Relevant bibliographies by topics / Bayesian therory

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian therory'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Bayesian therory"

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Thomson, A. H. "BAYESIAN FEEDBACK METHODS FOR OPTIMISING THERAPY." Clinical Neuropharmacology 15 (1992): 245A—246A. http://dx.doi.org/10.1097/00002826-199201001-00128.

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Abrams, Keith, Deborah Ashby, and Doug Errington. "Bayesian analysis of neutron therapy trial." Controlled Clinical Trials 12, no. 5 (1991): 666–67. http://dx.doi.org/10.1016/0197-2456(91)90200-6.

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Duan, Leo L., Alexander L. Young, Akihiko Nishimura, and David B. Dunson. "Bayesian constraint relaxation." Biometrika 107, no. 1 (2019): 191–204. http://dx.doi.org/10.1093/biomet/asz069.

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Summary Prior information often takes the form of parameter constraints. Bayesian methods include such information through prior distributions having constrained support. By using posterior sampling algorithms, one can quantify uncertainty without relying on asymptotic approximations. However, sharply constrained priors are not necessary in some settings and tend to limit modelling scope to a narrow set of distributions that are tractable computationally. We propose to replace the sharp indicator function of the constraint with an exponential kernel, thereby creating a close-to-constrained neighbourhood within the Euclidean space in which the constrained subspace is embedded. This kernel decays with distance from the constrained space at a rate depending on a relaxation hyperparameter. By avoiding the sharp constraint, we enable use of off-the-shelf posterior sampling algorithms, such as Hamiltonian Monte Carlo, facilitating automatic computation in a broad range of models. We study the constrained and relaxed distributions under multiple settings and theoretically quantify their differences. Application of the method is illustrated through several novel modelling examples.
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Neoptolemos, John P., and Trevor F. Cox. "Bayesian analysis unravels pancreas-cancer adjuvant therapy." Lancet Oncology 14, no. 11 (2013): 1034–35. http://dx.doi.org/10.1016/s1470-2045(13)70403-0.

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Tidwell, Rebecca S. Slack, S. Andrew Peng, Minxing Chen, Diane D. Liu, Ying Yuan, and J. Jack Lee. "Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update." Clinical Trials 16, no. 6 (2019): 645–56. http://dx.doi.org/10.1177/1740774519871471.

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Background/aims In our 2009 article, we showed that Bayesian methods had established a foothold in developing therapies in our institutional oncology trials. In this article, we will document what has happened since that time. In addition, we will describe barriers to implementing Bayesian clinical trials, as well as our experience overcoming them. Methods We reviewed MD Anderson Cancer Center clinical trials submitted to the institutional protocol office for scientific and ethical review between January 2009 and December 2013, the same length time period as the previous article. We tabulated Bayesian methods implemented for design or analyses for each trial and then compared these to our previous findings. Results Overall, we identified 1020 trials and found that 283 (28%) had Bayesian components so we designated them as Bayesian trials. Among MD Anderson–only and multicenter trials, 56% and 14%, respectively, were Bayesian, higher rates than our previous study. Bayesian trials were more common in phase I/II trials (34%) than in phase III/IV (6%) trials. Among Bayesian trials, the most commonly used features were for toxicity monitoring (65%), efficacy monitoring (36%), and dose finding (22%). The majority (86%) of Bayesian trials used non-informative priors. A total of 75 (27%) trials applied Bayesian methods for trial design and primary endpoint analysis. Among this latter group, the most commonly used methods were the Bayesian logistic regression model (N = 22), the continual reassessment method (N = 20), and adaptive randomization (N = 16). Median institutional review board approval time from protocol submission was the same 1.4 months for Bayesian and non-Bayesian trials. Since the previous publication, the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial was the first large-scale decision trial combining multiple treatments in a single trial. Since then, two regimens in breast cancer therapy have been identified and published from the cooperative Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY 2), enhancing cooperation among investigators and drug developers across the nation, as well as advancing information needed for personalized medicine. Many software programs and Shiny applications for Bayesian trial design and calculations are available from our website which has had more than 21,000 downloads worldwide since 2004. Conclusion Bayesian trials have the increased flexibility in trial design needed for personalized medicine, resulting in more cooperation among researchers working to fight against cancer. Some disadvantages of Bayesian trials remain, but new methods and software are available to improve their function and incorporation into cancer clinical research.
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Büchter, Theresa, Nicole Steib, Katharina Böcherer-Linder, et al. "Designing Visualisations for Bayesian Problems According to Multimedia Principles." Education Sciences 12, no. 11 (2022): 739. http://dx.doi.org/10.3390/educsci12110739.

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Questions involving Bayesian Reasoning often arise in events of everyday life, such as assessing the results of a breathalyser test or a medical diagnostic test. Bayesian Reasoning is perceived to be difficult, but visualisations are known to support it. However, prior research on visualisations for Bayesian Reasoning has only rarely addressed the issue on how to design such visualisations in the most effective way according to research on multimedia learning. In this article, we present a concise overview on subject-didactical considerations, together with the most fundamental research of both Bayesian Reasoning and multimedia learning. Building on these aspects, we provide a step-by-step development of the design of visualisations which support Bayesian problems, particularly for so-called double-trees and unit squares.
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Mukha, V. S. "Comparative numerical analysis of Bayesian decision rule and probabilistic neural network for pattern recognition." Doklady BGUIR 19, no. 7 (2021): 13–21. http://dx.doi.org/10.35596/1729-7648-2021-19-7-13-21.

