Dear Editor,Serotonin(5-HT),a pivotal neuromodulator,plays a central role in the social impairments characteristic of autism spectrum disorder(ASD).Clinical evidence reveals elevated blood 5-HT levels and reduced sero...Dear Editor,Serotonin(5-HT),a pivotal neuromodulator,plays a central role in the social impairments characteristic of autism spectrum disorder(ASD).Clinical evidence reveals elevated blood 5-HT levels and reduced serotonin transporter(5-HTT)availability in ASD patients[1],implicating serotonergic dysregulation in social behavior.展开更多
Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning sy...Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.展开更多
基金supported by Research Center for Brain Cognition and Human Development,Guangdong,China(2024B0303390003)Guangdong Basic and Applied Basic Research Foundation(2023A1515010477)+4 种基金the National Social Science Foundation of China(20&ZD296,CH)Key-Area Research and Development Program of Guangdong Province(2019B030335001)Special Funds for the Cultivation of Guangdong College Students’Scientific and Technological Innovation(“Climbing Program”Special Funds pdjh2024b118)Autism Research Special Fund of Zhejiang Foundation For Disabled Persons(2023003)Scientific Research Innovation Project of Graduate School of South China Normal University(43204021,RZ&CH).
文摘Dear Editor,Serotonin(5-HT),a pivotal neuromodulator,plays a central role in the social impairments characteristic of autism spectrum disorder(ASD).Clinical evidence reveals elevated blood 5-HT levels and reduced serotonin transporter(5-HTT)availability in ASD patients[1],implicating serotonergic dysregulation in social behavior.
基金the King Salman center for Disability Research for funding this work through Research Group No.KSRG-2024-050.
文摘Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.
