Information Measures Research Articles

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields

Viral hepatitis knowledge and vaccination awareness among men who have sex with men (MSM) in 43 countries of the WHO European Region: results from the European MSM Internet Survey, EMIS-2017.

BackgroundRecent hepatitis A virus outbreaks in Europe affecting men who have sex with men (MSM) and ongoing hepatitis B virus transmission among MSM underscore the ongoing need for viral hepatitis prevention in this population.AimTo describe viral hepatitis knowledge and associated factors among MSM in the WHO European Region to inform targeted prevention.MethodsIn the European MSM Internet Survey (EMIS-2017), basic knowledge was defined as correctly identifying at least 4 of 5 statements about viral hepatitis and vaccination. We described basic knowledge by country. In a multilevel logistic regression model, we estimated adjusted odds ratios (aOR) with 95% confidence intervals (CI) for having basic knowledge and explanatory variables: sociodemographic characteristics, history of hepatitis C and/or HIV diagnosis, sexual orientation disclosure at last sexually transmitted infections (STI) test and outness.ResultsOf 113,884 participants across 43 WHO European Region countries, 68% demonstrated basic knowledge, ranging from 50% in Israel to 80% in the Netherlands. Basic knowledge was significantly associated with older age (≥ 40 years vs < 25 years, aOR: 2.9, 95% CI: 2.7-3.0), a history of hepatitis C and/or HIV diagnosis (aOR: 1.8, 95% CI: 1.7-1.9) and sexual orientation disclosure at last STI test (aOR: 1.3, 95% CI: 1.2-1.3), among other factors.ConclusionsWe found a knowledge disparity regarding viral hepatitis and hepatitis vaccination awareness among MSM across Europe, highlighting a need to address these gaps. A non-judgemental, accepting climate that allows individuals attending medical services to safely disclose their sexual orientation is fundamental to enable healthcare professionals to target information and preventative measures more effectively.

Examining the Accuracy of Self-Reported Antiretroviral Therapy Adherence Using a Visual Analog Scale Among Opioid-Dependent People Living with HIV.

Evidence indicates that regular assessment of antiretroviral therapy (ART) adherence is necessary to promote adherence and achieve viral suppression. Self-reported adherence using a visual analog scale (VAS) has been used extensively to measure ART adherence. However, less is known about the accuracy of the VAS for measuring ART adherence among opioid-dependent people living with HIV. In this study, we aimed to evaluate the accuracy of the VAS in measuring ART adherence in opioid-dependent people living with HIV who are enrolled in a methadone maintenance program (MMP). This study was conducted within a larger randomized controlled trial among opioid-dependent people living with HIV (N = 109) who were enrolled in an inner-city MMP. Self-reported VAS ART adherence data were compared to pharmacy refill data, which is a more objective measure. Self-reported VAS ratings and pharmacy refill behavior were significantly correlated at the 6-month post-intervention mark (r = 0.349, p = 0.006) but not at any other time point (i.e., pre-intervention, post-intervention, 3-months, and 9-months post-intervention). Based on the pharmacy refill data, participants did not consistently adhere to their ART medication but self-reported high adherence on the VAS. The self-report VAS may not be an accurate measure for assessing ART adherence among opioid-dependent persons living with HIV and enrolled in a MMP. Based on our findings, it may be more accurate to use pharmacy refill information or other objective measures to quantify ART adherence over extended periods for this patient population.

A generalized TODIM approach based on information measurement and consensus building model in probabilistic hesitant fuzzy environment

