Individual Rules Research Articles

BioPred: An R package for biomarkers analysis in precision medicine.

The R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules (ITR) to streamline subgroup identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient population in drug development. The package is available at CRAN and https://github.com/deeplearner0731/BioPred. Supplementary data are available at Bioinformatics online.

A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes

BackgroundPrecision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.MethodsTo address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the “borrowing of information” across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.ResultsWe demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.ConclusionThe study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Cross-border planning for Brazilian families

Abstract This article aims to provide an overview of cross-border planning for Brazilian Families, addressing key topics to be taken into consideration, especially those related to marital asset regimes, forced heirship rights, private, corporate, tax and international laws, from a Brazilian perspective. It also addresses the significant impacts of the 2023 Brazilian tax reform, encompassing the taxation of investment funds in Brazil, income derived by individuals resident in the country, controlled entities and trusts abroad, the introduction of CFC rules for individuals and further anti-deferral rules.

Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil.

The efficient recognition of symptoms in viral infections holds promise for swift and precise diagnosis, thus mitigating health implications and the potential recurrence of infections. COVID-19 presents unique challenges due to various factors influencing diagnosis, especially regarding disease symptoms that closely resemble those of other viral diseases, including other strains of SARS, thus impacting the identification of useful and meaningful symptom patterns as they emerge in infections. Therefore, this study proposes an association rule mining approach, utilising the Apriori algorithm to analyse the similarities between individuals with confirmed SARS-CoV-2 diagnosis and those with unspecified SARS diagnosis. The objective is to investigate, through symptom rules, the presence of COVID-19 patterns among individuals initially not diagnosed with the disease. Experiments were conducted using cases from Brazilian SARS datasets for São Paulo State. Initially, reporting percentage similarities of symptoms in both groups were analysed. Subsequently, the top ten rules from each group were compared. Finally, a search for the top five most frequently occurring positive rules among the unspecified ones, and vice versa, was conducted to identify identical rules, with a particular focus on the presence of positive rules among the rules of individuals initially diagnosed with unspecified SARS.

Initial learning in the brain: From rules to action

Abstract We used fMRI to investigate the neural changes and representational dynamics associated with different learning modes during initial learning and subsequent implementation of previously acquired stimulus-response (S-R) associations. We compared instruction-based learning (INS) and trial-and-error learning (TE) via a third observation-based learning (OBS) condition. This was yoked to the TE condition and shared features with both, the INS and TE conditions. During learning, neural changes were observed in the Frontoparietal and Default Mode Networks across learning modes, consistent with a general decrease in cognitive control demand as learning progresses. INS and TE exhibited condition-specific signal changes, which we interpreted in the context of covert motor preparation during INS, and intentional action and increased cognitive control demand during early TE trials, respectively. Multivariate pattern analysis revealed individual rule information in bilateral prefrontal, premotor, and parietal cortices across learning modes. Most regions revealed consistent representations of individual S-R rules between the learning stage and subsequent implementation stage, regardless of the learning mode. This suggests that initially formed S-R rule representations guide task performance during S-R rule implementation, irrespective of how they are acquired. Finally, within the primary motor and sensory cortices, individual S-R rules were decodable during the learning stage not only when motor responses were overtly executed, as in TE, but also in the absence of overt motor execution, as in INS. This finding substantiates previous claims of covert motor preparatory mechanisms during INS.

Measuring the individualization potential of treatment individualization rules: Application to rules built with a new parametric interaction model for parallel-group clinical trials.

For personalized medicine, we propose a general method of evaluating the potential performance of an individualized treatment rule in future clinical applications with new patients. We focus on rules that choose the most beneficial treatment for the patient out of two active (nonplacebo) treatments, which the clinician will prescribe regularly to the patient after the decision. We develop a measure of the individualization potential (IP) of a rule. The IP compares the expected effectiveness of the rule in a future clinical individualization setting versus the effectiveness of not trying individualization. We illustrate our evaluation method by explaining how to measure the IP of a useful type of individualized rules calculated through a new parametric interaction model of data from parallel-group clinical trials with continuous responses. Our interaction model implies a structural equation model we use to estimate the rule and its IP. We examine the IP both theoretically and with simulations when the estimated individualized rule is put into practice in new patients. Our individualization approach was superior to outcome-weighted machine learning according to simulations. We also show connections with crossover and N-of-1 trials. As a real data application, we estimate a rule for the individualization of treatments for diabetic macular edema and evaluate its IP.

