A Hyperparameter-Free, Fast and Efficient Framework to Detect Clusters From Limited Samples Based on Ultra High-Dimensional Features.

Clustering is a challenging problem in machine learning in which one attempts to group objects into groups based on features measured on each object. In this article, we examine the case where ≪ and is not known. Clustering in such high dimensional, small sample size settings has numerous applications in biology, medicine, the social sciences, clinical trials, and other scientific and experimental fields.

Raised Venous Lactate and Markers of Intestinal Translocation Are Associated With Mortality Among In-Patients With HIV-Associated TB in Rural South Africa.

Introduction: Case fatality among in-patients with HIV-associated tuberculosis (HIV-TB) in Africa is high. We investigated the factors associated with mortality in a rural South African hospital.

Methods: This was a prospective observational study of HIV-TB in-patients, with death by 8 weeks the endpoint.