Early detection of autism spectrum disorder in young children with machine learning using medical claims data
Objectives Early diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months bas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-02-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/29/1/e100544.full |
| _version_ | 1797783661780664320 |
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| author | Qiushi Chen Lan Kong Yu-Hsin Chen Guodong Liu |
| author_facet | Qiushi Chen Lan Kong Yu-Hsin Chen Guodong Liu |
| author_sort | Qiushi Chen |
| collection | DOAJ |
| description | Objectives Early diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months based on their medical histories using real-world health claims data.Methods Using the MarketScan Health Claims Database 2005–2016, we identified 12 743 children with ASD and a random sample of 25 833 children without ASD as our study cohort. We developed logistic regression (LR) with least absolute shrinkage and selection operator and random forest (RF) models for predicting ASD diagnosis at ages of 18–30 months, using demographics, medical diagnoses and healthcare service procedures extracted from individual’s medical claims during early years postbirth as predictor variables.Results For predicting ASD diagnosis at age of 24 months, the LR and RF models achieved the area under the receiver operating characteristic curve (AUROC) of 0.758 and 0.775, respectively. Prediction accuracy further increased with age. With predictor variables separated by outpatient and inpatient visits, the RF model for prediction at age of 24 months achieved an AUROC of 0.834, with 96.4% specificity and 20.5% positive predictive value at 40% sensitivity, representing a promising improvement over the existing screening tool in practice.Conclusions Our study demonstrates the feasibility of using machine learning models and health claims data to identify children with ASD at a very young age. It is deemed a promising approach for monitoring ASD risk in the general children population and early detection of high-risk children for targeted screening. |
| first_indexed | 2024-03-13T00:29:03Z |
| format | Article |
| id | doaj.art-6dd265a0777b4490924f7cb1b9c2c4a5 |
| institution | Directory Open Access Journal |
| issn | 2632-1009 |
| language | English |
| last_indexed | 2024-03-13T00:29:03Z |
| publishDate | 2022-02-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj.art-6dd265a0777b4490924f7cb1b9c2c4a52023-07-10T23:00:07ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092022-02-0129110.1136/bmjhci-2022-100544Early detection of autism spectrum disorder in young children with machine learning using medical claims dataQiushi Chen0Lan Kong1Yu-Hsin Chen2Guodong Liu3The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USADepartment of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USAThe Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USADepartment of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USAObjectives Early diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months based on their medical histories using real-world health claims data.Methods Using the MarketScan Health Claims Database 2005–2016, we identified 12 743 children with ASD and a random sample of 25 833 children without ASD as our study cohort. We developed logistic regression (LR) with least absolute shrinkage and selection operator and random forest (RF) models for predicting ASD diagnosis at ages of 18–30 months, using demographics, medical diagnoses and healthcare service procedures extracted from individual’s medical claims during early years postbirth as predictor variables.Results For predicting ASD diagnosis at age of 24 months, the LR and RF models achieved the area under the receiver operating characteristic curve (AUROC) of 0.758 and 0.775, respectively. Prediction accuracy further increased with age. With predictor variables separated by outpatient and inpatient visits, the RF model for prediction at age of 24 months achieved an AUROC of 0.834, with 96.4% specificity and 20.5% positive predictive value at 40% sensitivity, representing a promising improvement over the existing screening tool in practice.Conclusions Our study demonstrates the feasibility of using machine learning models and health claims data to identify children with ASD at a very young age. It is deemed a promising approach for monitoring ASD risk in the general children population and early detection of high-risk children for targeted screening.https://informatics.bmj.com/content/29/1/e100544.full |
| spellingShingle | Qiushi Chen Lan Kong Yu-Hsin Chen Guodong Liu Early detection of autism spectrum disorder in young children with machine learning using medical claims data BMJ Health & Care Informatics |
| title | Early detection of autism spectrum disorder in young children with machine learning using medical claims data |
| title_full | Early detection of autism spectrum disorder in young children with machine learning using medical claims data |
| title_fullStr | Early detection of autism spectrum disorder in young children with machine learning using medical claims data |
| title_full_unstemmed | Early detection of autism spectrum disorder in young children with machine learning using medical claims data |
| title_short | Early detection of autism spectrum disorder in young children with machine learning using medical claims data |
| title_sort | early detection of autism spectrum disorder in young children with machine learning using medical claims data |
| url | https://informatics.bmj.com/content/29/1/e100544.full |
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