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PubMed1973

Use of sequential Bayesian model in diagnosis of jaundice by computer.

A sequential Bayesian model has been developed for a computer and used to diagnose jaundiced patients admitted to hospital. Up to 102 items of information from the history, physical examination, and special investigations available within 48 hours of admission were collected on 309 patients. The results from these patients were used to calculate the probabilities of 11 possible diseases in 65 new patients and also to place patients into groups for medical or surgical treatment.The overall accuracy of the model in diagnosing patients as having one of 11 diseases was 69%, and where the final probability reached > 0.96, it was 89%. The overall accuracy in making a medical or surgical decision was 89%, and where the final probability reached > 0.96 it was 94%.Improvement in accuracy should result as the number of cases seen with rare conditions increases, and probably a similar model could be developed and used to make most use of those indicants with the highest cost-effectiveness.

Acute Disease↗

PubMed1991

Everyday diagnostics--a critique of the Bayesian model.

In recent years Bayesian probability calculus and Bayesian decision procedures have been recommended for use in clinical medicine. The author investigate everyday diagnostics asking if it takes place in a reality that meets the conditions of the method. He finds it does not. Above all we lack that strict randomness essential for probability calculus and, further, evaluation of utility easily becomes disputable, not to say unethical. The measures constructed for neutralizing these discrepancies between model and reality demand great resources. Yet, they are not enough to permit the clinician to refrain from supervising the consequences of his decisions as carefully as he has always done. A different method, a different model is wanted.

Bayes Theorem↗

PubMed1989

Individualizing vancomycin dosage regimens: one- versus two-compartment Bayesian models.

The absolute and relative predictive performances of one- and two-compartment Bayesian forecasting models were evaluated and compared. Initial population parameters were derived from 25 adult patients with stable renal function and who were being treated for presumed or documented gram-positive infections. The performance of each model was compared using these population parameters with and without steady-state or non-steady-state feedback concentrations to predict future peak and trough concentrations in an additional 20 patients. Both models tended to underpredict vancomycin peak and trough concentrations obtained at steady state. The use of a two-compartment model resulted in statistically less bias and more precise predictions of vancomycin peak concentrations when either population parameters or non-steady-state concentrations were used for future predictions. No difference in model performance was observed when steady-state concentrations were used to predict future steady-state concentrations. The results of this evaluation demonstrate that the two-compartment Bayesian model is less biased and more precise in determining future vancomycin serum concentrations given only population parameters or non-steady-state feedback information. No difference in model performance could be discerned when steady-state concentrations were used as feedback information.

Adolescent↗

PubMedDate not supplied

Sequential analysis in a Bayesian model of diastolic blood pressure measurement.

A sequential method for diagnosing or excluding hypertension based on the Bayesian model of diastolic blood pressure presented in a companion article is presented. The likelihood ratio method of Wald is modified to include the effects of a prior probability distribution and to constrain the strategy to achieve specified positive and negative predictive values. The resulting formulas for upper and lower limits to diagnose and exclude diastolic hypertension can be evaluated using a hand calculator and a table of areas of the standard normal distribution. The strategy is illustrated for a population having a blood pressure distribution similar to that of the cohort screened for participation in the Hypertension Detection and Follow-up Program, with 90 mm Hg as the cutoff defining hypertension and required positive and negative predictive values of 95%. The performance of the strategy was simulated using Monte Carlo methods. The median number of readings required for diagnosis is three, and 80% of subjects are diagnosed in 11 or fewer readings. In contrast to the strategy's 95% predictive values, a fixed-number-of-measurements strategy requiring the same mean number of measurements has a positive predictive value of only 83% and a negative predictive value of 96%. When the parameters of the model have been properly measured or estimated, this method is practical, efficient, and accurate for diagnosing hypertension in a known population.

Bayes Theorem↗

PubMedDate not supplied

Evaluation of a computerized Bayesian model for diagnosis of renal cyst vs. tumor vs. normal variant from urogram information.

