AI RESEARCH
PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
arXiv CS.LG
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ArXi:2605.07267v1 Announce Type: new Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing temporal causal discovery methods are poorly matched to this setting: cohort-level models provide stable but non-personalized structures, while per-patient discovery is unreliable because individual trajectories are short, noisy, irregular, and non-stationary.