attention-deficit hyperactivity disorder]."
That was a conclusion reached in the paper by Marlena Duda and colleagues  (open-access) building on their previous foray into this important research area (see here). Last time around  this research group - the Duda/Wall et al research combination - set the scene for boiling down the Social Responsiveness Scale (SRS) from 65 items to something considerably smaller when it came to distinguishing autism from ADHD. This based on the idea that autism and ADHD are not unstrange diagnostic bedfellows (see here).
This time around, researchers set out to "expand upon our prior work by including a novel crowdsourced data set of responses to our predefined top 15 SRS-derived questions from parents of children with ASD (n=248) or ADHD (n=174) to improve our model’s capability to generalize to new, ‘real-world’ data." Mention of the term 'crowdsourced' means that authors utilised various online social media platforms to "to inform the community of the study" and gather responses. Importantly, they note that "diagnoses of ASD or ADHD were provided as parent report."
Results: once again applying various machine learning algorithms to their recently captured data and "mixing these novel survey data with our initial archival sample (n=3417)" authors reported some interesting findings. Taking the two samples - the archival samples and the recent crowdsourced data - together they reported on the creation of "a classification algorithm that can generalize well to unseen data (AUC=0.89±0.01), even when those data have more natural variablity like the kind seen in our survey sample." This was based on the use of 15 items from the SRS.
But... things were not all smooth sailing in this latest research effort. Bearing in mind the use of those 'parent reported' autism and ADHD participants in this latest study, authors noted that 'real-world' data is not necessarily the same as the very clinical data relied upon on the last research occasion. So: "In the archival sample, the responses for ADHD subjects were more uniform and on average less severe than the ADHD responses in the survey sample."
Still, these are important results albeit requiring 'continued evaluation' as further crowdsourced and other data filter through. Indeed 'adaption' to new data seems to be something that the authors are particularly keen on to "further improve the generalizability of the classifier." I continue to applaud their research in this area as a function of their efforts (see here) to make autism and/or ADHD screening quicker, easier and more cost-effective.
And on that last point. it is timely that such research continues given what is being proposed in certain parts of England when it comes to autism diagnoses (see here). Indeed, the suggestion of "restricting an autism diagnosis to only the most severe cases" as a function of some quite spectacular increasing demand - "The team is supposed to carry out 750 assessments a year. But it is getting almost double that level of demand, with about 25 referrals a week" - reiterates a need to streamline diagnostic services to make screening/diagnosis quicker, easier and more cost-effective.
For those also who have said 'so what' to the increase in cases of autism (yes, someone actually did albeit with caveats), such proposals to potentially restrict autism diagnoses, I would say, are a direct result of such a mindset to 're-think' autism. Although well meaning, if enough people talk about difference over disability for example, purse string holders in the NHS (National Health Service) were eventually bound to ask 'why diagnose?' and 'why offer services?' (services that can cost quite a lot and even for those with 'severe autism' are often not there). As other authors have eloquently argued (see here) and indeed, foretold, mixed in with the current economic situation being put forward all in the name of austerity, low-hanging NHS services fruit like autism screening/assessment were certain to be eventually targeted and the 'difference over disability' framing unfortunately provides ample ammunition for such proposals...
 Duda M. et al. Crowdsourced validation of a machine-learning classification system for autism and ADHD. Transl Psychiatry. 2017 May 16;7(5):e1133.
 Duda M. et al. Use of machine learning for behavioral distinction of autism and ADHD. Transl Psychiatry. 2016 Feb 9;6:e732.
Duda M, Haber N, Daniels J, & Wall DP (2017). Crowdsourced validation of a machine-learning classification system for autism and ADHD. Translational psychiatry, 7 (5) PMID: 무료 슬롯머신 게임 201928509905