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At present, neural networks are increasingly used to solve many problems instead of traditional methods for solving them. This involves comparing the neural network and the traditional method for specific tasks. In this paper, computer modeling of the Bayesian decision rule and the probabilistic neural network is carried out in order to compare their operational characteristics for recognizing Gaussian patterns. Recognition of four and six images (classes) with the number of features from 1 to 6 was simulated in cases where the images are well and poorly separated. The sizes of the training and test samples are chosen quiet big: 500 implementations for each image. Such characteristics as training time of the decision rule, recognition time on the test sample, recognition reliability on the test sample, recognition reliability on the training sample were analyzed. In framework of these conditions it was found that the recognition reliability on the test sample in the case of well separated patterns and with any number of the instances is close to 100 percent for both decision rules. The neural network loses 0,1–16 percent to Bayesian decision rule in the recognition reliability on the test sample for poorly separated patterns. The training time of the neural network exceeds the training time of the Bayesian decision rule in 4–5 times and the recognition time – in 4–6 times. As a result, there are no obvious advantages of the probabilistic neural network over the Bayesian decision rule in the problem of Gaussian pattern recognition. The existing generalization of the Bayesian decision rule described in the article is an alternative to the neural network for the case of non-Gaussian patterns.
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Quintela-del-Río, Alejandro, Beatriz Rodríguez-Romero, Verónica Robles-García, Pablo Arias-Rodríguez, Javier Cudeiro-Mazaira, and Alicia Martínez-Rodríguez. "Bayesian Methods in the Field of Rehabilitation." American Journal of Physical Medicine & Rehabilitation 98, no. 6 (2019): 516–20. http://dx.doi.org/10.1097/phm.0000000000001124.

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McAleer, S. D., H. Chrystyn, and D. A. G. Newton. "BAYESIAN DERIVED PHARMACOKINETIC/PHARMACODYNAMIC VARIABLES FROM WARFARIN THERAPY." Journal of Pharmacy and Pharmacology 42, S1 (1990): 189P. http://dx.doi.org/10.1111/j.2042-7158.1990.tb14562.x.

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Cao, Ming, Yue Fan, and Qinke Peng. "Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization." Computational and Mathematical Methods in Medicine 2021 (August 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/7471516.

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High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. How to identify these biomarkers precisely is significant for disease diagnosis, prognosis, and therapy. Many studies utilized pathway information to identify the biomarkers. However, most of these studies only incorporate the group information while the pathway structural information is ignored. In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. All the priors are conjugated; thus, the parameters can be estimated effectively through Gibbs sampling. We present the application of our method on 6 microarray datasets, comparing with Bayesian Lasso, Bayesian Elastic Net, and Bayesian Fused Lasso. The results show that our method performs better than other Bayesian methods and pathway structural information can improve the result.
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Dissertations / Theses on the topic "Bayesian therory"

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Vecchi, Claudio. "Scripting automation e modelli bayesiani: Applicazioni cliniche in radioterapia e sviluppo di tecniche innovative per adaptive radiation therapy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6623/.

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La presente ricerca consiste nel validare ed automatizzare metodiche di Adaptive Radiation Therapy (ART), che hanno come obiettivo la personalizzazione continua del piano di trattamento radioterapico in base alle variazioni anatomiche e dosimetriche del paziente. Tali variazioni (casuali e/o sistematiche) sono identificabili mediante l’utilizzo dell’imaging diagnostico. Il lavoro svolto presso la struttura di Fisica Medica dell’Azienda Ospedaliera Universitaria del Policlinico di Modena, si inserisce in un progetto del Ministero della Salute del bando Giovani Ricercatori dal titolo: “Dose warping methods for IGRT and ADAPTIVERT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments”. Questa metodica si sta affermando sempre più come nuova opportunità di trattamento e, per tale motivo, nasce l’esigenza di studiare e automatizzare processi realizzabili nella pratica clinica, con un utilizzo limitato di risorse. Si sono sviluppati script che hanno permesso l’automazione delle operazioni di Adaptive e deformazioni, raccogliendo i dati di 51 pazienti sottoposti a terapia mediante Tomotherapy. L’analisi delle co-registrazioni deformabili delle strutture e delle dosi distribuite, ha evidenziato criticità del software che hanno reso necessario lo sviluppo di sistemi di controllo dei risultati, per facilitare l’utente nella revisione quotidiana dei casi clinici. La letteratura riporta un numero piuttosto limitato di esperienze sulla validazione e utilizzo su larga scala di questi tools, per tale motivo, si è condotto un esame approfondito della qualità degli algoritmi elastici e la valutazione clinica in collaborazione di fisici medici e medici radioterapisti. Sono inoltre stati sviluppati principi di strutturazione di reti Bayesiane, che consentono di predirre la qualità delle deformazioni in diversi ambiti clinici (H&N, Prostata, Polmoni) e coordinare il lavoro quotidiano dei professionisti, identificando i pazienti, per i quali sono apprezzabili variazioni morfo-dosimetriche significative. Da notare come tale attività venga sviluppata automaticamente durante le ore notturne, sfruttando l’automation come strumento avanzato e indipendente dall’operatore. Infine, il forte sviluppo, negli ultimi anni della biomeccanica applicata al movimento degli organi (dimostrato dalla numerosa letteratura al riguardo), ha avuto come effetto lo sviluppo, la valutazione e l’introduzione di algoritmi di deformazione efficaci. In questa direzione, nel presente lavoro, si sono analizzate quantitivamente le variazioni e gli spostamenti delle parotidi, rispetto all’inizio del trattamento, gettando le basi per una proficua linea di ricerca in ambito radioterapico.
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Maposa, Innocent. "Survival modelling and analysis of HIV/AIDS patients on HIV care and antiretroviral treatment to determine longevity prognostic factors." University of the Western Cape, 2016. http://hdl.handle.net/11394/5444.