Neyman meets causal machine learning: Experimental evaluation of individualized treatment rules

Abstract A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across disciplines. In this article, we demonstrate that Neyman’s methodology can also be used to experimentally evaluate the efficacy of individualized treatment rules (ITRs), which are derived by modern causal machine learning (ML) algorithms. In particular, we show how to account for additional uncertainty resulting from a training process based on cross-fitting. The primary advantage of Neyman’s approach is that it can be applied to any ITR regardless of the properties of ML algorithms that are used to derive the ITR. We also show, somewhat surprisingly, that for certain metrics, it is more efficient to conduct this ex-post experimental evaluation of an ITR than to conduct an ex-ante experimental evaluation that randomly assigns some units to the ITR. Our analysis demonstrates that Neyman’s repeated sampling framework is as relevant for causal inference today as it has been since its inception.

Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival.

Individualized treatment rules inform tailored treatment decisions based on the patient's information, where the goal is to optimize clinical benefit for the population. When the clinical outcome of interest is survival time, most of current approaches typically aim to maximize the expected time of survival. We propose a new criterion for constructing Individualized treatment rules that optimize the clinical benefit with survival outcomes, termed as the adjusted probability of a longer survival. This objective captures the likelihood of living longer with being on treatment, compared to the alternative, which provides an alternative and often straightforward interpretation to communicate with clinicians and patients. We view it as an alternative to the survival analysis standard of the hazard ratio and the increasingly used restricted mean survival time. We develop a new method to construct the optimal Individualized treatment rule by maximizing a nonparametric estimator of the adjusted probability of a longer survival for a decision rule. Simulation studies demonstrate the reliability of the proposed method across a range of different scenarios. We further perform data analysis using data collected from a randomized Phase III clinical trial (SWOG S0819).

Personalized Cardiology

Abstract Personalized/precision cardiology is a great hope of our times. In daily practice, we apply personalized, but not precise, cardiology: we take individual patient histories and perform individual clinical examinations, but we treat using general rule, general evidence-based guidelines… We do hope, in the near future, to take individual examination and treat using individual rules. Genuine, meaningful precision cardiology will rely heavily on genetics and artificial intelligence, but lifestyle, individual risk factors, patient and family compliance to treatment, and genetic makeup can influence diagnostic accuracy and treatment effects and should not be neglected. Extensive data exists regarding applications of artificial intelligence in the practice of precision cardiology, but we lack the systems that would translate its potential into clinical outcomes for real patients.

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm.

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

On quantifying heterogeneous treatment effects with regression‐based individualized treatment rules: Loss function families and bounds on estimation error

SummaryHeterogeneity in response to treatment is a pervasive problem in medicine. Many researchers have proposed individualized treatment rule methods for this problem, which personalize treatment recommendations based on an individual's recorded covariates. A challenge with using these methods in practice is that they determine a treatment rule, rather than quantify treatment benefit. This can be problematic, as a recommended treatment could be burdensome and have negligible improvements in outcome for some individuals. With the aim of helping practitioners make informed modelling choices, we identify two families of loss functions to use with individualized treatment rule methods. Under the assumption of correct model specification, estimation with a loss function from one family ensures that the model's treatment recommendations can be interpreted in terms of the risk difference, while the other family of loss functions ensures that the model's treatment recommendations can be interpreted in terms of the risk ratio. We also derive two upper bounds for a model's error in risk difference and risk ratio estimation. Each upper bound can be calculated using observed data and can provide insight to practitioners regarding model error in estimating treatment effects. We illustrate our contributions with simulation studies as well as with data from the ACTG‐175 AIDS study.

