The arrival time and energy of FRBs traverse the time-energy bivariate space like a Brownian motion.

The origin of fast radio bursts (FRBs), the brightest cosmic explosion in radio bands, remains unknown. We introduce here a novel method for a comprehensive analysis of active FRBs' behaviors in the time-energy domain. Using "Pincus Index" and "Maximum Lyapunov Exponent", we were able to quantify the randomness and chaoticity, respectively, of the bursting events and put FRBs in the context of common transient physical phenomena, such as pulsar, earthquakes, and solar flares. In the bivariate time-energy domain, repeated FRB bursts' behaviors deviate significantly (more random, less chaotic) from pulsars, earthquakes, and solar flares. The waiting times between FRB bursts and the corresponding energy changes exhibit no correlation and remain unpredictable, suggesting that the emission of FRBs does not exhibit the time and energy clustering observed in seismic events. The pronounced stochasticity may arise from a singular source with high entropy or the combination of diverse emission mechanisms/sites. Consequently, our methodology serves as a pragmatic tool for illustrating the congruities and distinctions among diverse physical processes.

Magnetic field reversal in the turbulent environment around a repeating fast radio burst.

Fast radio bursts (FRBs) are brief, intense flashes of radio waves from unidentified extragalactic sources. Polarized FRBs originate in highly magnetized environments. We report observations of the repeating FRB 20190520B spanning 17 months, which show that the FRB's Faraday rotation is highly variable and twice changes sign. The FRB also depolarizes below radio frequencies of about 1 to 3 gigahertz. We interpret these properties as being due to changes in the parallel component of the magnetic field integrated along the line of sight, including reversing direction of the field. This could result from propagation through a turbulent magnetized screen of plasma, located 10-5 to [Formula: see text] parsecs from the FRB source. This is consistent with the bursts passing through the stellar wind of a binary companion of the FRB source.

Frequency-dependent polarization of repeating fast radio bursts-implications for their origin.

The polarization of fast radio bursts (FRBs), which are bright astronomical transient phenomena, contains information about their environments. Using wide-band observations with two telescopes, we report polarization measurements of five repeating FRBs and find a trend of lower polarization at lower frequencies. This behavior is modeled as multipath scattering, characterized by a single parameter, σRM, the rotation measure (RM) scatter. Sources with higher σRM have higher RM magnitude and scattering time scales. The two sources with the highest σRM, FRB 20121102A and FRB 20190520B, are associated with compact persistent radio sources. These properties indicate a complex environment near the repeating FRBs, such as a supernova remnant or a pulsar wind nebula, consistent with their having arisen from young stellar populations.

Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach.

BACKGROUND: Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the "gold standard" reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. OBJECTIVE: This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. METHODS: We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. RESULTS: When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve-receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. CONCLUSIONS: This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.

A reference guide to rapidly implementing an institutional dashboard for resource allocation and oversight during COVID-19 pandemic surge.

OBJECTIVES: We develop a dashboard that leverages electronic health record (EHR) data to monitor intensive care unit patient status and ventilator utilization in the setting of the COVID-19 pandemic. MATERIALS AND METHODS: Data visualization software is used to display information from critical care data mart that extracts information from the EHR. A multidisciplinary collaborative led the development. RESULTS: The dashboard displays institution-level ventilator utilization details, as well as patient-level details such as ventilator settings, organ-system specific parameters, laboratory values, and infusions. DISCUSSION: Components of the dashboard were selected to facilitate the determination of resources and simultaneous assessment of multiple patients. Abnormal values are color coded. An overall illness assessment score is tracked daily to capture illness severity over time. CONCLUSION: This reference guide shares the architecture and sample reusable code to implement a robust, flexible, and scalable dashboard for monitoring ventilator utilization and illness severity in intensive care unit ventilated patients.