Information measurement and decision-making methods are two important aspects in the application of probabilistic hesitant fuzzy elements (PHFEs), and these issues are crucial for improving decision quality and enhancing the applicability of PHFSs. This paper focuses on the entropy measure and score function of PHFEs, and then proposes a generalized TOIDM method based on information measurement and consensus building. In this contribution, we develop a novel non-probabilistic entropy measure by two non-fuzzy elements and a novel score function based on sigmoid function. The novel non-probabilistic entropy of PHFEs can comprehensively reflect the dynamic changes of fuzzy uncertainty and hesitant uncertainty, and provide a new axiomatic framework. The novel score function of PHFEs can synthesize multiple information effectively with strong integrity. In the sequel, the two proposed information measurement methods are compared with existing methods to further verify the validity of the developed methods. Eventually a consensus building model is constructed based on the proposed information measurement method, and then a generalized TODIM approach based on information measurement and consensus building is proposed for probabilistic hesitant fuzzy environment. The developed approach is demonstrated with an emergency rescue planning decision-making problem. Sensitivity analysis and comparative analysis are employed to verify the validity and stability of the approach and information measures proposed in this paper. Based on the findings of this study, we conclude that the proposed methods effectively enrich the existing information measurement methods and decision-making methods in the field of probabilistic hesitant fuzzy.

Beyond the Blackwell order in dichotomies

I establish a translation invariance property of the Blackwell order for dichotomies and show that the norm of the distance from the identity matrix may be interpreted as a measure of informativeness. The better experiment is closer to the fully revealing experiment; this measure extends the Blackwell order, is complete, and prior-independent.

Diagnostic et prise en charge de l'infection congénitale à cytomégalovirus

AbstractCongenital cytomegalovirus (CMV) infection affects almost 1 % of newborns, with a risk of sequelae of 17-20 %. Primary maternal infection during the first trimester of pregnancy is the most significant risk factor for severe disease. To prevent primary maternal infection, primary prevention strategies (hygiene and information measures) have been shown to be effective but are still insufficient. Although not yet recommended by the health authorities, screening enables cases of primary maternal infection to be detected as early as possible, so that transmission can be prevented by administering valaciclovir. This strategy has proved effective, reducing maternal-fetal transmission of CMV by 70 %. Screening is based on CMV serology (IgG and IgM), supplemented by IgG avidity testing if IgM are present. It is essential that screening and initiation of treatment should be as early as possible to optimize the efficacy of this strategy. PCR on amniotic fluid, proposed in the case of a first trimester maternal primary infection, can be used to diagnose fetal infection. It is also indicated in the event of abnormalities detected on ultrasound. CMV infection in newborns can be confirmed by PCR on saliva or urine, and treatment with valaganciclovir can be initiated in cases of symptomatic congenital infection.

Early NRS Leg and Back Thresholds Predict Clinical Recovery after MIS Transforaminal Lumbar Interbody Fusion for Degenerative Spine Disease.

This retrospective study included patients who underwent primary one-level minimally invasive (MIS) transforaminal lumbar interbody fusion (TLIF) for degenerative lumbar spine conditions. To identify early predictors of failing to achieve the Oswestry Disability Index (ODI) minimum clinically important difference (MCID) one-year post-surgery. Early identification of patients at risk of failing to achieve ODI-MCID is crucial for early intervention and improved postoperative counseling. Currently, no specific thresholds guide patient follow-up for optimal recovery. The assessment included demographic information, surgical details, and patient-reported outcome measures (PROMs). PROMs were collected postoperatively at 2-, 6-, and 12-week time points, as well as at 6- and 12-months. The study included 166 patients, with 34% failing to achieve ODI-MCID at one year. Early VAS back and leg scores were found to be significant predictors of ODI-MCID achievement. The optimal thresholds identified were 2.25 for early VAS back and 4.25 for early VAS leg. A rerun regression identified the thresholds as independent predictors of ODI-MCID, with odds ratios of 0.31 for both measures. VAS back and leg score thresholds at 6-12 weeks can predict ODI-MCID achievement one year after MIS TLIF. Patients exceeding the identified thresholds may be at risk of failing ODI-MCID and should be monitored closely. Level 3.

