Leveraging real-world data to optimize pharmacotherapy outcomes in multimorbid patients by using machine learning and knowledge representation methods (LEAPfROG Study)

People with multimorbidity suffer from suboptimal pharmacotherapy due to insufficient knowledge to guide prescribing decisions. Leveraging routinely collected data in Electronic Health Records (EHRs) using machine learning (ML, a subset of Artificial intelligence), has been widely advocated to close this knowledge gap and provide missing insights on safety and effectiveness of drugs.

However, EHR data suffer from many quality issues. In addition, there are still significant limitations in the introduction of ML models to support in prescribing decisions, due to the lack of transparency in how model outputs are generated (the "black box” dilemma), and lack of logical reasoning (models not informed by causal domain knowledge).

Therefore, LEAPfROG aims for scientific breakthrough in AI application in healthcare by creating a scalable PharmacoInformatics Platform (PIL), in which a community of cross-sectoral partners will combine machine learning and knowledge representation methods to address these limitations, and jointly deliver novel AI-powered tools, methods, models, and a prototype for AI-based decision support capable to provide trustworthy and explainable causal suggestions.

The value of PIL will be demonstrated via clinically relevant and urgent use case of drug-induced kidney diseases (DIKD) in patients with chronic kidney disease (CKD). Patients with CKD are amongst the most complex and multimorbid. By providing missing insights on DIKD in patients with CKD via PIL, LEAPfROG will contribute to safer and more effective pharmacotherapy in these patients. Aiming to increase quality of life of patients with CKD and decrease healthcare costs related to medication harm.