The diagnostic problem of cyst/tumor/normal variant raised on an excretory urogram leads to a decision to do needle aspiration or renal arteriography. This decision depends critically upon the probability distribution for the three diagnoses. A computerized Bayesian model of a uroradiologist's diagnostic process in solving the problem was developed. The model was based on subjective probabilities supplied by an experienced uroradiologist. The model was evaluated in terms of its ability to decrease the cost of further diagnosis regarding aspiration versus arteriography. The model's output was compared with decisions made by unaided radiologists viewing the same panel of 50 urogram test cases. Results indicate that the model does not improve upon the decisions made by a radiologist highly experienced with this diagnostic problem. However, the decisions made by unaided, less experienced radiologists result in greater cost than those of the model.

Angiography↗

PubMed1988

Evaluating diagnostic performance of clinical tests by spreadsheet modeling. Bayesian analysis using Ri/Cj ratio as a unifying concept.

We present a general spreadsheet model for evaluating diagnostic performance of clinical tests. Our model depicts test results as an r X c matrix, with r possible test results and c possible clinical states. Analysis of this matrix is based on the Ri/Cj ratio, calculated as a number of subjects having a specified result Ri within a given clinical state Cj, divided by total subjects within this clinical state. From this model, we can identify three special cases: (1) a 2 X c matrix, with two possible test results of T+ or T-, over c possible clinical states; (2) an r X 2 matrix, with r possible test results, over two possible clinical states of D+ or D-; and (3) a 2 X 2 matrix, with two possible test results over two possible clinical states. Application of the Ri/Cj ratio to the r X c matrix provides a useful approach to graphic analysis of multiple test results over multiple clinical states. The Ri/Cj ratio also provides a general approach to Bayesian analysis, in which likelihood ratio, relative operating characteristic analysis, sensitivity, and specificity represent special cases or special applications.

Bayes Theorem↗

PubMed2025

Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.

With the increasing maturity of genetic profiling, an essential and routine task in cancer research is to model disease outcomes/phenotypes using genetic variables. Many methods have been successfully developed. However, oftentimes, empirical performance is unsatisfactory because of a "lack of information." In cancer research and clinical practice, a source of information that is broadly available and highly cost-effective comes from pathological images, which are routinely collected for definitive diagnosis and staging. In this article, we consider a Bayesian approach for selecting relevant genetic variables and modeling their relationships with a cancer outcome/phenotype. We propose borrowing information from (manually curated, low-dimensional) pathological imaging features via reinforcing the same selection results for the cancer outcome and imaging features. We further develop a weighting strategy to accommodate the scenario where information borrowing may not be equally effective for all subjects. Computation is carefully examined. Simulations demonstrate competitive performance of the proposed approach. We analyze TCGA (The Cancer Genome Atlas) LUAD (lung adenocarcinoma) data, with overall survival and gene expressions being the outcome and genetic variables, respectively. Findings different from the alternatives and with sound properties are made.

Humans↗

PubMed1975

The impact of laboratory error on the normal range: a Bayesian model.

Interpretation of clinical laboratory results, aside from clinical considerations, is based on the probability of the result being within a given normal range. This probability is influenced by the degree of error inherent in the analytical method. It would be advantageous to assign a more definite probability to the result of the measurement by combining the error distribution of the result around the true value and the distribution of the healthy population that serves as a reference. Bayesian statistics permits the revision of this prior information into a single probability.

Calcium↗

PubMed1992

Bayesian model selection and minimum description length estimation of auditory-nerve discharge rates.

Auditory-nerve fiber discharges are modeled as self-exciting point processes with intensity given by the product of a stimulus-related function and a refractory-related function. Previous methods of estimating these two functions, based on the maximum-likelihood principle, have the problem of estimating more parameters than the data can support. A new procedure, based on a Bayes criterion for choosing the complexity of the model in addition to estimating the parameters, solves the over-parametrization problem. This procedure is seen to relate asymptotically to Rissanen's minimum description length (MDL) criterion. A performance comparison of the MDL procedure with previous maximum-likelihood algorithms promotes the adoption of the MDL procedure for simultaneous estimation of the stimulus and recovery properties of auditory-nerve discharge.

Algorithms↗

PubMedDate not supplied

Intelligent dialogue based on statistical models of clinical decision-making.

The independence Bayesian model has been used widely in computer programs designed to support clinical decision-making. A reasoning strategy has been developed to enable these programs to conduct clinically pertinent dialogue and explain their reasoning. It has been implemented in a program for the diagnosis of acute abdominal pain based on the Bayesian model of de Dombal et al. Several features of the dialogue design have been adopted from artificial intelligence research, including shared initiative and critiquing. The program adopts a flexible goal-driven strategy, attempting to confirm the clinician's diagnosis or rule out the likeliest alternative. Symptoms and signs are selected in order of their expected weights of evidence in favour of the hypothesized disease.