System-wide health needs segmentation: innovating integrated care for complex needs households

Abstract Background Health needs segmentation is increasingly used in healthcare delivery and health policy planning to cater for diverse population groups and develop effective and equitable health policies. But existing models often lack a systemic approach, focusing instead on single conditions or settings. This leads to fragmentation, gaps and inefficiencies in service provision and inequities in health outcomes, particularly when addressing complexity, and specifically for children, as adversity clusters in families. Aims To develop a household health needs segmentation model to identify those households with children with high levels of complex cross-sectoral needs, to plan for integrated care services. Methods Data linkage of primary, secondary, mental, community and social care services, mortality records, residential information, and small-area multiple deprivation measures was used to design a rule-based model to identifying households with children with the most complex needs in the UK region of Cheshire and Merseyside (C&amp;M), with large variation in demography, (human) geography, and deprivation. Results Of 2,645,329 individuals in C&amp;M (97% of the registered population in 2021), 1,022,840 lived in 266,939 households with children aged 0-16: 21,527 households had complex needs. This 8% of families accounted for an estimated 34% of health and social care costs for families, £362 million in total, 42% of which spent on children in care of local authorities. Implications The model identified a specific group of families with complex service use patterns, high mental and physical comorbidity and socioeconomic vulnerabilities. These exhibit potential for improved outcomes through better integration of services and targeting of community building and family support resources. Conclusions Our model represents a significant innovation in health needs segmentation. By using whole system linked data, it provides a pragmatic way of profiling complexity for proactive care. Key messages • Complex health and social problems in children cluster in families with adults also having complex needs, so we need to transform fragmented services to support the whole family to improve outcomes. • Household level segmentation using cross-sectoral, whole system data is an innovation to identify households with complex needs with respect to their locality to improve integration of support.

Context-Dependent Criteria for Dirichlet Process in Sequential Decision-Making Problems

In models with insufficient initial information, parameter estimation can be subject to statistical uncertainty, potentially resulting in suboptimal decision-making; however, delaying implementation to gather more information can also incur costs. This paper examines an extension of information-theoretic approaches designed to address this classical dilemma, focusing on balancing the expected profits and the information needed to be obtained about all of the possible outcomes. Initially utilized in binary outcome scenarios, these methods leverage information measures to harmonize competing objectives efficiently. Building upon the foundations laid by existing research, this methodology is expanded to encompass experiments with multiple outcome categories using Dirichlet processes. The core of our approach is centered around weighted entropy measures, particularly in scenarios dictated by Dirichlet distributions, which have not been extensively explored previously. We innovatively adapt the technique initially applied to binary case to Dirichlet distributions/processes. The primary contribution of our work is the formulation of a sequential minimization strategy for the main term of an asymptotic expansion of differential entropy, which scales with sample size, for non-binary outcomes. This paper provides a theoretical grounding, extended empirical applications, and comprehensive proofs, setting a robust framework for further interdisciplinary applications of information-theoretic paradigms in sequential decision-making.

The correspondences between variance and information entropies of a particle confined by a q-deformed hyperbolic potential

The bound state solutions of the Schrödinger equation (SE) under a deformed hyperbolic potential are used in this paper to investigate the correlation between the information content, such as Shannon entropies and Fisher information, and the variance of a quantum system in both momentum and position spaces. The variance was obtained from the expectation moments in conjugated spaces and used to get the uncertainty products, Fisher information products and Shannon entropic sums for different potential parameters. These information measures were observed to vary with the potential parameters and obey their lowest bound inequalities. An increase in the Fisher information leads to a lower uncertainty and less information while decreases in the Fisher information result in higher uncertainties. We proposed a relationship between the Shannon entropies, Fisher information and the variance where an increase in the Fisher information leads to a lower Shannon entropy or a lower spread of the probability distribution and vice versa. The numerical results obtained with the proposed relations agree with the most utilized method in the literature. This current work simplifies the calculation of the Shannon entropies notably for complex potential functions.

SMCVdb: a database of experimental cellular toxicity information for drug candidate molecules.

