Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

Background: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare.

Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.

Background: There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset.

Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19.

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population.

Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm.

Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface.

Laser stabilization to neutral Yb in a discharge with polarization-enhanced frequency modulation spectroscopy.

Isotope selective optical excitation of atoms is important for experiments with neutral atoms, metrology, and work with trapped ions, including quantum information processing. Polarization-enhanced absorption spectroscopy is used to frequency stabilize a tunable external cavity laser diode system at 398.9 nm for isotope selective photoionization of neutral Yb atoms.

Multichannel optomechanical switch and locking system for wavemeters.

Here we present a cost-effective multichannel optomechanical switch and software proportional-integral-derivative (PID) controller system for locking multiple lasers to a single-channel commercial wavemeter. The switch is based on a rotating cylinder that selectively transmits one laser beam at a time to the wavemeter. The wavelength is read by the computer, and an error signal is output to the lasers to correct wavelength drifts every millisecond.