Previously, I analyzed clinical data review platforms. This blog explains how artificial intelligence assists search, confidence scores, and data reviews.
Data managers and reviewers log in to clinical data review platforms (CDRP), and slice and dice the data they want in order to review for missing, wrong, or inaccurate data. If they can search using natural language, they can spend more time on reviewing the data rather than creating complex search criteria. For example, users can write:
- “Show me all demography data where subjects are males, but pregnancy is yes.”
- “Show me all data from adverse events and concomitant medications, highlighting concomitant medications without corresponding adverse events.”
- “Show me all the severe adverse events reported for the third visit.”
NLP/NLQ (natural language querying) converts these texts to search criteria, which is then converted to an SQL query. The query is executed, and the results are returned in figure 1 (below). When ML algorithms understand clinical data, they can execute the search criterion and deliver the results. Information extraction with language translation (e.g., English to ML) can be used.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
Extending this would include allowing audio input from users, converting audio to text, and then using NLQ to convert the text to corresponding queries. For non-English speaking users, NLP language translators can be used to achieve the same results.
Data Review Prioritization
When clinical data reviewers, medical monitors, statisticians, and safety reviewers are reviewing data in the CDRP, an ML model can analyze the data and apply statistical analysis to determine the probability of whether the data is clean or not. In addition, this model can be used to detect anomalies in the data. Users should be presented with data based on the probability that the data is clean or the data points that require their attention.
The data reviewers can prioritize their review activity based on the input from the ML models. It will be valuable for the users and will significantly reduce the time taken for data review. Users can set the threshold for the data points they want to review first, based on the statistical analysis done by the ML model.
To learn more about how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process, you can download the guide here or submit the form below.