| dc.description.abstract | Sepsis is a life-threatening condition that requires timely and accurate diagnosis
to improve patient outcomes and resource allocation. While predictive models for
sepsis risk exist, they often lack interpretability, limiting their clinical utility. This
study investigates the use of subgroup discovery, specifically through a beam search
algorithm, to uncover interpretable patterns associated with elevated sepsis risk in
emergency department patients. Using a real-world dataset from the St. Antonius
Hospital comprising over 27,000 entries and 59 clinical features, beam search was
applied across 324 parameter configurations to identify meaningful subgroups. Key
parameters such as Beam Width, Depth, Bins, and the StandardQF quality function
were varied to explore their impact on subgroup characteristics.
Results demonstrate a clear trade-off between subgroup coverage and precision de-
pending on the minimum number of required subgroups, with configurations requir-
ing more subgroups yielding higher lift values but lower patient coverage. Clinically
relevant variables such as CRP, bilirubin, creatinine, and missingness patterns con-
sistently emerged in top-performing subgroups. Despite computational limitations
that restricted comparisons with exhaustive search methods, this study illustrates
the potential of subgroup discovery to provide actionable and interpretable insights
for sepsis risk stratification in clinical practice. | |