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Philosophiae Doctor - PhD<br>The HIV/AIDS pandemic has been a torment to the African developmental agenda, especially the Southern African Development Countries (SADC), for the past two decades. The disease and condition tends to affect the productive age groups. Children have also not been spared from the severe effects associated with the disease. The advent of antiretroviral treatment (ART) has brought a great relief to governments and patients in these regions. More people living with HIV/AIDS have experienced a boost in their survival prospects and hence their contribution to national developmental projects. Survival analysis methods are usually used in biostatistics, epidemiological modelling and clinical research to model time to event data. The most interesting aspect of this analysis comes when survival models are used to determine risk factors for the survival of patients undergoing some treatment or living with a certain disease condition. The purpose of this thesis was to determine prognostic risk factors for patients' survival whilst on ART. The study sought to highlight the risk factors that impact the survival time negatively at different survival time points. The study utilized a sample of paediatric and adult datasets from Namibia and Zimbabwe respectively. The paediatric dataset from Katutura hospital (Namibia) comprised of the adolescents and children on ART, whilst the adult dataset from Bulawayo hospital (Zimbabwe) comprised of those patients on ART in the 15 years and above age categories. All datasets used in this thesis were based on retrospective cohorts followed for some period of time. Different methods to reduce errors in parameter estimation were employed to the datasets. The proportional hazards, Bayesian proportional hazards and the censored quantile regression models were utilized in this study. The results from the proportional hazards model show that most of the variables considered were not signifcant overall. The Bayesian proportional hazards model shows us that all the considered factors had different risk profiles at the different quartiles of the survival times. This highlights that by using the proportional hazards models, we only get a fixed constant effect of the risk factors, yet in reality, the effect of risk factors differs at different survival time points. This picture was strongly highlighted by the censored quantile regression model which indicated that some variables were significant in the early periods of initiation whilst they did not significantly affect survival time at any other points in the survival time distribution. The censored quantile regression models clearly demonstrate that there are significant insights gained on the dynamics of how different prognostic risk factors affect patient survival time across the survival time distribution compared to when we use proportional hazards and Bayesian propotional hazards models. However, the advantages of using the proportional hazards framework, due to the estimation of hazard rates as well as it's application in the competing risk framework are still unassailable. The hazard rate estimation under the censored quantile regression framework is an area that is still under development and the computational aspects are yet to be incorporated into the mainstream statistical softwares. This study concludes that, with the current literature and computational support, using both model frameworks to ascertain the dynamic effects of different prognostic risk factors for survival in people living with HIV/AIDS and on ART would give the researchers more insights. These insights will then help public health policy makers to draft relevant targeted policies aimed at improving these patients' survival time on treatment.
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SEDDA, GIULIA. "The interplay between movement and perception: how interaction can influence sensorimotor performance and neuromotor recovery." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1011732.

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Movement and perception interact continuously in daily activities. Motor output changes the outside world and affect perceptual representations. Similarly, perception has consequences on movement. Nevertheless, how movement and perception influence each other and share information is still an open question. Mappings from movement to perceptual outcome and vice versa change continuously throughout life. For example, a cerebrovascular accident (stroke) elicits in the nervous system a complex series of reorganization processes at various levels and with different temporal scales. Functional recovery after a stroke seems to be mediated by use-dependent reorganization of the preserved neural circuitry. The goal of this thesis is to discuss how interaction with the environment can influence the progress of both sensorimotor performance and neuromotor recovery. I investigate how individuals develop an implicit knowledge of the ways motor outputs regularly correlate with changes in sensory inputs, by interacting with the environment and experiencing the perceptual consequences of self-generated movements. Further, I applied this paradigm to model the exercise-based neurorehabilitation in stroke survivors, which aims at gradually improving both perceptual and motor performance through repeated exercise. The scientific findings of this thesis indicate that motor learning resolve visual perceptual uncertainty and contributes to persistent changes in visual and somatosensory perception. Moreover, computational neurorehabilitation may help to identify the underlying mechanisms of both motor and perceptual recovery, and may lead to more personalized therapies.
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Krizova, Katarina. "ADHD CHILDREN AND MENTAL HEALTH SERVICE USE: MATERNAL DETERMINANTS." UKnowledge, 2015. http://uknowledge.uky.edu/hes_etds/28.

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The current study investigated maternal determinants of mental health service use, namely, individual child therapy, among preadolescent children diagnosed with ADHD. The Behavioral Model of Health Care Utilization (Andersen, 2008) was used as a theoretical framework for the study. Data from the last three rounds of ECLS-K dataset were employed to test a longitudinal model using Bayesian analysis. Socio-demographic variables and maternal mental health were tested as exogenous variables and mother-child relationship variables, discipline variables, and perceived maternal concern about child’s overall behavior and child’s emotional symptoms were tested as intervening variables. Results showed that only maternal mental health remained in the model as an exogenous variable. The effect of mental health on child therapy was mediated by maternal aggravation and maternal concern about overall behavior in one path and by maternal concern about emotional symptoms in another path, suggesting that maternal mental health needs to be considered when attempting to understand help-seeking determinants. Both concern variables were found to have large direct effects on child therapy. The results of the current study showed the importance of maternal mental health and the importance of determinants related to mother-child relationship in a mother’s decision to seek therapy for a child.
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NUNZIATI, MATTEO. "Tecnologie biometriche per il riconoscimento del parlante / Biometric technologies for speaker recognition." Doctoral thesis, 2008. http://hdl.handle.net/2158/590123.

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Cypko, Mario A. "Therapy Decision Support System using Bayesian Networks for Multidisciplinary Treatment Decisions." 2017. https://ul.qucosa.de/id/qucosa%3A16891.

