Concurrent Adherence To Multiple Chronic Disease Medications: Examining The Behavior And Issues Concerning Its Measurement

Objectives the objectives were to 1) examine adherence to multiple medications prescribed for a chronic disease (intra-disease multiple medication adherence) and that of multiple chronic diseases (inter-disease multiple medication adherence); 2) determine appropriate measurement paradigm from different intra-disease multiple medication adherence measurement approaches; 3) identify optimal cut-point for a dichotomized composite measure. Methods a retrospective study design was used. The subjects came from the marketscanâ® commercial claims and encounters data 2002-2003 and filled both sulfonylurea (su) and thiazolidinedione (tzd). Adherence was measured by proportion of days covered (pdc) over each period of 30 or 90 days and cumulatively. Random components from multivariate multilevel models were analyzed to examine multiple medication adherence relationships, including associations of evolutions of adherence. Survival analysis was performed on any-cause or diabetes-related emergency services (er) utilization. Concordance statistics were computed to compare different measurement approaches. Results intra-disease multiple medication analysis demonstrated strong and significant (p\u3c0.05) relationships between overall adherence estimates for su and tzd and changes in adherence estimates over time. Patients who were receiving lipid or hypertension medications, or both in addition to su and tzd shostrong and significant (p\u3c0.05) relationships between overall adherence to cross-disease medications or cross-disease adherence slope estimates. However, such results were not observed in diabetic subjects who were prescribed nitrates for angina. Each of six composite measures of intra-disease multiple medication adherence significantly predicted hazard (hazard ratio \u3c1.0) of all-cause or any diabetes-related er utilization. Although each concordance statistic was significant (p\u3c0.05), there were no differences among concordance statistics produced by these measurement approaches. The average and all approach shosome superiority. The optimality of cut-point for categorizing adherence based on a composite measure of intra-disease multiple medication adherence ranged from 75-85%. Conclusion the study population demonstrated good but not optimal levels of adherence to multiple chronic disease medications. Factors that affect adherence to individual medications appear to be related and should be targeted for intervention. Efficacy of a composite measure of intra-disease multiple medications may depend on intervention goals. Further research needs to identify a composite measurement approach that demonstrates superiority in predictive and discriminatory power consistently

Willingness to Influence Indication-Based Off-Label Prescribing: an Investigation of Hospital Pharmacists

Off-label prescribing is a comand legal practice in the United States. However, off-label prescribing occurs oftentimes with inadequate evidence of effectiveness. Such practice, especially when prescribing for disease conditions different from approved clinical indications (indication-based off-label prescribing), brings about controversy and raises different issues with various stakeholders. Although indication-based off-label prescribing offers advantages in terms of providing innovative therapy, it raises concerns because the safety and efficacy of such use may not be evaluated adequately. Thus, the objective of the study was to examine whether pharmacists are willing to influence physicians while evaluating an indication-based off-label medication order. Based on the extended social power typology originally proposed by French and raven (1965), the study examined the role of relative expert power, perceived appropriateness, and perceived negative relational consequences on pharmacist\u27s willingness to influence using rationality tactic. Pharmacists practicing in hospitals were recruited from the membership rolls of state affiliates of the American Society of Health-System Pharmacists (ASHP). The state affiliates were requested to distribute to their members an invitation to participate that contained a link to the survey instrument. The study employed a 2 x 2 experimental design in which relative expert power and appropriateness were manipulated using a hypothetical vignette. Respondents who reported practicing in a hospital were randomly assigned to one of the four experimental groups. Responses from 267 pharmacists were available for analysis. After consistency inspection, 242 pharmacist respondents were included in the analysis to examine the various propositions. Results of the analysis shothat, in general, pharmacists were willing to influence physicians to ensure rationality of indication-based off-label prescribing. Although small in magnitude, pharmacists did express concern about negative impact on inter-professional relationship quality that might arise due to influence attempts. Indeed, the effect of perceived expert power differential between the physician and pharmacist and the effect of perceived appropriateness of the off-label medication order on willingness to influence were strongly (p\u3c0.05) moderated by perceived impact on relationship quality. In addition, the perceived expert power differential was associated with pharmacists\u27 willingness to influence. Pharmacists\u27 willingness to influence increased as perceived appropriateness decreased

Theoretical quantification of shape distortion in fuzzy hough transform

We present a generalization of classical Hough transform in fuzzy set theoretic framework (called fuzzy Hough transform or FHT) in order to handle the impreciseness/ill-definedness in shape description. In addition to identifying the shapes, the methodology can quantify the amount of distortion present in each shape by suitably characterizing the parametric space. We extended FHT to take care of gray level images (gray FHT) in order to handle the gray level variation along with shape distortion. The gray FHT gives rise to a scheme for image segmentation based on the a priori knowledge about the shapes

Topological phase diagram and saddle point singularity in a tunable topological crystalline insulator

