Applying ML to clinical trials and PV
The clinical trial process is mostly sequential and generates large volumes of data in different modalities. These datasets need to be analyzed and processed for information retrieval. The information embedded in these data assets is critical to the success of the overall trial. The information from a previous step might inform how future steps need to be carried out. Moreover, regulatory requirements emphasize strict timelines and governance regarding how you process this information. ML can help automate the repeatable steps in a clinical trial process and help make the trial more efficient. Additionally, it can help with information discovery to better inform regulators and policymakers about future trials. Now, let us look at some common ways in which ML can be applied to clinical trials and PV.
Literature search and protocol design
During the study design phase, scientists need to search through a variety of literature from previous...
