An ensemble approach for supporting the respiratory isolation of presumed tuberculosis inpatients

Tuberculosis remains a global health challenge, especially in low and middle-income countries. New diagnostic tools can allow earlier diagnosis, reducing both the mortality and transmission in the community. In hospitals, the decision making relative to the allocation of presumed pulmonary tuberculosis inpatients in airborne rooms is critical, since no standard criterion has been established. In this paper, we propose a novel technique for developing a committee of classifiers aiming at supporting the decision making relative to inpatient respiratory isolation. The proposed approach is agnostic on the classification model adopted, exploiting tailored strategies for optimally integrating a small and diverse set of compact classifiers, resulting in highly accurate committees. The results confirm that the resulting committees have outperformed several recently proposed single-models and ensemble solutions, including deep learning techniques. As a practical benefit, the adoption of such decision support tool can reduce in almost one half the percentage of inpatients unnecessarily isolated at a university hospital. (C) 2018 Elsevier B.V. All rights reserved.