Paleoenvironmental significance of growth story of long-living deep-water acervulinid macroids from Kikai-jima shelf, Central Ryukyu Islands, Japan

Macroids are unattached centimetre-sized nodules built by encrusting invertebrates. Encrusting foraminifera (Acervulina inhaerens) and subordinate thin coralline algae form extensive macroid beds on sandy and gravelly bioclastic carbonates off Kikai-jima, on a coral-reef-related island shelf, in the Central Ryukyu Islands, Japan. At water depths from 75 to 100 m, the beds consist of spheroidal and sub-spheroidal macroids, c. 6 cm in mean diameter, with asymmetric concentric inner arrangement. The macroids are bioeroded by Entobia, Maeandropolydora, Trypanites, Gastrochaenolites, and microborings. They generally show two distinct growth stages separated by an abraded rugged surface deeply colonized by borers, mainly Entobia. Radiocarbon dating yielded an oldest age of c. 4400 cal yr BP for the earliest acervulinid growth, whereas the second stages were much younger, ranging in age from c. 1500 cal yr BP to present day. Datings of the two growth stages in five specimens indicate that active growth and growth interruption were not synchronous in the different nodules. For c. 4400 years the macroids grew within an estimated maximum range of palaeotemperature changes of c. 4.7 °C, under chronic oligotrophic to mesotrophic conditions, low-level hydrodynamism and low sedimentation rates. The lack of synchroneity among individual macroids rules out catastrophic events and ecosystem-wide environmental changes as possible causes of growth interruption. Random biogenic mobilization and temporal occupation of the macroid surface by organisms with no rigid skeleton and/or biofilms likely interrupted acervulinid growth at individual macroid scale. The environmental conditions in which Kikai-jima macroid beds develop do not support interpretations of acervulinid macroid accumulations during Paleocene−Eocene Thermal Maximum (PETM) and Middle Eocene Climatic Optimum (MECO) events in the Western Tethys as indicators of eutrophic conditions.

Railway crew planning with fairness over time

Passenger railway operators typically employ large numbers of drivers and guards, and are interested in providing them with fair and attractive working conditions. At Netherlands Railways (NS), the largest passenger railway operator in The Netherlands, this challenge is addressed through the use of Sharing-Sweet-and-Sour rules, which specify a fair allocation of sweet (attractive) and sour (unattractive) work over the different crew bases. While these rules are currently implemented at the crew base level and in the tactical planning phase, NS is considering formulating these rules at the individual level, in the operational planning phase, and with respect to a given planning period. This gives rise to a new problem, which we call the railway crew planning problem with fairness over time. We propose a rolling horizon approach with a penalty-based feedback mechanism and a column generation heuristic to solve this problem. On several real-life instances from NS, including up to 572 unique guards, this method is able to satisfy the individual rules for on average 95.2% of the employees.

How acceptable is optimal deterrence?

The article examines the social acceptability of optimal deterrence policies. While there is considerable research on intuitions regarding punishment and the counter-intuitive nature of economic reasoning, the problem of whether decisions based on optimal deterrence are acceptable remains inadequately researched. Two studies examined how acceptance rates vary for different decisions implementing optimal deterrence theory. They demonstrate that (1) policies increasing punishment severity are more acceptable than those decreasing it; (2) changes at the level of penal policy are more acceptable than individual court rulings; (3) acceptance rates decrease as the magnitude of change increases; (4) strict adherence to optimal deterrence theory’s recommendations does not significantly affect the acceptability of the decisions. In addition, no optimal deterrence policies were accepted, on average, or by the majority of participants.

ПИТАННЯ ФОРМУВАННЯ МУЛЬТИ-НЕЧІТКИХ МОДЕЛЕЙ І ПРОЦЕДУР СИСТЕМ МОНІТОРИНГУ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ

The article details issues related to the features, tasks and the development of fuzzy models and intelligent procedures designed for software monitoring systems. At the same time, improved forms of fuzzy control models, called multi-fuzzy, are proposed. They generalize the methods of fuzzy modeling in the structure of rules and procedures by representing the dependence of the rules in the knowledge base, establishing various models of input data and applying various fuzzy inference methods for individual knowledge base rules.

