Machine Learning Tool Development

Machine learning (ML) can identify statistical patterns from generated data to train computers to perform tasks intended to aid in human decision making. Emerging use of ML is occurring in clinical trial evaluation and clinical trial recruitment, a field in which improvement in reaching and recruiting racial and ethnic minorities is increasingly essential.

For individuals from groups who are recipients of societal biases, utilisation of ML can lead to the creation and use of biased algorithms. The design of equitable ML tools that advance health equity could be guided by community engagement processes which leverage collective knowledge and experience to inform clinical trial development and design.

The primary input data for the ML algorithm will be the results of the facilitators and barriers model created from the group-based model activity and analysis of collected qualitative and survey data. Initial survey responses will be aggregated thematically across multiple responses and Likert scales to create ordinal scales to use supervised ML approaches.

This will include regression models and decision trees to identify patterns in the data. Supervised ML approaches are useful in identifying patterns where we have labelled and structured data. Meanwhile, coded responses from free form assessments such as focus groups will be analysed using unsupervised ML approaches such as hierarchical and non-hierarchical clustering of responses/participants.

Unsupervised approaches can highlight and detect previously undetected patterns in data that provide insights into the perspectives of study participants. For example, each model will have an identified central latent variable that is directly and indirectly related to measurable or observable factors (second level or third level variables) for a clinical trial.