Background: Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race. This study examined the relationship between race and admission service, and its effect on 30-day readmission and mortality Methods: We performed a retrospective cohort study from September 2008 to November 2017 at a single large urban academic referral center of all patients self-referred to the emergency department and admitted to either the cardiology or general medicine service with a principal diagnosis of HF, who self-identified as white, black, or Latinx. We used multivariable generalized estimating equation models to assess the relationship between race and admission to the cardiology service. We used Cox regression to assess the association between race, admission service, and 30-day readmission and mortality. Results: Among 1967 unique patients (66.7% white, 23.6% black, and 9.7% Latinx), black and Latinx patients had lower rates of admission to the cardiology service than white patients (adjusted rate ratio, 0.91; 95% CI, 0.84–0.98, for black; adjusted rate ratio, 0.83; 95% CI, 0.72–0.97 for Latinx). Female sex and age >75 years were also independently associated with lower rates of admission to the cardiology service. Admission to the cardiology service was independently associated with decreased readmission within 30 days, independent of race. Conclusions: Black and Latinx patients were less likely to be admitted to cardiology for HF care. This inequity may, in part, drive racial inequities in HF outcomes.

This paper proposes an architecture for biometric electronic identification document (e-ID) system based on Blockchain for citizens identity verification in transactions corresponding to the notary, registration, tax declaration and payment, basic health services and registration of economic activities, among others. To validate the user authentication, a biometric e-ID system is used to avoid spoofing and related attacks. Also, to validate the document a digital certificate is used with the corresponding public and private key for each citizen by using a user’s PIN. The proposed transaction validation process was implemented on a Blockchain system in order to record and verify the transactions made by all citizens registered in the electoral census, which guarantees security, integrity, scalability, traceability, and no-ambiguity. Additionally, a Blockchain network architecture is presented in a distributed and decentralized way including all the nodes of the network, database and government entities such as national register and notary offices. The results of the application of a new consensus algorithm to our Blockchain network are also presented showing mining time, memory and CPU usage when the number of transactions scales up.

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A wide range of IDS implementations with anomaly detection modules have been deployed. In general, those modules depend on intrusion knowledge databases, such as Knowledge Discovery Dataset (KDD99), Center for Applied Internet Data Analysis (CAIDA) or Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD), among others. Once the database is analyzed and a machine learning method is employed to generate detectors, some classes of new detectors are created. Thereafter, detectors are supposed to be deployed in real network environments in order to achieve detection with good results for false positives and detection rates. Since the traffic behavior is quite different according to the user's network activities over available services, restrictions and applications, it is supposed that behavioral-based detectors are not well suited to all kind of networks. This paper presents the differences of detection results between some network scenarios by applying traditional detectors that were calculated with artificial neural networks. The same detector is deployed in different scenarios to measure the efficiency or inefficiency of static training detectors.

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