Using Artificial Intelligence (AI) to Understand Chronic Kidney Disease
Todd Brothers uses AI to uncover patterns in kidney disease that could transform care
Chronic kidney disease (CKD) affects millions of people worldwide and is a major contributor to cardiovascular complications, hospitalization and premature death. Yet the disease often develops gradually and presents differently across patients, making it difficult to predict who will experience rapid progression and who will remain stable. Understanding these differences is essential for improving treatment strategies and patient outcomes.
At the University of Rhode Island College of Pharmacy, Todd Brothers, Pharm.D., BCCCP, BCPS, clinical associate professor of pharmacy practice and clinical research and a critical care pharmacy expert, applies AI and machine learning to better understand this complex disease. His research focuses on developing computational tools that analyze large healthcare datasets to identify patterns that may not be apparent using traditional approaches.
One of Brothers’ recent projects involves developing a machine learning framework to identify distinct phenotypes within chronic kidney disease populations. These phenotypes represent subgroups of patients with shared clinical characteristics, disease progression patterns and therapeutic responses. Despite classification based on kidney function, CKD exhibits significant variability in complications and outcomes. Machine learning enables the analysis of large, complex datasets to uncover patterns that may improve risk stratification and guide more targeted treatment strategies.
Using electronic health record data, Brothers and his collaborators applied clustering algorithms and statistical modeling techniques to identify subgroups of patients with shared clinical characteristics. These models analyze a wide range of patient data, including laboratory values, medical history, medications and coexisting conditions. By comparing multiple machine learning approaches, the framework improves the reliability of the phenotypes identified through the analysis.
The results highlight how chronic kidney disease is closely connected with other health conditions, particularly cardiovascular disease. The research also revealed the role that acute kidney injury can play in accelerating long-term disease progression. Identifying these patterns helps clinicians better understand how kidney disease develops and which patients may be at higher risk of complications.
Brothers’ work reflects a growing movement toward precision medicine, where treatment decisions are informed by deeper insights into individual patient characteristics. Machine learning models have the potential to help clinicians predict disease progression, tailor medication strategies and design more patient-specific interventions.
As health care systems generate increasing volumes of clinical data, AI is becoming essential for transforming this information into meaningful clinical insight. By integrating clinical pharmacy expertise with advanced computational methods, Brothers’ research is advancing new approaches to risk identification, treatment optimization and ultimately improving care for patients with chronic kidney disease.
“By integrating clinical pharmacy expertise with advanced computational methods, Brothers’ research is advancing new approaches to risk identification, treatment optimization and ultimately improving care for patients with chronic kidney disease.”Todd Brothers, Pharm.D.
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