Background Unplanned readmission of a hospitalized patient is an indicator of patients’ exposure to risk and an avoidable waste of medical resources. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks. Identification of high-risk patients who are likely to be readmitted can provide significant benefits for both patients and medical providers. The emergence of machine learning solutions to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities for developing an efficient discharge decision-making support system for physicians and ICU specialists. Methods and findings We used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We incorporate multiple types of features including chart events, demographic, and ICD-9 embeddings. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able to incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate). We show that our LSTM-based solution can better capture high volatility and unstable status in ICU patients, an important factor in ICU readmission. Our machine learning models identify ICU readmissions at a higher sensitivity rate of 0.742 (95% CI, 0.718–0.766) and an improved Area Under the Curve of 0.791 (95% CI, 0.782–0.800) compared with traditional methods. We perform in-depth deep learning performance analysis, as well as the analysis of each feature contribution to the predictive model. Conclusion Our manuscript highlights the ability of machine learning models to improve our ICU decision-making accuracy and is a real-world example of precision medicine in hospitals. These data-driven solutions hold the potential for substantial clinical impact by augmenting clinical decision-making for physicians and ICU specialists. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.

Summary It is increasingly apparent that bacteriophages, viruses that infect bacteria and more commonly referred to as simply phages, have tropisms outside their bacterial hosts. Using live tissue culture cell imaging, we demonstrate that cell type, phage size, and morphology play a major role in phage internalization. Uptake was validated under physiological conditions using a microfluidic device. Phages adhered to mammalian tissues, with adherent phages being subsequently internalized by macropinocytosis, with functional phages accumulating intracellularly. We incorporated these results into a pharmacokinetic model demonstrating the potential impact of phage accumulation by cell layers, which represents a potential sink for circulating phages in the body. During phage therapy, high doses of phages are directly administered to a patient in order to treat a bacterial infection, thereby facilitating broad interactions between phages and mammalian cells. Understanding these interactions will have important implications on innate immune responses, phage pharmacokinetics, and the efficacy of phage therapy.

A respiratory infection caused by antibiotic-resistant bacteria can be life-threatening. In recent years, there has been tremendous effort put towards therapeutic application of bacteriophages (phages) as an alternative or supplementary treatment option over conventional antibiotics. Phages are natural parasitic viruses of bacteria that can kill the bacterial host, including antibiotic-resistant bacteria. Inhaled phage therapy involves the development of stable phage formulations suitable for inhalation delivery followed by preclinical and clinical studies for assessment of efficacy, pharmacokinetics and safety. We presented an overview of recent advances in phage formulation for inhalation delivery and their efficacy in acute and chronic rodent respiratory infection models. We have reviewed and presented on the prospects of inhaled phage therapy as a complementary treatment option with current antibiotics and as a preventative means. Inhaled phage therapy has the potential to transform the prevention and treatment of bacterial respiratory infections, including those caused by antibiotic-resistant bacteria.

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