Source: Emerj | February 19, 2019
Author: Niccolo Mejia
It may feel as though AI applications like machine vision and natural language processing hold the most potential value to pharmaceutical companies because of their capabilities to intake and transform unstructured medical data. This is especially true with machine vision, as medical imaging data can be used across multiple departments when analyzed by AI software.
Despite this, there is still a relatively equal amount of use cases for predictive analytics software that requires more prepared and structured pharmaceutical data. Leaders designing clinical trials may benefit from a predictive analytics tool that can compare their plans with the operations and procedures of past trials. A user may also be able to search for past trials based on the type of drug being tested and find which practices led the leading drugs to success.
The same is true for the discovery of new molecules and compounds that can be used to produce new drugs. Marketing teams may also benefit from predictive analytics solutions in that they can determine where demand for certain drugs may rise across different geographical locations. They may also be able to use the software to compare marketing campaigns for new drugs, strategies, and sales techniques with the current practices.
We spoke to Daniela Braga, founder, and CEO of DefinedCrowd, a data enrichment and crowdsourcing company, about how business leaders can determine their data needs for new AI projects. For pharmaceutical companies, this would be especially important for predictive analytics solutions as finding the correct data set amongst large stores of legacy data can be difficult.
With regards to cleaning and preparing data for use in machine learning models and data science experiments, Braga said:
So you need to collect data, you need to annotate data. There is a lot involved in entity tagging. It is one of the most difficult tasks especially, in domains. You need sometimes domain-specialized people to annotate those entities. And there is a whole way around measuring the quality of the people because we combine people with machines to make our data processing more efficient and accurate.
We caution business leaders looking to implement predictive analytics software to be aware that cleaning and structuring their data for processing by a machine learning model may be necessary.
This report covers possible use cases for applying predictive analytics to the following business problems facing pharmaceutical companies:
- Clinical Trial Design and Optimization
- Drug Discovery
Interested readers may want to read our full reports on AI in the European pharma industry and AI in the Asian pharma industry.
Our report begins with the uses of predictive analytics for the design and optimization of clinical trials, and how business leaders can gain a better understanding of past trials for similar drugs to the one they are looking to test.