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Treatment decision-making in head and neck oncology is gaining complexity by the increasing evidence pointing towards more individualized and selective treatment options. Therefore, decision making in multidisciplinary teams is becoming the key point in the clinical pathways. Clinical decision-support systems based on Bayesian networks can support complex decision-making processes by providing mathematically correct and transparent advises. In the last three decades, different clinical applications of Bayesian networks have been proposed. Because appropriate data for model learning and testing is often unobtainable, expert modeling is required. To decrease the modeling and validation effort, networks usually represent small or highly simplified decision structures. However, especially systems for supporting multidisciplinary treatment decisions may only gain a user’s confidence if the systems’ results are comprehensive and comprehensible. Challenges in developing such systems relate to knowledge engineering, model validation, system interaction, clinical implementation and standardization. These challenges are well-known, however, they are not or only partially addressed by the developers. The thesis presented a methodology for the development of Bayesian network-based clinical treatment decision support systems. For this purpose, a concept introduced interactions between actors and systems. The proposed concept emphasizes model development with an exemplary use case of model interaction. A graph model design was presented that allows integrating all relevant variables of multidisciplinary treatment decisions. At the current stage, we developed TreLynCa: A graph model representing the treatment decisions of laryngeal cancer. From TreLynCa, a subnetwork that represents the TNM staging is completed by the required probabilistic parameters, and finally validated. The model validation required the development of a validation cycle in combination with existing data- and expert-based validation methods. Furthermore, modeling methods were developed that enable domain experts to model autonomously without Bayesian network expertise. Specifically, a novel graph modeling method was developed, and an existing method for modeling probabilistic parameters was extended. Both methods transform Bayesian network modeling tasks into a natural language form and provide a regulated modeling environment. A method for graph modeling is based on the presented graph model design with a regulated and restricted modeling procedure. This modeling procedure is supposed to enable collaborative modeling of compatible models. The method is currently under development. A method for probabilistic modeling is extended to reduce the modeling effort to a linear time. The method has been implemented as a web tool and was tested and evaluated in two studies. Finally, for clinical application of the TNM model, requirements were collected and constructed in a visual framework. In collaboration with visual scientists, the framework has been implemented and evaluated.
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Powell, Ryan. "A novel approach to support evidence-based medicine: should sulfonylureas remain an acceptable therapy for diabetes?" Thesis, 2017. https://hdl.handle.net/2144/23373.

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A key element in evidence-based medicine approaches is the ability for clinicians to evaluate the scientific rigor and relevance of research evidence. In the treatment of diabetes, clinicians make increasingly difficult decisions about which drug regimens are best for their patients with limited evidence-based information. While the consensus is that metformin should be the initial drug treatment when diet and exercise are not sufficient, clinicians disagree on whether sulfonylureas should remain a suitable therapy after metformin. While this would be improved with further research investigating the comparative safety of therapeutic options, there is also need for better ways to synthesize available information to guide evidence-based decision-making in health services research. Study 1 summarizes the pre-existing evidence on the long-term safety risks associated with sulfonylurea therapy relative to other drug classes. Results from a series of meta-analyses provide some evidence that sulfonylureas are associated with elevated all-cause mortality and cardiovascular risks relative to several other medications, either as a monotherapy or in combination with metformin. Study 2 analyzes the comparative safety of second-line treatment in diabetic patients in the Veterans Health Administration to address gaps in the literature. Results suggest that second-line use of sulfonylureas is associated with increased risks compared to thiazolidinediones. Results also suggest that changes to existing metformin therapy may lead to differential hazards. Clinicians may disagree about the quality of the evidence as well as the relevancy to their own treatment population. Improvements in methods for evidence-based medicine that take this into account are needed. Study 3 applies an underutilized research method that allows for a more thoughtful synthesis of all available evidence. This framework allows clinicians to incorporate the scientific rigor and relevancy of previous study results when integrating new data into their current knowledge base. Results suggest an elevated risk in all models for sulfonylureas compared to thiazolidinediones and highlight the need to design more focused research to support clinical decision-making around medication safety. This novel application to evidence synthesis shows promise as applied to a health services research problem and has potential as a useful framework in other health services research areas.<br>2017-12-09T00:00:00Z
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"A Study on Optimization Measurement Policies for Quality Control Improvements in Gene Therapy Manufacturing." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.62668.

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abstract: With the increased demand for genetically modified T-cells in treating hematological malignancies, the need for an optimized measurement policy within the current good manufacturing practices for better quality control has grown greatly. There are several steps involved in manufacturing gene therapy. These steps are for the autologous-type gene therapy, in chronological order, are harvesting T-cells from the patient, activation of the cells (thawing the cryogenically frozen cells after transport to manufacturing center), viral vector transduction, Chimeric Antigen Receptor (CAR) attachment during T-cell expansion, then infusion into patient. The need for improved measurement heuristics within the transduction and expansion portions of the manufacturing process has reached an all-time high because of the costly nature of manufacturing the product, the high cycle time (approximately 14-28 days from activation to infusion), and the risk for external contamination during manufacturing that negatively impacts patients post infusion (such as illness and death). The main objective of this work is to investigate and improve measurement policies on the basis of quality control in the transduction/expansion bio-manufacturing processes. More specifically, this study addresses the issue of measuring yield within the transduction/expansion phases of gene therapy. To do so, it was decided to model the process as a Markov Decision Process where the decisions being made are optimally chosen to create an overall optimal measurement policy; for a set of predefined parameters.<br>Dissertation/Thesis<br>Masters Thesis Industrial Engineering 2020
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Wijeysundera, Harindra Channa. "Economic Evaluation of Percutaneous Coronary Intervention in Stable Coronary Artery Disease: Studies in Utilities and Decision Modeling." Thesis, 2011. http://hdl.handle.net/1807/32162.