We report the evolution of the surface electronic structure and surface material properties of a topological crystalline insulator (TCI) Pb1-xSnxSe as a function of various material parameters including composition x, temperature T and crystal structure. Our spectroscopic data demonstrate the electronic groundstate condition for the saddle point singularity, the tunability of surface chemical potential, and the surface states' response to circularly polarized light. Our results show that each material parameter can tune the system between trivial and topological phase in a distinct way unlike as seen in Bi2Se3 and related compounds, leading to a rich and unique topological phase diagram. Our systematic studies of the TCI Pb1-xSnxSe are valuable materials guide to realize new topological phenomena.Comment: 10 pages, 7 figures. Expanded version of arXiv:1403.156

Fuzzy feature evaluation index and connectionist realization

A new feature evaluation index based on fuzzy set theory and a connectionist model for its evaluation are provided. A concept of flexible membership function incorporating weighting factors, is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. The overall importance of the features is evaluated both individually and in a group considering their dependence as well as independence. Effectiveness of the algorithms along with comparison is demonstrated on speech and Iris data

Unsupervised feature extraction using neuro-fuzzy approach

The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction under unsupervised training. A fuzzy feature evaluation index for a set of features is newly defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space that is obtained by a set of linear transformation on the original space. A layered network is designed for performing the task of minimization of the evaluation index through unsupervised learning process. This extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'<n) transformed space, along with their relative importance. The extracted features are found to provide better classification performance than the original ones for different real life data with dimensions 3, 4, 9, 18 and 34. The superiority of the method over principal component analysis network, nonlinear discriminant analysis network and Kohonen self-organizing feature map is also established

Transformer based Multitask Learning for Image Captioning and Object Detection

In several real-world scenarios like autonomous navigation and mobility, to obtain a better visual understanding of the surroundings, image captioning and object detection play a crucial role. This work introduces a novel multitask learning framework that combines image captioning and object detection into a joint model. We propose TICOD, Transformer-based Image Captioning and Object detection model for jointly training both tasks by combining the losses obtained from image captioning and object detection networks. By leveraging joint training, the model benefits from the complementary information shared between the two tasks, leading to improved performance for image captioning. Our approach utilizes a transformer-based architecture that enables end-to-end network integration for image captioning and object detection and performs both tasks jointly. We evaluate the effectiveness of our approach through comprehensive experiments on the MS-COCO dataset. Our model outperforms the baselines from image captioning literature by achieving a 3.65% improvement in BERTScore.Comment: Accepted at PAKDD 202

協同的コロニーの軌道データによる先導・従エージェントとそれらの相互作⽤領域の同定に関する研究

Systems composed of locally interacting particles or agents such as birds, fish, and cells show spontaneous, spatiotemporal, collective behaviors as a whole. Exploration of the underlying mechanisms and principles of such coordination of agents has been made in experimental, and theoretical studies. In many systems, it has been proved that the presence of influential individuals, known as `leaders', are responsible for the collective motion of agents. These leaders control the movement of the whole community. In some cases, e.g., fish shoal, MDCK epithelial cells, the relative position of the agents helps to identify the leader. But in many cases where agents do not move in the same direction, e.g., Dictyostelium Discoideum, the relative position does not help to identify leaders. Hence identifying leader agents in a collectively moving community is a perplexing work. Since the follower's movement is regulated by the leader agents, hence there is a correlation between the movement of leader and follower agents at a certain time lag. Consequently, cross-correlation has been used in identifying leader and follower agents. But its performance is questionable in non-linear systems. Transfer entropy, an information-theoretic measure, is capable of capturing non-linear interaction between agents, hence it has been used to identify leader agents. The effectiveness of TE in identifying leader agents has been tested in a Dictyostelium discoideum colony. It has been found that the result obtained using TE is almost identical to the expert's result. The Vicsek model (VM) often studied as a metaphor for collectively moving animals is employed. A modified version of the VM has helped us to investigate the classification performance of CC and TE. It has been found that TE outperforms CC. Different model parameters have been varied to check their effect on classification scores. An information-theoretic scheme is proposed to estimate the underlying domain of interactions and the timescale of the interactions for many-particle systems. Based on ensemble data of trajectories of the model system, it is shown that using the interaction domain significantly improves the performance of classification of leaders and followers compared to the approach without utilizing knowledge of the domain. Given an interaction timescale estimated from an ensemble of trajectories, the first derivative of transfer entropy averaged over the ensemble with respect to the cut-off distance is presented to serve as an indicator to infer the interaction domain. It is shown transfer entropy is superior to infer the interaction radius compared to cross-correlation, hence resulting in a higher performance to infer leader-follower relationship. Effects of noise size exerted from the environment, and the ratio of the numbers of leader and follower on the classification performance is also discussed. Later it was found that the `minimum derivative' scheme is dependent on how transfer entropy can be estimated so that it takes into account enough statistics of interacting particles, and positions and numbers of the minimum of the derivative of average transfer entropy along with the cutoff distance λ may also be subject to the extent of external noise and time length of trajectories. The author has scrutinized how the prediction performance in capturing the underlying interaction domain depends on the size of noise and time length of the trajectory data. An alternative scheme has been proposed which is expected to be stable against noises and time length, that relies on the degree of convexity at coarse-grained scale in the derivative of average transfer entropy along with the cutoff distance, and time variance of underlying interaction radius of particles.担当：理学部図書