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At least 19 recordsLinked to original sources
PubMed1992

Drug delivery optimization through Bayesian networks.

This paper describes how Bayesian Networks can be used in combination with compartmental models to plan Recombinant Human Erythropoietin (r-HuEPO) delivery in the treatment of anemia of chronic uremic patients. Past measurements of hematocrit or hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of the erythropoiesis. This adaptive process allows more accurate patient-specific predictions, and hence a more rational dosage planning. We describe a drug delivery optimization protocol, based on our approach. Some results obtained on real data are presented.

Anemia↗
PubMed2026

Suction-assisted ureteroscopy compared with traditional ureteroscopy for renal stones ≤ 2 cm: a systematic review, Bayesian network meta-analysis and meta-regression.

INTRODUCTION AND OBJECTIVE: Suction-enhanced flexible ureteroscopy (URS) aims to improve stone clearance and reduce complications. We performed a Bayesian network meta-analysis to compare the efficacy and safety of flexible aspiration navigable sheaths (FANS) and direct in-scope suction (DISS) for renal calculi ≤ 2 cm. METHODS: A systematic search of PubMed, MEDLINE, Scopus, Web of Science, and Google Scholar was conducted through June 2026. Comparative studies of FANS, DISS, or conventional access sheaths for renal stones ≤ 2 cm were included. The primary outcome was 30-day stone-free rate (SFR). Secondary outcomes included operative time, fever, sepsis, and complications. A Bayesian random-effects network meta-analysis synthesized direct and indirect evidence. RESULTS: Seventeen studies including 3,657 patients (1,677 FANS, 56 DISS, 1,924 control) were included. FANS showed higher SFR (OR 2.5, 95% CrI 2.0-3.1), while grouped DISS had a similar but less precise effect (OR 3.1, 95% CrI 1.0-8.8). Calyxo V2 had the highest SFR (OR 5.4, 95% CrI 1.0-29.0), whereas PUSEN showed no significant difference (OR 1.6, 95% CrI 0.41-6.3). FANS reduced postoperative fever and complications. FANS also showed lower odds of postoperative sepsis (OR 0.40, 95% CrI 0.12-0.97). CONCLUSIONS: Suction-assisted ureteroscopy improves SFR for renal calculi ≤ 2 cm. FANS was associated with shorter operative time, fever, and complications. DISS systems show promising but limited results, with performance differing by technology configuration. Larger prospective trials are needed.

Humans↗
PubMed2026

Comparative effectiveness of game-based learning modalities in nursing and medical education: a systematic review and Bayesian network meta-analysis.

BACKGROUND: Game-based learning (GBL) is increasingly used in healthcare education, but educators must choose among diverse modalities (e.g., quiz platforms, apps, serious games and metaverse environments). Comparative evidence on which modalities perform best across learning domains (knowledge, attitudes, and practice) remains limited. AIM: To compare the effects of distinct GBL modalities on knowledge, attitudes, and practice outcomes in nursing and medical education and to explore whether comparative effects differ by learner group (pre-licensure students and in-service professionals). DESIGN: PRISMA-NMA-aligned systematic review and Bayesian network meta-analysis. METHODS: We searched eight databases and trial registries through September 2, 2024, for randomized controlled trials comparing GBL with traditional teaching (TT). Outcomes were transformed to a 0-100 scale and analysed as change from baseline in Bayesian consistency models; random-effects models were selected using deviance information criterion (DIC). Risk of bias was assessed using RoB 2. We report mean differences (MDs) with 95% credible intervals (CrIs) versus TT, ranking probabilities, and subgroup NMAs by learner group. RESULTS: Thirty-one RCTs (n = 3439) were included; 15 contributed complete data to the network. Risk of bias was low in 15 trials and raised some concerns in 16. The network was modest for knowledge (11 trials) and sparse for attitudes (3) and practice (4). Compared with TT, metaverse-based learning showed improved attitudes (MD 15; 95% CrI 12 to 18), based on a single trial. For knowledge and practice, Kahoot-based quizzes (MD 9.1; 95% CrI -8.9 to 27) and app-based learning (MD 4.6; 95% CrI -4.4 to 14) had the highest estimated mean improvements, but credible intervals were wide and included the null for most comparisons. Subgroup rankings differed by learner group, but several comparisons were imprecise and uncertainty was substantial, particularly in sparse networks. CONCLUSIONS: GBL modalities may improve learning outcomes compared with TT, but relative effects appear domain-specific and the certainty of rankings is limited by sparse evidence and imprecision. Future trials should prioritise head-to-head comparisons, robust outcome measurement, and longer-term retention and transfer outcomes in both student and in-service populations.

