scholarly journals Identifying Children and Youth With Autism Spectrum Disorder in Electronic Medical Records: Examining Health System Utilization and Comorbidities

2020 ◽  
Author(s):  
Jennifer D. Brooks ◽  
Susan E. Bronskill ◽  
Longdi Fu ◽  
Farah E. Saxena ◽  
Jasleen Arneja ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Health System ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Children And Youth ◽  
System Utilization

scholarly journals Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records

2015 ◽  
Vol 45 (7) ◽  
pp. 1989-1996 ◽  
Author(s):  
Karen J. Coleman ◽  
Marta A. Lutsky ◽  
Vincent Yau ◽  
Yinge Qian ◽  
Magdalena E. Pomichowski ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Healthcare Systems ◽  
Autism Spectrum ◽  
Spectrum Disorder

Maternal metabolic risk factors for autism spectrum disorder-An analysis of electronic medical records and linked birth data

2016 ◽  
Vol 9 (8) ◽  
pp. 829-837 ◽  
Author(s):  
Natalia Connolly ◽  
Julia Anixt ◽  
Patty Manning ◽  
Daniel Ping-I Lin ◽  
Keith A. Marsolo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Autism Spectrum ◽  
Metabolic Risk Factors ◽  
Spectrum Disorder ◽  
Metabolic Risk ◽  
Birth Data

scholarly journals Identification of newborns at risk for autism using electronic medical records and machine learning

2019 ◽  
Author(s):  
Rayees Rahman ◽  
Arad Kodesh ◽  
Stephen Z Levine ◽  
Sven Sandin ◽  
Abraham Reichenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Positive Predictive Value ◽  
Electronic Medical Records ◽  
Predictive Value ◽  
False Positive ◽  
Medical Records ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Positive Rate

AbstractImportanceCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, where most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome.ObjectiveDevelop a machine learning (ML) method predicting the diagnosis of ASD in offspring in a general population sample, using parental electronic medical records (EMR) available before childbirthDesignPrognostic study of EMR data within a single Israeli health maintenance organization, for the parents of 1,397 ASD children (ICD-9/10), and 94,741 non-ASD children born between January 1st, 1997 through December 31st, 2008. The complete EMR record of the parents was used to develop various ML models to predict the risk of having a child with ASD.Main outcomes and measuresRoutinely available parental sociodemographic information, medical histories and prescribed medications data until offspring’s birth were used to generate features to train various machine learning algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross validation, by computing C statistics, sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value, PPV).ResultsAll ML models tested had similar performance, achieving an average C statistics of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85% for predicting ASD in this dataset.Conclusion and relevanceML algorithms combined with EMR capture early life ASD risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.Key pointsQuestionCan autism risk in children be predicted using the pre-birth electronic medical record (EMR) of the parents?FindingsIn this population-based study that included 1,397 children with autism spectrum disorder (ASD) and 94,741 non-ASD children, we developed a machine learning classifier for predicting the likelihood of childhood diagnosis of ASD with an average C statistic of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85%.MeaningThe results presented serve as a proof-of-principle of the potential utility of EMR for the identification of a large proportion of future children at a high-risk of ASD.

Examining the relationship between parent physical activity support behaviour and physical activity among children and youth with autism spectrum disorder

Autism ◽  
2020 ◽  
Vol 24 (7) ◽  
pp. 1783-1794 ◽  
Author(s):  
Denver M Brown ◽  
Kelly P Arbour-Nicitopoulos ◽  
Kathleen A Martin Ginis ◽  
Amy E Latimer-Cheung ◽  
Rebecca L Bassett-Gunter
Keyword(s):  
Physical Activity ◽  
Autism Spectrum Disorder ◽  
Physical Activity Behaviour ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Children And Youth ◽  
Child Physical Activity ◽  
Behavioural Regulation ◽  
The Relationship ◽  
Activity Behaviour

