By Joy Carol Ming

Though prevalence and awareness for Autism Spectrum Disorder (ASD) has steadily increased, a true understanding is hard to reach because of the behavior-based nature of the diagnosis and the heterogeneity of symptoms. Parents and caregivers often informally discuss symptoms and behaviors they observe from their children with autism through online medical forums, contrasting the more traditional and structured text of electronic medical records collected by doctors. We modify an anchor-word driven topic model algorithm originally proposed by Arora et al. (2012a) to elicit and compare the medical concept topics, or “themes” from both modes of data: the novel data set of posts from autism-specific online medical forums and electronic medical records. We present methods to extract relevant medical concepts from colloquially written forum posts through the use of choice sections of the consumer health vocabulary and other filtering techniques. In order to account for the sparsity of concept data, we propose and evaluate a more robust approach to selecting anchor words that takes into account variance and inclusivity. This approach that combines concept and anchor words selection seeds the discussion about how unstructured text can influence and expand understanding of the enigmatic disorder, autism.

Some notable components of this code include the following:

• Posts. This includes both the code for scraping the forums as well as compressedversions of the raw sources scraped from forums
• Exploration. This includes the code used in data exploration and the visualizations generated for these explorations and for the paper.
• Pipeline. This includes the code that can be used to run the pipeline. Instructions for running the pipeline are included on this page and a sample pipeline is included in pipeline.sh.
• Results. The results section of the code includes the results of running the algorithm on both the online medical forum and electronic medical records. The textfile result.txt explains the parameters used to generate each result. The files are named with the number of anchors and files with *.topwords.translate are the files that include the anchor words and top words translated from CUIs in a similar format to those included in the tables throughout the paper.