Papers for
personalized medicine teams
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Probabilities of causation bounds unified across all dimensions
Unified Sharp Bounds on Probabilities of Causation
Abstract: Probabilities of causation (PoCs) have developed steadily, from the binary measures first introduced by Pearl to the non-binary setting recently studied by Shu et al. As the theory has matured, PoCs have found applications in a growing range of areas, including personalized medicine, marketing and legal attribution. Shu et al. proposed closed-form bounds for multi-valued treatments and outcomes and proved them valid, but left their sharpness in high dimensions open. We close this gap with a mathematical proof. We further show that $\PNS$, $\PSub$, $\PRep$ and $\PN$ are all special cases of a single, more general formula. Combined with their equivalence classes, this settles the question of sharp bounds for discrete PoCs from experimental and observational data: in every dimension, they are given in closed form by a single theorem.
Human menstrual cycle hormone dynamics captured with efficient model
A Mechanistic Model of the Human Menstrual Cycle
Abstract: The human menstrual cycle is regulated by complex hormonal feedback mechanisms which are essential for reproductive health. Existing mathematical models either rely on simplified phenomenological assumptions or achieve physiological accuracy at the cost of high mathematical complexity and computational costs. This paper presents a novel mechanistic model which balances biological realism with mathematical simplicity. The proposed framework describes the dynamics of the four central cycle-driving hormones, namely follicle stimulating hormone, luteinizing hormone, estradiol, and progesterone, using a system of ordinary differential equations. Mechanistic detail is incorporated through representations of feedback mechanisms, follicular development, luteinization, vascularization of the corpus luteum, and aspects of hormone synthesis mechanisms. Despite its comparatively low complexity, the model reproduces characteristic trends of hormone activity and concentration dynamics reported in experimental data. Due to its computational efficiency and modularity, the model is well suited as a basis for extended in-silico experiments, including applications in pharmacology and personalized medicine. Our results demonstrate that targeted incorporation of biological mechanisms enables accurate yet tractable modeling of endocrine system dynamics.