Using Machine Learning to Improve CNS Research Outcomes

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Many studies investigating new treatments for neurological indications suffer from decreased drug-placebo separation as a result of unreliable data quality.

In this article explaining the results of original research, a team of Signant’s clinical data scientists analyzed data from 17 acute schizophrenia trials. They demonstrate how advanced data analysis tools such as machine learning and smart algorithms can be applied to identify data quality concerns.

Revealing quality indicators such as unwanted rater- and site-level variability enables clinicians to intervene to maintain the integrity of the trial.

Read the article to get expert recommendations about developing machine learning models and intervening action plans to improve signal detection.

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