License (German): | ![]() |