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EURASIP J Bioinform Syst Biol. 2017 Feb 1;2017:3. doi: 10.1186/s13637-017-0057-1. eCollection 2017.

Autism spectrum disorder detection from semi-structured and unstructured medical data.

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0000 0004 1936 9174grid.16416.34Department of Computer Science, University of Rochester, Rochester, 14627 NY USA.
0000 0004 1936 9166grid.412750.5School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, 14642 NY USA.


Autism spectrum disorder (ASD) is a developmental disorder that significantly impairs patients' ability to perform normal social interaction and communication. Moreover, the diagnosis procedure of ASD is highly time-consuming, labor-intensive, and requires extensive expertise. Although there exists no known cure for ASD, there is consensus among clinicians regarding the importance of early intervention for the recovery of ASD patients. Therefore, to benefit autism patients by enhancing their access to treatments such as early intervention, we aim to develop a robust machine learning-based system for autism detection by using Natural Language Processing techniques based on information extracted from medical forms of potential ASD patients. Our detecting framework involves converting semi-structured and unstructured medical forms into digital format, preprocessing, learning document representation, and finally, classification. Testing results are evaluated against the ground truth set by expert clinicians and the proposed system achieve a 83.4% accuracy and 91.1% recall, which is very promising. The proposed ASD detection framework could significantly simplify and shorten the procedure of ASD diagnosis.


Autism spectrum disorder; Classification; Distributed representation; Medical forms

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