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The initial treatment options for patients with stable coronary artery disease include optimal medical therapy alone, or coronary revascularization with optimal medical therapy. The most common revascularization modality is percutaneous coronary intervention (PCI) with either bare metal stents (BMS) or drug-eluting stents (DES). PCI is believed to reduce recurrent angina and thereby decrease the need for additional procedures compared to optimal medical therapy alone. It remains unclear if these benefits are sufficient to offset the increased costs and small increase in adverse events associated with PCI. The objectives of this thesis were to determine the degree of angina relief afforded by PCI and develop a tool to provide contemporary estimates of the impact of angina on quality of life. In addition, we sought to develop a comprehensive state-transition model, calibrated to real world costs and outcomes to compare the cost-effectiveness of initial medical therapy versus PCI with either BMS or DES in patients with stable coronary artery disease. ii We performed a systematic search and meta-analysis of the published literature. Although PCI was associated with an overall benefit on angina relief (odds ratio [OR] 1.69; 95% Confidence Interval [CI] 1.24-2.30), this benefit was largely attenuated in contemporary studies (OR 1.13; 95% CI 0.76-1.68). Our meta-regression analysis suggests that this observation was related to greater use of evidence-based medications in more recent trials. Using simple linear regression, we were able to create a mapping tool that could accurately estimate utility weights from data on the Seattle Angina Question, the most common descriptive quality of life instrument used in the cardiovascular literature. In our economic evaluation, we found that an initial strategy of PCI with a BMS was cost- effective compared to medical therapy, with an incremental cost-effectiveness ratio (ICER) of $13,271 per quality adjusted life year gained. In contrast, DES had a greater cost and lower survival than BMS and was therefore a dominated strategy.
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Books on the topic "Bayesian therory"

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Pan, Haitao, and Ying Yuan. Bayesian Adaptive Design for Immunotherapy and Targeted Therapy. Springer, 2024.

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Pan, Haitao, and Ying Yuan. Bayesian Adaptive Design for Immunotherapy and Targeted Therapy. Springer, 2023.

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Book chapters on the topic "Bayesian therory"

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Adak, Sudeshna, and Abhinanda Sarkar. "Longitudinal Modeling of the Side Effects of Radiation Therapy." In Case Studies in Bayesian Statistics. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1502-8_5.

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"Bayesian Predictive Inference for a Binary RandomVariable: Survey Estimation of the Quality of Care thatRadiation Therapy Patients Receive." In Bayesian Biostatistics. CRC Press, 2018. http://dx.doi.org/10.1201/9781315274300-38.

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Berzuini, Carlo, Riccardo Bellazzi, and David Spiegelhalter. "BAYESIAN NETWORKS APPLIED TO THERAPY MONITORING." In Uncertainty Proceedings 1991. Elsevier, 1991. http://dx.doi.org/10.1016/b978-1-55860-203-8.50008-5.

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Little, Max A. "Nonparametric Bayesian machine learning and signal processing." In Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.003.0010.

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We have seen that stochastic processes play an important foundational role in a wide range of methods in DSP. For example, we treat a discrete-time signal as a Gaussian process, and thereby obtain many mathematically simplified algorithms, particularly based on the power spectral density. At the same time, in machine learning, it has generally been observed that nonparametric methods outperform parametric methods in terms of predictive accuracy since they can adapt to data with arbitrary complexity. However, these techniques are not Bayesian so we are unable to do important inferential procedures such as draw samples from the underlying probabilistic model or compute posterior confidence intervals. But, Bayesian models are often only mathematically tractable if parametric, with the corresponding loss of predictive accuracy. An alternative, discussed in this section, is to extend the mathematical tractability of stochastic processes to Bayesian methods. This leads to so-called Bayesian nonparametrics exemplified by techniques such as Gaussian process regression and Dirichlet process mixture modelling that have been shown to be extremely useful in practical DSP and machine learning applications.
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Sankararaman, Shankar, and Sankaran Mahadevan. "Statistical Approach to Structural Damage Diagnosis under Uncertainty." In Emerging Design Solutions in Structural Health Monitoring Systems. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8490-4.ch008.

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This chapter presents a statistical methodology for structural damage diagnosis (detection, localization and estimation), in the context of continuous online monitoring. There are several sources of uncertainty such as physical variability, measurement uncertainty and model errors that affect structural damage diagnosis, and therefore, it may not be possible to precisely detect, localize, and estimate damage. Hence, a statistical approach can help to identify these sources of uncertainty, quantify their combined effect on diagnosis, and thereby, provide an estimate of the confidence in the results of diagnosis. Damage detection is based on residuals between nominal and damaged system-level responses, using statistical hypothesis testing whose uncertainty can be captured easily. Localization is based on the comparison of damage signatures derived from the system model. Both classical statistics-based methods and Bayesian statistics-based methods are investigated to quantify the uncertainty in all the three steps of diagnosis, i.e. detection, localization, and quantification. While classical statistics-based methods use the concept of least squares-based optimization, Bayesian methods make use of likelihood function and Bayes theorem. The uncertainties in damage detection, isolation and quantification are combined to quantify the overall uncertainty in diagnosis. The proposed methods are illustrated using the example of a structural frame.
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Mishra, Mayank, and Chavon Grande. "Probabilistic NDT data fusion of Ferroscan test data using Bayesian inference." In Structural Analysis of Historical Constructions: Anamnesis, Diagnosis, Therapy, Controls. CRC Press, 2016. http://dx.doi.org/10.1201/9781315616995-99.

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Han, Seunghan, and Walter Stechele. "Default Reasoning for Forensic Visual Surveillance Based on Subjective Logic and its Comparison with L-Fuzzy Set Based Approaches." In Multimedia Data Engineering Applications and Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2940-0.ch004.

