Machine learning identifies chronic disease subtypes using BMI trajectories

Researchers have developed a machine-learning approach to identify distinct patient subtypes based on their Body Mass Index (BMI) trajectories over time. Utilizing electronic health records from approximately two million individuals, the study defined nine new variables derived from BMI patterns to cluster patients. This method re-established known links between obesity and diseases like diabetes and hypertension, while also uncovering specific patient subgroups with unique characteristics for various chronic conditions. AI

IMPACT This research could lead to more personalized treatment strategies for chronic diseases by identifying distinct patient subgroups based on their health data.

RANK_REASON The cluster contains an academic paper detailing a machine learning approach to patient subtyping. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Md Mozaharul Mottalib

• paper
• other

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