ArticleNPJ digital medicine2023
FASDetect as a machine learning-based screening app for FASD in youth with ADHD.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- The Gut-Brain Axis in Fetal Alcohol Spectrum Disorder (FASD): Why the Gut Shapes Behavior, Depression, and Self-Injurious Behavior in Children with Prenatal Alcohol Exposure-A Narrative Review with a Proposal for Staged Nutritional and Microbiological Intervention.Journal of clinical medicine · 2026Review
- Biologically Informed Machine Learning Prioritizes Dietary Supplements That Protect Neural Crest Cells from Ethanol-Induced Epigenetic Dysregulation and Developmental Impairment.International journal of molecular sciences · 2025Article
- Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review.Alcohol, clinical & experimental research · 2024Review
- Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.Frontiers in neuroscience · 2024Article
- Predicting fetal alcohol spectrum disorders in preschool-aged children from early life factors.Alcohol, clinical & experimental research · 2024Article
- Machine learning-driven blood biomarker profiling and EGCG intervention in fetal alcohol spectrum disorder.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 freely available at https://fasdetect.dhc-lab.hpi.de .
Identifiers
What 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.