Many drug discovery exercises fail because small molecules that are effective inhibitors of target proteins exhibit high cellular toxicity. Early and effective assessment of toxicity and pharmacokinetics is essential to accelerate the drug discovery process. Conventional methods for toxicity profiling, including in vitro and in vivo assays, are laborious and resource-intensive. In response, we introduce the Small Molecule Cell Viability Database (SMCVdb), a comprehensive resource containing toxicity data for over 24 000 compounds obtained through high-content imaging (HCI). SMCVdb seamlessly integrates chemical descriptions and molecular weight data, offering researchers a holistic platform for toxicity data aiding compound prioritization and selection based on biological and economic considerations. Data collection for SMCVdb involved a systematic approach combining HCI toxicity profiling with chemical information and quality control measures ensured data accuracy and consistency. The user-friendly web interface of SMCVdb provides multiple search and filter options, allowing users to query the database based on compound name, molecular weight range, or viability percentage. SMCVdb empowers users to access toxicity profiles, molecular weights, compound names, and chemical descriptions, facilitating the exploration of relationships between compound properties and their effects on cell viability. In summary, the database provides experimentally derived cellular toxicity information for over 24 000 drug candidate molecules to academic researchers, and pharmaceutical companies. The SMCVdb will keep growing and will prove to be a pivotal resource to expedite research in drug discovery and compound evaluation. Database URL: http://smcvdb.rcb.ac.in:4321/.

Tuning Pair and Higher‐Order Mutual Information Measures in Oscillator Systems with N$N$‐Dependent Frequencies

Tuning Pair and Higher‐Order Mutual Information Measures in Oscillator Systems with N$N$‐Dependent Frequencies

Extropy and statistical features of dual generalized order statistics’ concomitants arising from the Sarmanov family

Abstract In this study, we disclose several statistical properties of concomitants of dual generalized order statistics (DGOSs) under a generic setting from the Sarmanov family of bivariate distributions. For this family, cross-moments and joint distributions of concomitants of DGOSs are explored. These results' implications for order statistics and record values are examined. Extropy and cumulative residual extropy (CRE), two well-known information measures, are also investigated. This study offers a few beautiful symmetrical relationships that these inaccuracy measurements satisfy. In addition, based on the concomitants of DGOSs, the issue of estimating the negative cumulative residual extropy (NCRE), empirical NCRE and NCE are investigated using the empirical technique. Lastly, a set of bivariate data from the real world is examined.

Network mutual information measures for graph similarity

A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.

Information–Theoretic Analysis of Visibility Graph Properties of Extremes in Time Series Generated by a Nonlinear Langevin Equation

In this study, we examined how the nonlinearity α of the Langevin equation influences the behavior of extremes in a generated time series. The extremes, defined according to run theory, result in two types of series, run lengths and surplus magnitudes, whose complex structure was investigated using the visibility graph (VG) method. For both types of series, the information measures of the Shannon entropy measure and Fisher Information Measure were utilized for illustrating the influence of the nonlinearity α on the distribution of the degree, which is the main topological parameter describing the graph constructed by the VG method. The main finding of our study was that the Shannon entropy of the degree of the run lengths and the surplus magnitudes of the extremes is mostly influenced by the nonlinearity, which decreases with with an increase in α. This result suggests that the run lengths and surplus magnitudes of extremes are characterized by a sort of order that increases with increases in nonlinearity.

Fisher Information-Based Optimization of Mapped Fourier Grid Methods

The mapped Fourier grid method (mapped-FGM) is a simple and efficient discrete variable representation (DVR) numerical technique for solving atomic radial Schrödinger differential equations. It is set up on equidistant grid points, and the mapping, a suitable coordinate transformation to the radial variable, deals with the potential energy peculiarities that are incompatible with constant step grids. For a given constrained number of grid points, classical phase space and semiclassical arguments help in selecting the mapping function and the maximum radial extension, while the energy does not generally exhibit a variational extremization trend. In this work, optimal computational parameters and mapping quality are alternatively assessed using the extremization of (coordinate and momentum) Fisher information. A benchmark system (hydrogen atom) is employed, where energy eigenvalues and Fisher information are traced in a standard convergence procedure. High-precision energy eigenvalues exhibit a correlation with the extrema of Fisher information measures. Highly efficient mapping schemes (sometimes classically counterintuitive) also stand out with these measures. Same trends are evidenced in the solution of Dalgarno–Lewis equations, i.e., inhomogeneous counterparts of the radial Schrödinger equation occurring in perturbation theory. A detailed analysis of the results, implications on more complex single valence electron Hamiltonians, and future extensions are also included.

Limits on inferring T cell specificity from partial information

A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.

