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 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

scholarly journals Etiology of Autism the Complexity of Risk Factors in Autism Spectrum Disorder

10.5772/59109 ◽  
2015 ◽  
Author(s):  
Agnes Cristina Fett-Conte ◽  
Ana Luiza Bossolani-Martins ◽  
Dante Bruno Avanso Rosan
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder

Risk Factors for Violent Offending in Autism Spectrum Disorder

2008 ◽  
Vol 24 (8) ◽  
pp. 1358-1370 ◽  
Author(s):  
Niklas Långström ◽  
Martin Grann ◽  
Vladislav Ruchkin ◽  
Gabrielle Sjöstedt ◽  
Seena Fazel
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Violent Offending

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.

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