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Trip's SmartSearch engine has discovered connected searches & results. Click to show### 1. Application of Bayes' Theorem in Valuating Depression Tests Performance Full Text available with Trip Pro

Application of

**Bayes**'**Theorem**in Valuating Depression Tests Performance The validity of clinical diagnoses is a fundamental topic in clinical psychology, because now there are some political administrations, as the IOM or the U.K. government, which are focusing on best evidence-based practice in clinical psychology. The most problematic issue in clinical psychology is to avoid wrong diagnoses which can have negative consequences on individual life and on the utility of clinical treatments (...) . In the case of diagnoses based on self-report tests, the diagnostic decision about individual health is based on the comparison between its score and the cutoff, according to the frequentist approach to probability. However, the frequentist approach underestimates the possible risks of incorrect diagnoses based on cutoffs only. The Bayesian approach is a valid alternative to make diagnoses on the basis of the scores from psychological tests. The**Bayes**'**theorem**estimates the posterior probability#### 2018 Frontiers in psychology

### 2. Bayes Theorem

**Bayes**

**Theorem**

**Bayes**

**Theorem**Toggle navigation Brain Head & Neck Chest Endocrine Abdomen Musculoskeletal Skin Infectious Disease Hematology & Oncology Cohorts Diagnostics Emergency Findings Procedures Prevention & Management Pharmacy Resuscitation Trauma Emergency Procedures Ultrasound Cardiovascular Emergencies Lung Emergencies Infectious Disease Pediatrics Neurologic Emergencies Skin Exposure Miscellaneous Abuse Cancer Administration 4

**Bayes**

**Theorem**

**Bayes**

**Theorem**Aka:

**Bayes**

**Theorem**, Bayesian (...) Statistics From Related Chapters II. Definitions

**Bayes**

**Theorem**(calculation) P (Disease | Positive Test) = P(Positive test | Disease) * P(Disease) / P(Positive Test) Where P (A | B) = Probability of A given B P(Positive test | Disease) = III. Evaluation: Example - Probability of Disease Based on a Test Positive Test Disease Y Present in 75 Disease Y NOT Present in 25 Negative Test Disease Y Present in 10 Disease Y NOT Present in 190 Probabilities P(Positive test I Disease) = = 75 / (75 + 10) = 0.88 P

#### 2018 FP Notebook

### 3. How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes' Theorem Full Text available with Trip Pro

How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using

**Bayes**'**Theorem**Ruling out disease often requires expensive or potentially harmful confirmation testing. For such testing, a less invasive triage test is often used. Intuitively, few negative confirmatory tests suggest success of this approach. However, if negative confirmation tests become too rare, too many disease cases could have been missed. It is therefore important to know how (...) many negative tests are needed to safely exclude a diagnosis. We quantified this relationship using**Bayes**'**theorem**, and applied this to the example of pulmonary embolism (PE), for which triage is done with a Clinical Decision Rule (CDR) and D-dimer testing, and CT-angiography (CTA) is the confirmation test. For a maximum proportion of missed PEs of 1% in triage-negative patients, we calculate a 67% 'mandatory minimum' proportion of negative CTA scans. To achieve this, the proportion of patients#### 2016 PloS one

### 4. Use of LC-MS/MS and Bayes' theorem to identify protein kinases that phosphorylate aquaporin-2 at Ser256 Full Text available with Trip Pro

Use of LC-MS/MS and

**Bayes**'**theorem**to identify protein kinases that phosphorylate aquaporin-2 at Ser256 In the renal collecting duct, binding of AVP to the V2 receptor triggers signaling changes that regulate osmotic water transport. Short-term regulation of water transport is dependent on vasopressin-induced phosphorylation of aquaporin-2 (AQP2) at Ser256. The protein kinase that phosphorylates this site is not known. We use**Bayes**'**theorem**to rank all 521 rat protein kinases with regard (...) of phosphorylating AQP2 at Ser256, although CAMK2 and PKA were more potent than SGK. The in vitro phosphorylation experiments also identified candidate protein kinases for several additional phosphoproteins with likely roles in collecting duct regulation, including Nedd4-2, Map4k4, and 3-phosphoinositide-dependent protein kinase 1. We conclude that**Bayes**'**theorem**is an effective means of integrating data from multiple data sets in physiology.#### 2014 American Journal of Physiology - Cell Physiology

### 5. A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem Full Text available with Trip Pro

A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using

**Bayes****Theorem**Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One (...) is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the**Bayes****theorem**to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving#### 2013 The Scientific World Journal