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Default reasoning can provide a means of deriving plausible semantic conclusion under imprecise and contradictory information in forensic visual surveillance. In such reasoning under uncertainty, proper uncertainty handling formalism is required. A discrete species of Bilattice for multivalued default logic demonstrated default reasoning in visual surveillance. In this article, the authors present an approach to default reasoning using subjective logic that acts in a continuous space. As an uncertainty representation and handling formalism, subjective logic bridges Dempster Shafer belief theory and second order Bayesian, thereby making it attractive tool for artificial reasoning. For the verification of the proposed approach, the authors extend the inference scheme on the bilattice for multivalued default logic to L-fuzzy set based logics that can be modeled with continuous species of bilattice structures. The authors present some illustrative case studies in visual surveillance scenarios to contrast the proposed approach with L-fuzzy set based approaches.
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Taffarel, S., G. P. Campostrini, L. Rosato, C. Marson, F. da Porto, and C. Modena. "The application of a Bayesian approach to assess the seismic vulnerability of historical centers." In Structural Analysis of Historical Constructions: Anamnesis, Diagnosis, Therapy, Controls. CRC Press, 2016. http://dx.doi.org/10.1201/9781315616995-166.

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O. Soogun, Adenike, Ayesha B.M. Kharsany, Temesgen Zewotir, and Delia North. "Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women in an HIV Hyperendemic Area of KwaZulu-Natal, South Africa." In Infectious Diseases. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105547.

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New HIV infections among young women remains exceptionally high and to prevent onward transmission, UNAIDS set ambitious treatment targets. This study aimed to determine the prevalence, spatial variation and factors associated with unsuppressed HIV viral load at ≥400 copies per mL. This study analysed data from women aged 15–49 years from the HIV Incidence Provincial Surveillance System (HIPSS) enrolled in two sequential cross-sectional studies undertaken in 2014 and 2015 in rural and peri-urban KwaZulu-Natal, South Africa. Bayesian geoadditive model with spatial effect for a small enumeration area was adopted using Integrated Nested Laplace Approximation (INLA) function to analyze the findings. The overall prevalence of unsuppressed HIV viral load was 45.2% in 2014 and 38.1% in 2015. Factors associated with unsuppressed viral load were no prior knowledge of HIV status, had a moderate-to-low perception of acquiring HIV, not on antiretroviral therapy (ART), and having a low CD4 cell count. In 2014, women who ever consumed alcohol and in 2015, ever ran out of money, had two or more lifetime sexual partners, ever tested for tuberculosis, and ever diagnosed with sexually transmitted infection were at higher risk of being virally unsuppressed. The nonlinear effect showed that women aged 15 to 29 years, from smaller households and had fewer number of lifetime HIV tests, were more likely to be virally unsuppressed. High viral load risk areas were the north-east and south-west in 2014, with north and west in 2015. The findings provide guidance on identifying key populations and areas for targeted interventions.
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Conference papers on the topic "Bayesian therory"

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Seo, Sangwon, and Vaibhav V. Unhelkar. "Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/346.

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We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to model the behavior of teams performing sequential tasks in Markovian domains. In contrast to existing multi-agent imitation learning techniques, BTIL explicitly models and infers the time-varying mental states of team members, thereby enabling learning of decentralized team policies from demonstrations of suboptimal teamwork. Further, to allow for sample- and label-efficient policy learning from small datasets, BTIL employs a Bayesian perspective and is capable of learning from semi-supervised demonstrations. We demonstrate and benchmark the performance of BTIL on synthetic multi-agent tasks as well as a novel dataset of human-agent teamwork. Our experiments show that BTIL can successfully learn team policies from demonstrations despite the influence of team members' (time-varying and potentially misaligned) mental states on their behavior.
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Bijedic, N., I. Hamulic, Z. Vukobrat-Bijedic, and A. Husic-Selimovic. "Bayesian network model of HCV therapy response prediction in FBiH." In 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2012. http://dx.doi.org/10.1109/sisy.2012.6339487.

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Agrawal, Krishna, Kushagra Jain, Dhawal Gupta, Raunak Srivastav, Abhijeet Agnihotri, and Atul Thakur. "Bayesian Optimization Based Terrestrial Gait Tuning for a 12-DOF Alligator-Inspired Robot With Active Body Undulation." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86033.

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In addition to aiding in swimming, body undulation of an alligator plays a critical role in terrestrial locomotion by imparting stability. This paper reports design, fabrication and terrestrial locomotion control incorporating active body undulation of a 12-DOF alligator-inspired robot. Each of the four legs of the developed robot has two rotational degrees of freedom while the body can perform undulation using additional four rotational degrees of freedom. This paper also presents a Bayesian optimization based approach to tune the gait parameters of both leg oscillation and body undulation in order to maximize the average robot speed. We obtained improvement by a factor of 1.93 in average robot speed in comparison to the one obtained by randomly generated parameters and report the experimental results in this paper. In future, we plan to generalize the developed Bayesian optimization based parameter tuning approach for the swimming gait and thereby impart amphibious capabilities to the developed robot.
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Liu, Xiaofeng, Bo Hu, Linghao Jin, et al. "Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/122.

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In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of p(x|y) and p(y). However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r.t. p(x), which rests on an unrealistic assumption that p(y) is invariant across domains. We thereby propose a novel variational Bayesian inference framework to enforce the conditional distribution alignment w.r.t. p(x|y) via the prior distribution matching in a latent space, which also takes the marginal label shift w.r.t. p(y) into consideration with the posterior alignment. Extensive experiments on various benchmarks demonstrate that our framework is robust to the label shift and the cross-domain accuracy is significantly improved, thereby achieving superior performance over the conventional IFL counterparts.
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Sengupta, Ushnish, Carl E. Rasmussen, and Matthew P. Juniper. "Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14904.