Children and youth with autism spectrum disorder engage in less physical activity than neurotypically developing peers. This may be due to factors associated with autism spectrum disorder at the individual and environmental level that can make physical activity participation more challenging. Parent support is a known determinant of physical activity among children and youth; however, limited research has explored the relationship between parent physical activity support behaviour and child physical activity behaviour within the autism spectrum disorder population. Guided by the multi-process action control framework, this study examined the relationship between parent physical activity support behaviour and physical activity levels of children and youth with autism spectrum disorder. Parents ( n = 201) of school-aged children and youth with autism spectrum disorder completed measures of parent physical activity support (intentions, behavioural regulation, support behaviour), as well as their child’s physical activity behaviour. Congruent with the multi-process action control model, intentions to provide physical activity support were significantly associated with parent physical activity support behaviour. Behavioural regulation of physical activity support mediated this relationship, which in turn significantly predicted child physical activity behaviour. Findings suggest parents play an instrumental role in the physical activity behaviour of children and youth with autism spectrum disorder. Family-level interventions targeting parents’ behavioural regulation strategies to provide physical activity support may be an effective strategy to increase physical activity in children and youth with autism spectrum disorder. Lay abstract Children and youth with autism spectrum disorder engage in less physical activity than neurotypically developing peers. This may be due to factors associated with autism spectrum disorder at the individual and environmental level that can make physical activity participation more challenging. Parent support is a known determinant of physical activity among children and youth; however, limited research has explored the relationship between parent physical activity support behaviour and child physical activity behaviour within the autism spectrum disorder population. The purpose of this study was to examine the relationship between parent physical activity support behaviour and physical activity levels of children and youth with autism spectrum disorder. Parents ( n = 201) of school-aged children and youth with autism spectrum disorder completed measures of parent physical activity support (intentions, behavioural regulation, support behaviour), as well as their child’s physical activity behaviour. The results showed that parent’s intentions to provide physical activity support were associated with their support behaviour for their child’s physical activity (e.g. encouragement, being active together). Parents who followed through with their intentions to provide support reported using behavioural regulation strategies such as goal setting and planning more often. Finally, the results showed parent physical activity support behaviour was positively associated with child physical activity behaviour. Findings suggest parents play an instrumental role in the physical activity behaviour of children and youth with autism spectrum disorder. Family-level interventions targeting parents’ behavioural regulation strategies to provide physical activity support may be an effective strategy to increase physical activity in children and youth with autism spectrum disorder.

scholarly journals The neonatal levels of TSB, NSE and CK-BB in autism spectrum disorder from Southern China

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Meng-na Lv ◽  
Hong Zhang ◽  
Yi Shu ◽  
Shan Chen ◽  
Yuan-yuan Hu ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Medical Records ◽  
Southern China ◽  
Neurodevelopmental Disorder ◽  
Group Identification ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Total Serum ◽  
Control Group

AbstractBackground" Autism spectrum disorder (ASD) is a serious neurodevelopmental disorder that impairs a child’s ability to communicate with others. It also includes restricted repetitive behaviors, interests and activities. Symptoms manifest before the age of 3. In the previous studies, we found structural abnormalities of the temporal lobe cortex. High spine densities were most commonly found in ASD subjects with lower levels of cognitive functioning. In the present study, we retrospectively analyzed medical records in relation to the neonatal levels of total serum bilirubin (TSB), neuron-specific enolase (NSE), creatine kinase brain band isoenzyme (CK-BB), and neonatal behavior in ASD patients from Southern China. Methods: A total of 80 patients with ASD (ASD group) were screened for this retrospective study. Among them, 34 were low-functioning ASD (L-ASD group) and 46 were high-functioning ASD (H-ASD group). Identification of the ASD cases was confirmed with a Revised Autism Diagnostic Inventory. For comparison with ASD cases, 80 normal neonates (control group) were selected from the same period. Biochemical parameters, including TSB, NSE and CK-BB in the neonatal period and medical records on neonatal behavior were collected. Results: The levels of serum TSB, NSE and CK-BB in the ASD group were significantly higher when compared with those from the control group (P < 0.01, or P < 0.05). The amounts of serum TSB, NSE and CK-BB in the L-ASD group were significantly higher when compared with those in the H-ASD group (P < 0.01, or P < 0.05). The Neonatal Behavioral Assessment Scale (NBAS) scores in the ASD group were significantly lower than that in the control group (P < 0.05). Likewise, the NBAS scores in the L-ASD group were significantly lower than that in the H-ASD group (P < 0.05). There was no association between serum TSB, NSE, CK-BB and NBAS scores (P > 0.05) in the ASD group. Conclusions: The neonatal levels of TSB, NSE and CK-BB in ASD from Southern China were significantly higher than those of healthy controls. These findings need to be investigated thoroughly by future studies with large sample.

State adoption of Medicaid 1915(c) waivers for children and youth with Autism Spectrum Disorder

2015 ◽  
Vol 8 (3) ◽  
pp. 443-451 ◽  
Author(s):  
M. Beth Merryman ◽  
Nancy A. Miller ◽  
Emily Shockley ◽  
Karen Goldrich Eskow ◽  
Gregory S. Chasson
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Children And Youth ◽  
State Adoption
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