### 6. Bayes Theorem

**Bayes**

**Theorem**

**Bayes**

**Theorem**Toggle navigation Brain Head & Neck Chest Endocrine Abdomen Musculoskeletal Skin Infectious Disease Hematology & Oncology Cohorts Diagnostics Emergency Findings Procedures Prevention & Management Pharmacy Resuscitation Trauma Emergency Procedures Ultrasound Cardiovascular Emergencies Lung Emergencies Infectious Disease Pediatrics Neurologic Emergencies Skin Exposure Miscellaneous Abuse Cancer Administration 4

**Bayes**

**Theorem**

**Bayes**

**Theorem**Aka:

**Bayes**

**Theorem**, Bayesian (...) Statistics From Related Chapters II. Definitions

**Bayes**

**Theorem**(calculation) P (Disease | Positive Test) = P(Positive test | Disease) * P(Disease) / P(Positive Test) Where P (A | B) = Probability of A given B P(Positive test | Disease) = III. Evaluation: Example - Probability of Disease Based on a Test Positive Test Disease Y Present in 75 Disease Y NOT Present in 25 Negative Test Disease Y Present in 10 Disease Y NOT Present in 190 Probabilities P(Positive test I Disease) = = 75 / (75 + 10) = 0.88 P

#### 2015 FP Notebook

### 7. A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions Full Text available with Trip Pro

-multinomial distributions are estimated from training microbiome data sets based on maximum likelihood. The posterior probability of a microbiome sample belonging to a disease or healthy category is calculated based on

**Bayes**'**theorem**, using the likelihood values computed from the estimated Dirichlet-multinomial distribution, as well as a prior probability estimated from the training microbiome data set or previously published information on disease prevalence. When tested on real-world microbiome data (...) A Dirichlet-Multinomial**Bayes**Classifier for Disease Diagnosis with Microbial Compositions Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a**Bayes**classifier by modeling microbial compositions with Dirichlet-multinomial distributions, which are widely used to model multicategorical count data with extra variation. The parameters of the Dirichlet#### 2017 mSphere

### 8. NeBcon: protein contact map prediction using neural network training coupled with naÃ¯ve Bayes classifiers Full Text available with Trip Pro

methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction.We developed a new pipeline, NeBcon, which uses the naïve

**Bayes**classifier (NBC)**theorem**to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. The posterior probabilities of the NBC model are then trained with intrinsic structural features through neural (...) NeBcon: protein contact map prediction using neural network training coupled with naÃ¯ve**Bayes**classifiers Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient#### 2017 Bioinformatics

### 9. NaÃ¯ve Bayes classification in R Full Text available with Trip Pro

NaÃ¯ve

**Bayes**classification in R Naïve**Bayes**classification is a kind of simple probabilistic classification methods based on**Bayes**'**theorem**with the assumption of independence between features. The model is trained on training dataset to make predictions by predict() function. This article introduces two functions naiveBayes() and train() for the performance of Naïve**Bayes**classification.#### 2016 Annals of Translational Medicine

### 10. An Example of an Improvable Raoâ€“Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator Full Text available with Trip Pro

An Example of an Improvable Raoâ€“Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized

**Bayes**Estimator The Rao-Blackwell**theorem**offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement (...) . This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized**Bayes**estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised#### 2016 The American statistician

### 11. Interpretation of gene associations with risk of acute respiratory distress syndrome: P values, Bayes factors, positive predictive values, and need for replication Full Text available with Trip Pro

**Bayes**

**Theorem**Genetic Association Studies statistics & numerical data Humans Polymorphism, Single Nucleotide genetics Predictive Value of Tests Respiratory Distress Syndrome, Adult diagnosis genetics Acute respiratory distress syndrome

**Bayes**factor Gene association P values 2016 12 22 6 0 2016 12 22 6 0 2018 1 20 6 0 epublish 27998281 10.1186/s13054-016-1550-8 10.1186/s13054-016-1550-8 PMC5175392 Pediatr Crit Care Med. 2008 Nov;9(6):553-9 18838927 Pediatr Crit Care Med. 2015 Jun;16(5 Suppl 1):S6-22 (...) Interpretation of gene associations with risk of acute respiratory distress syndrome: P values,

**Bayes**factors, positive predictive values, and need for replication 27998281 2018 01 19 2018 11 13 1466-609X 20 1 2016 Dec 21 Critical care (London, England) Crit Care Interpretation of gene associations with risk of acute respiratory distress syndrome: P values,

**Bayes**factors, positive predictive values, and need for replication. 402 10.1186/s13054-016-1550-8 Rimpau Sebastian S Department