Abdomen↗

PubMed1988

Use of a Bayesian statistical model for risk assessment in coronary artery surgery.

A computerized statistical model based on the theorem of Bayes was developed to predict mortality after coronary artery bypass grafting. From January, 1984, to April, 1987, at our hospital, 700 patients underwent isolated coronary artery bypass grafting. The presence or absence of 20 risk factors was determined for each patient. The first 300 patients formed the initial database of the Bayesian predictive model, and the remaining 400 patients were prospectively evaluated in four groups of 100 each. Each group was prospectively evaluated and then incorporated into the database to update the model. There was good agreement between predicted and observed results. Bayesian theory is particularly suited to this task because it (1) accommodates multiple risk factors, (2) is tailored to one's specific practice, (3) determines individual, rather than group, prognosis, and (4) can be updated with time to compensate for a changing patient population. These flexible attributes are especially valuable in light of recent changes in the coronary artery bypass graft patient profile.

Aged↗

PubMed1991

Early individualization of tricyclic antidepressant dosing using a Bayesian pharmacokinetic model.

Existing methods to prospectively dose tricyclic antidepressants (TCAs) require either specific test doses, precisely timed serum sampling, or both. We prospectively tested a new pharmacokinetic model that allows flexible dosing and sampling to determine maintenance requirements in patients receiving TCAs. Thirty-four patients entered the study. Drug concentrations were measured on the third day after starting TCA therapy. These values were analyzed using a Bayesian pharmacokinetic model to determine drug clearance and volume of distribution. This information was then used to predict the serum concentration resulting from a maintenance dose chosen by the psychiatrist. In phase I (n = 17), patients received imipramine without specific starting doses. Phase II (n = 17) was performed to provide a preliminary evaluation of the method in the usual clinical environment. In this phase, patients received either amitriptyline, imipramine, desipramine, doxepin (75 mg on day 1,100 mg on day 2), or nortriptyline (50 mg on day 1, 75 mg on day 2). Lower doses were allowed if clinically indicated. The predictability of future serum concentrations was then compared between the two phases. The mean prediction errors (model bias) in phases I and II were -15.5 +/- 27.3 and -12.3 +/- 21.8 ng/mL and were not different (p greater than 0.05). The absolute prediction errors (model precision) were 18.5 +/- 25.1 and 18.8 +/- 16.0 ng/mL and were not different (p greater than 0.05). Two slow metabolizers were identified (clearance less than 0.10 L/kg/h). This new method allows the determination of maintenance dose requirements early in therapy without standard test doses or specifically timed serum sampling.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

PubMed2026

Genetic Susceptibility to Incisional Hernia Evaluation of Hernia Polygenic Risk Scores.

OBJECTIVES: Incisional hernia (IH) affects 13-30% of people after abdominal surgery, resulting in substantial morbidity and costs. While clinical risk factors have been studied extensively, genomic risk for IH is incompletely understood. We aimed to evaluate the impact of polygenic risk scores (PRS) on IH risk prediction. METHODS: We created and evaluated three PRS for abdominal hernia, ventral hernia and latent hernia susceptibility for prediction of IH in an institutional biobank. The primary outcome was defined as the diagnosis or repair of an IH based on ICD-9/10-CM/PCS and CPT codes. Clinical covariates included age, sex, body mass index (BMI), smoking status, index procedure type, and perioperative surgical site infection. A phenome-wide association study (PheWAS) was performed to assess clinical associations with increased PRS. We then tested the ability of the PRS to improve prediction for IH by modeling clinical covariates with and without PRS in patients who underwent abdominal surgery. Model performance was assessed using 10 iterations of 5-fold cross-validation to estimate Brier scores and area under the receiver operating characteristic curve (AUROC), which were compared using cross-model Bayesian analysis of variance. RESULTS: In 55,809 subjects, assessed PRS was significantly associated with incisional, umbilical, and ventral hernia on PheWAS, with 1.19 greater odds of developing IH per 1-SD increase in PRS (95% CI: 1.13-1.25, P < 0.001). Of 9,909 subjects who underwent qualifying abdominal surgery, 706 developed IH. In this cohort, the latent hernia susceptibility PRS was associated with a 16% increased hazard of developing IH per 1-SD increase (HR 1.16; 95% CI: 1.07-1.26; P < 0.001). Compared to a predictive model using clinical covariates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC = 0.660, 95% CI: 0.653-0.666), addition of the PRS showed similar Brier score and AUROC estimates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC: 0.667, 95% CI: 0.661-0.673) at five years. Cross-model Bayesian analysis demonstrated >99% probability of practical equivalence when trying to detect a difference of ≥ 0.02. CONCLUSION: All three PRS for hernia were independently associated with IH, suggesting that genomic factors contribute significantly to IH development. However, none of the three PRS meaningfully improved clinical IH risk prediction in patients who underwent abdominal surgery. This suggests that clinical comorbidities and surgical techniques may be equally as important as genomic architecture.