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

Pragmatic Study on Deixis Analysis in Ruskin Bond’s Short Story ‘The Eyes Have It’

This in-depth investigation of Ruskin Bond's "The Eyes Have It" delves into the darkest recesses of deixis. The discerning eye uncovers compelling patterns by unraveling the enigmatic tapestry of person, spatial, temporal, discourse, and social deixis. Individual deixis rules at 36.07%, a throbbing heartbeat reverberating with character drenching. Spatial deixis (18.03%) lays out scenes of energetic tones, while talk deixis (20.08%) arranges the story's balletic developments. Character interaction gains vitality thanks to social deixis (16.80%). The elusive conductor orchestrating chronological harmony is temporal deixis (9.02 percent). A linguistic symphony dissected to illuminate the elusive pragmatics in short story literature, the narrative is imbued with coherence and depth by the dance of deixis types, which is intricately interwoven.

Особенности режима обжалования определений судов в гражданском судопроизводстве

The work is devoted to the study of legislative and law enforcement restrictions on challenging individual court rulings. It has been established that legal facts, including deadlines, and subjective rights are used as means of legal regimes in this case, in particular. These funds have a significant stimulating or disincentive effect on participants in the proceedings. In addition, the lack of legal regulation may have an impact on the participants in the process. As an example, the article considers the appeal of the ruling on the abandonment of the statement of claim without motion and on the transfer of the case by jurisdiction. The author comes to the conclusion that today there is a regime of unfavorable appeal of court rulings. As a result, there are abuses of the participants in the process, red tape on the part of the court. The solution to these problems is the establishment of disciplining and enforceable deadlines on the part of the participants, taking into account the interests of the participants in the formulation of their rights, as well as individual legal structures.

Individualized treatment rule characterization via a value function surrogate.

Precision medicine is a promising framework for generating evidence to improve health and health care. Yet, a gap persists between the ever-growing number of statistical precision medicine strategies for evidence generation and implementation in real-world clinical settings, and the strategies for closing this gap will likely be context-dependent. In this paper, we consider the specific context of partial compliance to wound management among patients with peripheral artery disease. Using a Gaussian process surrogate for the value function, we show the feasibility of using Bayesian optimization to learn optimal individualized treatment rules. Further, we expand beyond the common precision medicine task of learning an optimal individualized treatment rule to the characterization of classes of individualized treatment rules and show how those findings can be translated into clinical contexts.

Advancing indoor risk mapping for virus transmission of infectious diseases through geographic scenario simulation

ABSTRACT Close-range interpersonal interactions serve as a major channel for virus transmission, with higher infection risks indoors than outdoors. Thus, evaluating indoor infectious disease transmission risks is vital for effective epidemic prevention and control. However, collecting complete individual-level behavioral data faces challenges due to privacy concerns and data acquisition costs, impeding accurate indoor risk mapping. To address this, we propose an individual-centered, scenario-based simulation framework in this paper. This framework incorporates stepwise movement of agents to model crowd interactions in indoor spaces, enabling infection risk mapping through geographic scenario simulation. The simulation model’s core components encompass the generation of indoor environments, formulation of individual behavior rules, establishment of human-environment interaction logic, and simulation of virus transmission processes. Additionally, we outline the implementation algorithm for this simulation model. Lastly, we employ a high-risk university canteen as a case study to demonstrate the model’s capabilities in creating risk maps at different levels: spatial, spatiotemporal, and individual. The proposed framework achieves the construction and process simulation of multi-dimensional geographic scenarios at a microscale, introduces a behavior path model based on spatiotemporal cubes, and enhances the comprehensive analysis and mapping capabilities for indoor infectious disease risk, laying the foundation for precise prevention and control.