Humans↗
PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗
PubMed2026

Comparative Efficacy of Non-opioid Analgesic Drugs for Chronic Cancer Pain: A Bayesian Network Meta-analysis.

PURPOSE: While opioids remain the primary pharmacological intervention for cancer pain management, their clinical utility is frequently compromised by dose-limiting toxicities. This study aimed to determine the comparative efficacy, opioid-sparing potential, and clinical hierarchy of non-opioid adjuvant drug classes. The study was structured around the PICO framework to evaluate the pharmacological strategies currently utilized in multimodal clinical oncology. METHODS: A systematic search of electronic databases (PubMed, Embase, Cochrane) was conducted for randomized controlled trials (RCTs) published between 2000 and 2025. The primary outcome was global analgesic efficacy (standardized mean difference [SMD]), while secondary outcomes included the opioid-sparing effect, defined as the percentage reduction in morphine equivalent daily dose (MEDD) and the incidence of treatment-emergent adverse events (Harms). A Bayesian network meta-analysis (NMA) was performed to rank treatments using SUCRA values. The methodological quality was assessed using the Cochrane Risk of Bias (RoB 2.0) tool. RESULTS: Twenty-three RCTs (n = 1845) met the inclusion criteria. Nonsteroidal anti-inflammatory drugs (NSAIDs) (-1.10) and anticonvulsants (-1.06) demonstrated the most robust analgesic effects. The SUCRA ranking confirmed a clear hierarchy, with the combination of anticonvulsants and antidepressants showing the highest probability of efficacy. A significant opioid-sparing effect was observed for gabapentinoids and ketamine, facilitating MEDD reduction. While serious adverse events were rare, minor harms (somnolence, dizziness) were more frequent in the most effective classes. CONCLUSION: Our NMA provides a robust evidence base for a "Clinical Tier" system, ranking adjuvants by their balance of efficacy and safety. These findings support the early integration of Tier I agents (anticonvulsants and NSAIDs) to optimize pain control and reduce opioid-related toxicities in chronic cancer pain management.

Humans↗
PubMed2025

Comparative Efficacy of Janus Kinase Inhibitors Indicated for Severe Alopecia Areata: A Bayesian Network Meta-Analysis and Matching-Adjusted Indirect Comparison.

Systemic Janus kinase inhibitors (JAKIs) have markedly advanced the therapeutic landscape for alopecia areata (AA). Although baricitinib and ritlecitinib are approved in the United States (US) and Europe, and deuruxolitinib in the US for severe AA, the lack of head-to-head randomized controlled trials (RCTs) limits evidence-based prescribing decisions. Moreover, prior meta-analyses excluded data on certain oral JAKIs or incorporated findings from agents and dosing regimens that were abandoned, investigational, clinically ineffective, or associated with unacceptable safety profiles. To compare the efficacy of oral JAKIs, limited to FDA, EMA, or MHRA approved drugs and doses-baricitinib (2 and 4 mg QD), ritlecitinib (50 mg QD), and deuruxolitinib (8 mg BID)-for severe AA, using advanced indirect comparison methodologies. A systematic review was performed following PRISMA 2020 guidelines (CRD420251116775). Bayesian network meta-analysis (NMA) synthesized data from RCTs reporting Week 24 outcomes on Severity of Alopecia Tool (SALT) ≤ 10 and SALT ≤ 20 thresholds. Multilevel network meta-regression (ML-NMR) evaluated heterogeneity and adjusted for baseline imbalances. Additionally, unanchored matching-adjusted indirect comparisons (MAIC) were conducted using individual patient-level data from THRIVE trials. Surface under the cumulative ranking (SUCRA) values were calculated to rank treatments. Seven RCTs (n = 4560 participants) were included. Deuruxolitinib 8 mg significantly outperformed baricitinib 2 and 4 mg on both SALT endpoints. Differences with ritlecitinib 50 mg were directionally favorable for deuruxolitinib but not statistically significant in NMA and ML-NMR models. MAICs confirmed superior odds for deuruxolitinib versus baricitinib 2 mg (OR = 71.55) and ritlecitinib (OR = 18.27) for SALT ≤ 20. SUCRA rankings also consistently favored deuruxolitinib. Among approved oral JAKIs, deuruxolitinib 8 mg shows the highest short-term efficacy for severe AA. These findings provide preliminary evidence to guide treatment decisions but should be interpreted as exploratory pending confirmation.

Humans↗
PubMed1992

Bayesian belief networks in quantitative histopathology.

Bayesian belief networks have a dynamic range and numeric response characteristics that make them uniquely suitable for descriptive classification schemes. Features showing considerable overlap of tolerance regions may be used, in a cumulative manner, to derive unequivocal classification decisions. The numeric response characteristics of Bayesian belief networks are analyzed, and their application as control modules in automated scene segmentation in histopathology is demonstrated.