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Abstract Experiments are performed on a turbulent swirling flame placed inside a vertical tube whose fundamental acoustic mode becomes unstable at higher powers and equivalence ratios. The power, equivalence ratio, fuel composition and boundary condition of this tube are varied and, at each operating point, the combustion noise is recorded. In addition, short acoustic pulses at the fundamental frequency are supplied to the tube with a loudspeaker and the decay rates of subsequent acoustic oscillations are measured. This quantifies the linear stability of the system at every operating point. Using this data for training, we show that it is possible for a Bayesian ensemble of neural networks to predict the decay rate from a 300 millisecond sample of the (un-pulsed) combustion noise and therefore forecast impending thermoacoustic instabilities. We also show that it is possible to recover the equivalence ratio and power of the flame from these noise snippets, confirming our hypothesis that combustion noise indeed provides a fingerprint of the combustor’s internal state. Furthermore, the Bayesian nature of our algorithm enables principled estimates of uncertainty in our predictions, a reassuring feature that prevents it from making overconfident extrapolations. We use the techniques of permutation importance and integrated gradients to understand which features in the combustion noise spectra are crucial for accurate predictions and how they might influence the prediction. This study serves as a first step towards establishing interpretable and Bayesian machine learning techniques as tools to discover informative relationships in combustor data and thereby build trustworthy, robust and reliable combustion diagnostics.
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Shahan, David, and Carolyn C. Seepersad. "Bayesian Network Classifiers for Set-Based Collaborative Design." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28724.

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Complex design problems are typically decomposed into smaller design problems that are solved by domain-specific experts who must then coordinate their solutions into a satisfactory system-wide solution. In set-based collaborative design, collaborating engineers coordinate themselves by communicating multiple design alternatives at each step of the design process. The goal in set-based collaborative design is to spend additional resources exploring multiple options in the early stages of the design process, in exchange for less iteration in the latter stages, when iterative rework tends to be most expensive. Several methods have been proposed for representing sets of designs, including intervals, surrogate models, fuzzy membership functions, and probability distributions. In this paper, we introduce the use of Bayesian networks for capturing sets of promising designs, thereby classifying the design space into satisfactory and unsatisfactory regions. The method is compared to intervals in terms of its capacity to accurately classify satisfactory design regions as a function of the number of available data points. A simplified, multilevel design problem for an unmanned aerial vehicle is presented as the motivating example.
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Bai, Guangxing, Amirmahyar Abdolsamadi, and Pingfeng Wang. "Failure Prognosis Based on Adaptive State Space Models." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-66167.

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This paper presents a generic data-driven failure prognosis method based on adaptive state space models for engineering systems, which integrates adaptive model recognition with a dynamic system model for remaining useful life prediction. The developed approach employs a statistical learning framework for adaptively learning of time-series degradation performance, and then a Bayesian technique for self-updating of data-driven models to adapt the operational or environmental changes. With the developed approach, the prognosis technique can eliminate the dependence to system specific models and be adaptive to system performance changes due to degradation or variation of system operational conditions, thereby yielding accurate remaining useful life predictions. The developed methodology is demonstrated by an engineering case study.
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Wendel, John C., Andrew W. Nelson, Arif S. Malik, and Mark E. Zipf. "Bayesian-Based Probabilistic Force Modeling in Cold Rolling." In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1226.

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A primary factor in manufacturing high-quality cold-rolled sheet is the ability to accurately predict the required rolling force. The rolling force directly influences roll-stack deflections, which correlate to the resulting flatness quality of the rolled sheet. Increasingly high demand for thin and ultra-thin gauge for cold-rolled sheet metals, along with the correspondingly larger sensitivity of flatness defects when rolling thin gauges, makes it more important to accurately and rapidly predict the rolling force before the rolling operation begins. Accurate rolling force predictions enable assignment of appropriate pass schedules and flatness mechanism set-points early in the rolling process, thereby reducing rolling time, improving quality, and reducing scrap. Traditionally, force predictions in cold rolling have employed two-dimensional analytical models such as those proposed by Roberts and by Bland &amp; Ford. These simplified methods are prone to inaccuracy, however, because of several uncertain, yet influential, model parameters that are difficult to establish deterministically for wide-ranging products. These parameters include, for example, the average compressive yield strength of the rolled strip, frictional characteristics relating to low and high mill speeds, and the strain rate dependency of yield strength. Conventionally, these unknown parameters have been evaluated deterministically by comparing force predictions with actual rolling force data and using a best-fit regression approach. In this work, Bayesian updating using a probability mass function (PMF) is applied to identify joint posterior probability distributions of the uncertain parameters in rolling force models. It is shown that the non-deterministic Bayesian updating approach is particularly useful as new evidence becomes available in the form of additional rolling force data. The aim of the work is to incorporate Bayesian inference into rolling force prediction for cold rolling mills to create a probabilistic modeling approach which can also “learn” as new production data is added. The goal is a model that can better predict necessary mill parameters based on accurate probability estimates of the actual rolling force. The rolling force data used in this work for applying Bayesian updating is actual production data of grades 301 and 304L (low carbon) stainless steels, rolled on a 10-inch wide 4-high cold rolling mill. This force data was collected by observing and averaging load cell measurements at steady rolling speeds.
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Stöhr, M., A. Hikal, A. Oeser, et al. "Development of a therapy decision-supporting system for laryngeal cancer based on Bayesian networks." In Abstract- und Posterband – 90. Jahresversammlung der Deutschen Gesellschaft für HNO-Heilkunde, Kopf- und Hals-Chirurgie e.V., Bonn – Digitalisierung in der HNO-Heilkunde. Georg Thieme Verlag KG, 2019. http://dx.doi.org/10.1055/s-0039-1686080.

