Topological Structures in the Space of Treatment-Naive Patients with Chronic Lymphocytic Leukemia.

ABSTRACT: Patients are complex and heterogeneous; clinical data sets are complicated by noise, missing data, and the presence of mixed-type data. Using such data sets requires understanding the high-dimensional "space of patients", composed of all measurements that define all relevant phenotypes. The current state-of-the-art merely defines spatial groupings of patients using cluster analyses. Our goal is to apply topological data analysis (TDA), a new unsupervised technique, to obtain a more complete understanding of patient space. We applied TDA to a space of 266 previously untreated patients with Chronic Lymphocytic Leukemia (CLL), using the "daisy" metric to compute distances between clinical records. We found clear evidence for both loops and voids in the CLL data. To interpret these structures,

SUBMITTER: McGee RL 

PROVIDER: S-EPMC11311631 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature