Abstract
Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.
Highlights
Healthcare-associated infections (HAI) are one of the major problems of health systems in many countries due to their direct impact on morbidity, mortality, length of hospital stays, and costs [1].According to a CDC (Centers for Disease Control and Prevention) report, the estimated overall annual direct medical cost of HAI in U.S hospitals ranges between $28.4 and $45 billion, and the deaths they cause amount to more than 98,000
In order to fill this gap, we propose an approach for obtaining reliable HAI predictive models from imbalanced datasets, which allow clinicians to automatically detect the patients most susceptible to infections
The clustering-based random undersampling method, proposed in this work to address the problem of imbalanced data classification, was validated through an experimental study conducted
Summary
Healthcare-associated infections (HAI) are one of the major problems of health systems in many countries due to their direct impact on morbidity, mortality, length of hospital stays, and costs [1]. According to a CDC (Centers for Disease Control and Prevention) report, the estimated overall annual direct medical cost of HAI in U.S hospitals ranges between $28.4 and $45 billion, and the deaths they cause amount to more than 98,000. It is estimated that 20% of infections are preventable and the financial benefits of prevention in U.S hospitals range from $5.7 to $31.5 billion. Another study has found that the benefits of mortality risk reductions are at least 5 times greater than the benefits of only reducing direct medical costs that emerge in hospitals [2]. A significant percentage of HAIs occur in intensive-care units (ICUs), where patients are usually more susceptible to acquiring infections, which results in higher mortality rates or longer stays [3]
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