Generalized Parametric Fuzzy Information Measure

Objective: To generalize an existing fuzzy information measure and verify its important properties. Methods: Both objective and descriptive methods have been adopted; problem-solving and problem-posing methods have been used. A collection of properties presented by De Luca and Termini is used as a standard for establishing new fuzzy information measures. Findings: This study has analysed the different features of the parametric measure of fuzzy information that we have presented. The suggested fuzzy entropy measure is a legitimate measure of fuzzy information since it meets all necessary requirements. Its monotonic behaviour has also been studied. Comparisons between the proposed measure of fuzzy information and the corresponding measure of probabilistic information have been discussed. Novelty: The unique quality of the suggested fuzzy information measure is found in its monotonic behaviour corresponding to parameter α. When conducting a multicriteria decision analysis and directing the decision-making process, monotonic fuzzy information measures are more useful. Because the suggested measure satisfies fundamental set features, it can be applied in soft computing for economics, decision-making, and data aggregation. Mathematics Subject Classification: 94 D 05 Keywords: Fuzzy Entropy, Uncertainty, Fuzziness, Measures of Information, Shannon Entropy

7847 Radiomic Ultrasound Checkup of Thyroid Nodules Before Radiodiodine Therapy

Abstract Disclosure: A.A. Trukhin: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). A.V. Manaev: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). S.M. Zakharova: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). M.S. Sheremeta: None. E.A. Troshina: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). Background: Over 83 years radioiodine therapy (RIT) of autonomous thyroid nodules still have uncovered pages, according to its complexity and thyroid nodules variety. There are two main criteria to allow RIT – no malignancy and presence of iodine uptake, the last one could be checked by Tc-99m-pertachnetate or I-123, I-131 iodine scintigraphy. Case with thyroid nodules first undergo ultrasound (US) TI-RADS classification, then fine needle aspiration stratification according to the Bethesda system. Publications applied such algorithm shows 75-85% accuracy in malignancy determination, that in 15-25% can lead to improper RIT application. Aim: Method development for thyroid nodules ultrasound checkup before I-131 administration and I-131 24h uptake prognosis. Methods: Study includes 150 nodules (65 benign, 60 malignant, 25 autonomous benign thyroid nodules). Both US longitudinal and transversal thyroid nodule projections were obtained via GE Voluson E8 (36% benign cases / 27% malignant cases) and GE Logiq E (64% benign cases / 73% malignant cases) in cinematic loop regime. Autonomous thyroid nodules underwent I-131 pharmacokinetics examination with 24h uptake determination and RIT. Statistical textural features were obtained through spatial adjacency matrix. Results: Analysis of 16 statistical textural features shows weak correlation between 11 statistical textural features E (Energy), S (Entropy), LU (Local homogeneity), MP (Maximum probability), TR (Matrix trace), CORR (Correlation), IMC (information measure of correlation), DV (Difference variance), DE (Difference entropy), SE (Sum entropy), SA (Average value of the sum). ROC-analysis using 11 statistical textural features shows the best model for benign/malignance determination with AUC = 0.83, Se = 74%, Sp = 73%, PPV = 75%, NPV = 72%, Acc = 74%. I-131 pharmacokinetics examination shows strong correlation SA to I-131 24h uptake. Conclusions: The performed study demonstrates the possibility of using textural statistical features of ultrasound images as instrument for thyroid nodules ultrasound checkup before I-131 administration, for extra FNA prescription and prognosis the I-131 24h important in exact therapeutic I-131 activity calculation. Presentation: 6/2/2024

Why are information-theoretic descriptors powerful predictors of atomic and molecular polarizabilities.

We rationalize the excellent performance of information-theoretic descriptors for predicting atomic and molecular polarizabilities. It seems that descriptors which capture information about the change in valence-shell structure, especially the relative Fisher information measures, are particularly useful. Using this, we can rationalize why the G3 form of the relative Fisher information, which measures the deviation of effective nuclear charge between an atom-in-a-molecule and the reference pro-atom, is especially effective as a predictor of molecular polarizability. There are no methods used in this paper, which relies on mathematical derivation and analysis.