#### 2016 Critical Care

### 12. Bayes and the Law Full Text available with Trip Pro

. These include misconceptions by the legal community about

**Bayes**'**theorem**, over-reliance on the use of the likelihood ratio and the lack of adoption of modern computational methods. We argue that Bayesian Networks (BNs), which automatically produce the necessary Bayesian calculations, provide an opportunity to address most concerns about using**Bayes**in the law. (...)**Bayes**and the Law Although the last forty years has seen considerable growth in the use of statistics in legal proceedings, it is primarily classical statistical methods rather than Bayesian methods that have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This paper reviews the potential and actual use of**Bayes**in the law and explains the main reasons for its lack of impact on legal practice#### 2016 Annual review of statistics and its application

### 13. The Central Role of Bayesâ€™ Theorem for Joint Estimation of Causal Effects and Propensity Scores Full Text available with Trip Pro

and the use of

**Bayes****theorem**. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Avoiding specification of a detailed model for the outcome response surface is valuable for robust estimation of causal effects, but this strategy is at odds with the use of**Bayes****theorem**, which presupposes a full probability model for the observed data that adheres to the likelihood (...) The Central Role of Bayesâ€™**Theorem**for Joint Estimation of Causal Effects and Propensity Scores Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score#### 2015 The American statistician

### 14. Reasoning under uncertainty

of likelihood. For example, it may be stated that ‘it is very likely the patient has a particular disease’ or ‘it is very likely that the patient will die within a certain specified time period.’ This paper assumes that such measures of likelihood can be represented numerically by a number between 0 and 1, a number which is known as a probability. In other words, probability represents a measure of belief. There is a fundamental

**theorem**underlying reasoning under uncertainty. The**theorem**is**Bayes**’**theorem**(...) , named after a non-conformist theologian, Thomas**Bayes**(1701–1761), who was a student at the University of Edinburgh and a Fellow of the Royal Society.**Bayes**’ contribution to science was twofold. First, he argued, as above, that uncertainty about the occurrence or otherwise of an event can be represented by a probability. Second, he showed through his**theorem**how one’s uncertainty about the occurrence of an event can be revised in the receipt of evidence or information of relevance to that event#### 2019 Evidence-Based Mental Health

### 15. Blood and Clots Series: How can I tell whether this patient has a deep vein thrombosis?

. There was no tenderness in the distribution of the deep calf veins although he was tender in the medial mid-thigh. There were no other signs of deep vein thrombosis (DVT). Clinical probability estimation No diagnostic test is either 100% sensitive or 100% specific. As physicians, we can’t diagnose or exclude a condition without considering the results within the clinical context of our individual patient. This is

**Bayes****theorem**, which uses the pretest probability of a condition to calculate the probability (...) which placed him in the ‘DVT likely’ category. D-dimer D-dimer is most often used to exclude DVT or pulmonary embolism (PE) in patients with a low risk of venous thrombosis. In this case, I couldn’t use D-dimer to exclude DVT because of the higher pretest probability. This is an example of using**Bayes****theorem**. Generally speaking, a D-dimer can exclude venous thrombosis if the pretest probability is under 15% (or ‘DVT unlikely’) 3 . His probability of having DVT was too high for a normal D-dimer test#### 2018 CandiEM

### 16. Cancer Genetics Risk Assessment and Counseling (PDQ®): Health Professional Version

#### 2018 PDQ - NCI's Comprehensive Cancer Database

### 17. Breast Cancer Screening (PDQ®): Health Professional Version

reference diagnosis.[ ] While the overall agreement between the individual pathologists’ interpretations and the expert reference diagnoses was highest for invasive carcinoma, there were markedly lower levels of agreement for DCIS and atypia.[ ] As the B-Path study included higher proportions of cases of atypia and DCIS than typically seen in clinical practice, the authors expanded their work by applying

**Bayes**’**theorem**to estimate how diagnostic variability affects accuracy from the perspective of a U.S#### 2018 PDQ - NCI's Comprehensive Cancer Database

### 18. Management of carpal tunnel syndrome evidence-based clinical practice guideline

#### 2016 American Academy of Orthopaedic Surgeons

### 19. Skill (or lack thereof) of data-model fusion techniques to provide an early warning signal for an approaching tipping point. Full Text available with Trip Pro

schemes are: ensemble Kalman filtering (EnKF), particle filtering (PF), pre-calibration (PC), and Markov Chain Monte Carlo (MCMC) estimation. While differing in their core assumptions, each data assimilation scheme is based on

**Bayes**'**theorem**and updates prior beliefs about a system based on new information. For large computational investments, EnKF, PF and MCMC show similar skill in capturing the observed phosphorus in the lake (measured as expected root mean squared prediction error). EnKF, followed#### 2018 PLoS ONE

### 20. Preventing and Experiencing Ischemic Heart Disease as a Woman: State of the Science Full Text available with Trip Pro

#### 2016 American Heart Association

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