Bayesian analysis↗

PubMed1990

Popperian everyday diagnostics--the growth of diagnostic knowledge in the particular case.

In an earlier paper the Bayesian model for everyday diagnostics of disease in the particular patient and the Bayesian decision model was criticized. Here a Popperian model is applied and its presumptions and consequences are investigated. Abandoning calculable probabilities as Popper suggests in science, and substituting them with degrees of corroboration, is realistic.

Decision Theory↗

PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗

PubMed2026

On the origin of animals and placental mammals: a critique of literalist readings of the fossil record.

The fossil record is incomplete, as evidenced by the pervasive presence of ghost lineages throughout the Tree of Life. For example, across placental mammals, at least 720 Myr of basal lineages are ghost lineages, that is, lineages that have left no fossil evidence of their past history. In contrast, some studies have suggested that the fossil record is a faithful temporal archive of evolutionary history and thus the times of diversification of clades must be close to the ages of their oldest fossils. Such literalist interpretations have been contradicted by analysis of molecular datasets which, in many cases, indicate that groups including placental mammals and animals may have originated at times substantially older than their fossil records. Some of those studies have further argued that, in the case of animals and placental mammals, molecular clocks are uninformative, suffer from characteristic pathologies, and thus cannot distinguish between recent and ancient hypotheses of diversification. Here, we reexamine these two cases and show, using Bayesian model selection theory, that the explosive diversification models previously proposed for animals and placental mammals have a posterior probability of ∼0. We show the characteristic pathologies purportedly discovered do not exist, highlight errors in previous analyses, and provide advice on best practice for molecular-clock dating analysis.

Animals↗

PubMed2026

Weathering the storm: Most maternal and environmental drivers of individual reproductive success do not scale up to population recruitment in a large herbivore.

Population growth depends upon individual survival and reproduction, but do drivers of individual reproductive success scale up to population recruitment? Factors affecting individuals may have little effect on population dynamics if individuals within a population experience different conditions. When seasonal resource availability is unpredictable and breeding season long, average conditions over a breeding cycle may poorly reflect the environment experienced by many individuals. We compared the drivers of individual reproductive success and population recruitment in an asynchronously breeding large herbivore, the eastern grey kangaroo (Macropus giganteus). We analysed 18 years of individual-based data using multivariate hierarchical Bayesian models to first identify the causal mechanisms relating population density, environmental conditions and maternal traits to individual success. We then assessed whether the drivers of individual reproductive success scaled up to determine population recruitment. Most maternal and environmental covariates strongly influenced individual reproductive success, with distinct effects on juvenile survival before and after pouch exit. Maternal traits had a greater influence in the pouch, whereas environmental conditions became increasingly important once young exited the pouch. Most drivers of individual reproductive success did not affect population recruitment. Recruitment increased with population density and mean body condition of adult females. Weather harshness had a weak positive effect on recruitment, which appeared independent of female age structure, previous recruitment or forage. Most drivers of individual reproductive success did not scale up to population recruitment. Birth asynchrony could buffer population recruitment against environmental variation such that variables affecting individual reproduction have little impact at the population level. Large herbivores that reproduce asynchronously may therefore be more resilient to environmental variability than synchronous breeders.

Bayesian modelling↗

PubMed1991

Bayesian image processing in magnetic resonance imaging.

In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.

Algorithms↗