Automated Decision Support and Guideline Verification in Clinical Practice

Applying international guidelines in medical, including cardiological, therapies is a guarantee of safe and modern treatment. Unfortunately, standards are often not obeyed. In this paper we present an experimental software program based on rough sets methods. The main aim of this application is to improve patient care and help the decision process using guidelines verification. We concentrate on the practical aspects using these methods. Examples and clinical tests, which were based on real-life data of our patients, show that the accuracy of results reached on a large group of patients could be acceptable in clinical practice. 1

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one-week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country’s sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.info:eu-repo/semantics/publishedVersio

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Processing, Microstructure and Dielectric Properties of the Bi 5

The aim of the present work is the analysis of microstructure, dielectric permittivity and thermal properties analysis of $Bi_5Ti_3FeO_{15}$ ceramics obtained by two methods. The studied $Bi_5Ti_3FeO_{15}$ ceramics were prepared by conventional synthesis and hot uniaxial pressing reaction from the conventional mixture of oxides, viz. $TiO_2$, $Fe_2O_3$, $Bi_2O_3$. The studied material has layered perovskite like structures, first described by Aurivillius in 1949 and Subbaro in 1969. The ceramic $Bi_5Ti_3FeO_{15}$ is known to contain a series of compounds with the general formula: $Bi_{m+1}Fe_{m-3}Ti_3O_{3m+3}$. The X-ray diffraction methods were used for qualitative phase analysis of studied samples. The morphology was analyzed by scanning electron microscopy method. The thermal properties of the studied materials were measured using the differential thermal analysis at a constant heating rate of 15 K/min under an argon protective atmosphere. Thermal dependence of dielectric permittivity was studied between room temperature and 1137 K

Processing, Microstructure and Dielectric Properties of the Bi5Ti3FeO15Bi_5Ti_3FeO_{15} Ceramic

The aim of the present work is the analysis of microstructure, dielectric permittivity and thermal properties analysis of $Bi_5Ti_3FeO_{15}$ ceramics obtained by two methods. The studied $Bi_5Ti_3FeO_{15}$ ceramics were prepared by conventional synthesis and hot uniaxial pressing reaction from the conventional mixture of oxides, viz. $TiO_2$, $Fe_2O_3$, $Bi_2O_3$. The studied material has layered perovskite like structures, first described by Aurivillius in 1949 and Subbaro in 1969. The ceramic $Bi_5Ti_3FeO_{15}$ is known to contain a series of compounds with the general formula: $Bi_{m+1}Fe_{m-3}Ti_3O_{3m+3}$. The X-ray diffraction methods were used for qualitative phase analysis of studied samples. The morphology was analyzed by scanning electron microscopy method. The thermal properties of the studied materials were measured using the differential thermal analysis at a constant heating rate of 15 K/min under an argon protective atmosphere. Thermal dependence of dielectric permittivity was studied between room temperature and 1137 K

Mössbauer Spectroscopy, X-Ray Diffraction and SEM Studies on Multiferroic Bi5Ti3FeO15Bi_5Ti_3FeO_{15} Ceramics

Magnetoelectric multiferroics are materials which exhibit both magnetic order and ferroelectricity in the same phase. Multiferroic materials, where ferroelectricity and magnetism coexist, were extensively studied. This class of materials offers a large application potential for new devices due to the two coupled degrees of freedom based on the local off-centered distortion and the electron spin. The studied $Bi_5Ti_3FeO_{15}$ ceramics was prepared by conventional synthesis and hot uniaxial pressing reaction applying the conventional mixture of $TiO_2$, $Fe_2O_3$ and $Bi_2O_3$ oxides as precursor materials. The present work focuses on the structure analysis of multiferroic $Bi_5Ti_3FeO_{15}$ ceramics performed by X-ray diffraction method, scanning electron microscopy and the Mössbauer spectroscopy methods