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Hamaide, Valentin, and François Glineur. "Transfer learning in Bayesian optimization for the calibration of a beam line in proton therapy." In ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ciaco - i6doc.com, 2021. http://dx.doi.org/10.14428/esann/2021.es2021-79.

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Reports on the topic "Bayesian therory"

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Li, wanlin, jie Yun, siying He, ziqi Zhou, and ling He. Effect of different exercise therapies on fatigue in maintenance hemodialysis patients:A Bayesian Network Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0144.

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Review question / Objective: Population: maintenance hemodialysis patients. Intervention: exercise therapy (resistance exercise； aerobic exercise； resistance combined aerobic exercise； muscle relaxation training； Baduanjin ). Comparison: simple routine nursing. Outcome: fatigue； sleep quality. Study design: randomized controlled trial. Eligibility criteria: Inclusion and exclusion criteria： RCT of study type exercise intervention in MHD patients' fatigue； Study subjects： MHD patients ≥18 years old, regardless of gender, nationality or race； The intervention measures were exercise therapy, including resistance exercise, aerobic exercise, resistance combined aerobic exercise, Baduanjin, muscle relaxation training, etc. The control group was conventional nursing measures or the comparison of the above exercise therapy； Outcome indicators： The primary outcome indicator was fatigue score, and the secondary outcome indicator was sleep quality score； Exclusion criteria： Literature using non-exercise intervention; Non-Chinese and English documents; Unable to obtain the full text or repeated publication of literature; The data cannot be extracted or the extraction is incomplete; There are serious defects in the design of the research experiment.
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Xu, Piao. Sound therapy for the treatment of chronic subjective tinnitus:A Bayesian network meta analysis of RCTs. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2020. http://dx.doi.org/10.37766/inplasy2020.4.0033.

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Zhou, Hang, Li Yan, Shuguang Zhang, et al. Acupuncture related therapy for ovulatory disorders: a systematic review and Bayesian network meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.6.0078.

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Chen, WanQiang, FangFang Wu, HongBo Lv, et al. Whether cognitive behavioral therapy is effective for Alzheimer's disease: a protocol for Bayesian network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2020. http://dx.doi.org/10.37766/inplasy2020.10.0076.

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Wu, Tong, Chengwei Fu, Yiran Deng, Wanping Huang, Yang Jiao, and Xiaoxiao Li. Acupuncture therapy for Radiotherapy-induced adverse effects: A protocol for systematic review and Bayesian network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2020. http://dx.doi.org/10.37766/inplasy2020.7.0054.

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Hossain, Niamat Ullah Ibne, Farjana Nur, Raed Jaradat, et al. Metrics for assessing overall performance of inland waterway ports : a Bayesian Network based approach. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40545.

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Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports.
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He, zhe, liwei Xing, ming He, yuhuan Sun, jinlong Xu, and rong Zhao. Effect of Acupuncture on Mammary Gland Hyperplasia (MGH): a Bayesian network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0058.

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Review question / Objective: This review aims at conducting a network meta-analysis to assess the potential therapeutic effectiveness and safety of acupuncture therapy for the treatment of MGH. Condition being studied: MGH is a benign breast disease caused by excessive growth of mammary duct epithelial cells and interstitial fibers. Its prevalence rate among women of childbearing age is about 13.5-42%, accounting for 99.3% of the total number of patients with breast related diseases, and its possibility of developing breast cancer can reach 5-10%. Breast hyperplasia can cause clinical symptoms such as breast pain, breast lump, nipple pigmentation and mood fluctuation, which brings severe physical and mental burden to patients. Modern medicine believes that the pathogenesis of MGH is related to sexual hormone disorder secondary to hypothalamus pituitary ovary axis dysfunction.At present, the treatment options of MGH are limited and not completely effective. The commonly used drugs in clinical practice, such as tamoxifen, danazol and goserelin, are expensive, which may lead to breast pain, swelling and increase of interstitial fibrous nodules, and the long-term use of MGH has huge side effects. The clinical guidelines recommend that the use time should be 2 to 6 months. Therefore, it is necessary to seek a treatment method of MGH that is effective, stable and safe.
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Zhang, Wei-ping, Dong-qing Zhao, Guang-yu Qian, Jing Jin, Yin-ping Yao, and Xing-mao Bian. Acupuncture therapy strategy options in postoperative management after laparoscopic cholecystectomy：A protocol for systematic review and Bayesian meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2020. http://dx.doi.org/10.37766/inplasy2020.12.0056.

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Han, Yiqun, Jiayu Wang, and Binghe Xu. Assessment of optimal choice as first-line therapy for patients with triple-negative breast cancer: a Bayesian analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.8.0030.

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Bian, Zhiyuan, Jie Yu, Mingqi Tu, et al. Acupuncture and related therapies for carpal tunnel syndrome: A protocol for systematic review and Bayesian network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.11.0094.

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Review question / Objective: We aim to compare the efficacy and safety of different acupuncture and related therapies for carpal tunnel syndrome (CTS) using systematic review and network meta-analysis (NMA). Condition being studied: CTS is a symptomatic condition caused by compression of the median nerve within the carpal tunnel. Patients with CTS typically report paresthesia or pain in distribution of median nerve distal to the wrist. Diverse non-surgical treatments and surgical decompression have been used in the management of CTS. Acupuncture, a prominent component of traditional Chinese medicine (TCM), has also been practiced when treating CTS as a complementary therapy. However, the relative treatment effects of different acupuncture methods for CTS are unclear.
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