Boltzmann-Shannon interaction entropy: A normalized measure for continuous variables with an application as a subsample quality metric.

The recent introduction of geometric partition entropy offered an alternative to differential Shannon entropy for the quantification of uncertainty as estimated from a sample drawn from a one-dimensional bounded continuous probability distribution. In addition to being a fresh perspective for the basis of continuous information theory, this new approach provided several improvements over traditional entropy estimators including its effectiveness on sparse samples and a proper incorporation of the impact from extreme outliers. However, a complimentary relationship exists between the new geometric approach and the basic form of its frequency-based predecessor that is leveraged here to define an entropy measure with no bias toward the sample size.

Geometric Partition Entropy: Coarse-Graining a Continuous State Space.

Entropy is re-examined as a quantification of ignorance in the predictability of a one dimensional continuous phenomenon. Although traditional estimators for entropy have been widely utilized in this context, we show that both the thermodynamic and Shannon's theory of entropy are fundamentally discrete, and that the limiting process used to define differential entropy suffers from similar problems to those encountered in thermodynamics. In contrast, we consider a sampled data set to be observations of microstates (unmeasurable in thermodynamics and nonexistent in Shannon's discrete theory), meaning, in this context, it is the macrostates of the underlying phenomenon that are unknown.

Data-driven learning of Boolean networks and functions by optimal causation entropy principle.

Boolean functions, and networks thereof, are useful for analysis of complex data systems, including from biological systems, bioinformatics, decision making, medical fields, and finance. However, automated learning of a Boolean networked function, from data, is a challenging task due in part to the large number of unknown structures of the network and the underlying functions. In this paper, we develop a new information theoretic methodology, called Boolean optimal causation entropy, that we show is significantly more efficient than previous approaches.

Association of periodontal therapy, with inflammatory biomarkers and complications in COVID-19 patients: a case control study.

Background: In previous studies, COVID-19 complications were reported to be associated with periodontitis. Accordingly, this study was designed to test the hypothesis that a history of periodontal therapy could be associated with lower risk of COVID-19 complications.

Methods: A case-control study was performed using the medical health records of COVID-19 patients in the State of Qatar between March 2020 and February 2021 and dental records between January 2017 and December 2021.

The Value of Auditing Surgical Records in a Tertiary Hospital Setting.

Background: Assessing and improving quality of care should be of paramount importance to health care systems and providers. This study aimed to evaluate the quality of surgical records at the Jordan University Hospital.

Methods: We used the previously validated Surgical Tool for Auditing Records (STAR) to retrospectively evaluate the quality of surgical records of patients who underwent surgery in the general surgery department from 2016 to 2021.