Bayes Theorem↗
PubMed1991

Algorithms for Bayesian belief-network precomputation.

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference. We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

Algorithms↗
PubMed2025

Identification of a PRDM1-regulated T cell network to regulate atherosclerotic plaque inflammation.

BACKGROUND: Inflammation is a key driver of atherosclerosis, yet the mechanisms sustaining inflammation in human plaques remain poorly understood. This study uses a network-based approach to identify immune gene programs involved in the transition from low- to high-risk (rupture-prone) human atherosclerotic plaques. METHODS: Expression data from human carotid artery plaques, both stable (low-risk, n = 16) and unstable (high-risk, n = 27), were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA). Bayesian network inference, operated on the eigengene values from the WGCNA, further extended the WGCNA analysis, and similarity to the signature of T cell subsets was validated in single-cell RNA sequencing data of human plaques, and a loss-of-function study in a mouse model of atherosclerosis. In silico drug repurposing was performed to identify potential therapeutic targets. RESULTS: Our analysis revealed a distinct gene module with a prominent T cell signature, particularly in unstable plaques. Key regulatory factors, RUNX3, IRF7 and in particular PRDM1, were significantly downregulated in plaque T cells from symptomatic versus asymptomatic patients, indicating a protective role. Additionally, as PRDM1 is downstream of IRF7, we opted for PRDM1 as a key target. T cell-specific Prdm1 deficiency in Western-type diet fed Ldlr knockout mice featured accelerated plaque progression. Finally, as PRDM1 targeting drugs are not yet available, we performed in silico drug repurposing, identifying EGFR inhibitors as promising therapeutic candidates. CONCLUSIONS: This study highlights a PRDM1-regulated T cell network that distinguishes high-risk from low-risk plaques and demonstrates the regulatory role of T cell PRDM1 in controlling atherosclerosis, positioning this pathway as a promising therapeutic target.

Plaque, Atherosclerotic↗
PubMed2026

A systematic review and network meta-analysis of single nucleotide polymorphisms associated with oral submucous fibrosis risk.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic and insidious oral disease characterized by hyalinization of the subepithelial connective tissue and progressive fibrosis of the oral submucosa. It is a precancerous condition of oral squamous cell carcinoma. Studies have demonstrated that single nucleotide polymorphisms (SNPs) are closely associated with susceptibility to OSF. This study aims to comprehensively evaluate the association between SNPs and OSF risk and to rank the strength of the association between different genetic models and OSF susceptibility. METHODS: Literature related to OSF was comprehensively searched from PubMed, Web of Science, Embase, Cochrane Library, CNKI, and Wangfang databases up to July 2025. Full-text case-control studies with patients diagnosed with OSF were included. Quality assessment was performed to evaluate the risk of bias. RevMan 5.4, GeMTC 0.14.3, and STATA 17.0 were used for the pairwise and Bayesian network meta-analysis. RESULTS: A total of 24 studies with 2545 cases and 3772 controls, covering 13 SNPs in 11 genes, were included in our meta-analysis. We found that CYP1A1 rs4646903:T>C, CYP1A1 rs1048943:A>G, GSTT1 null genotype, GSTM1 null genotype, and XRCC3 rs861539:C>T were associated with an increased risk of OSF, while MMP2 rs243865:C>T and MMP3 rs3025058: 5A>6A were associated with a decreased risk of OSF. Further Bayesian network meta-analysis indicated the top 5 genetic models with the highest association with OSF risk in network group 1 were the dominant model, homozygous model, allelic model, and recessive model of CYP1A1 rs1048943:A>G (ranked 1-4), and the heterozygous/dominant model of CYP1A1 rs4646903:T>C (both ranked 5). While the allelic models of XRCC3 rs861539:C>T and MMP3 rs3025058: 5A>6A ranked first for predicting OSF in group 2 and group 3, respectively. CONCLUSION: Some specific SNPs are significantly related to the risk of OSF. Among them, the dominant model of CYP1A1 rs1048943:A>G may be the most strongly associated genetic model with OSF risk. Future large-sample, well-designed studies with detailed genotype data are needed to validate the roles of these SNPs in OSF risk.

Humans↗
PubMed2026

Causal assessment of Bayesian gene regulatory networks from single-cell transcriptomics.

Gene regulatory network (GRN) inference is an essential tool for revealing dysregulated relationships between genes in different cell types from single-cell transcriptomic (SCT) data. GRNs based on Bayesian networks (BNs) learned from SCT data can elucidate directed regulatory relationships representing complex disease mechanisms and their interplay through graphical modeling. However, software for learning BNs from SCT data is not widely available, nor is software for evaluating the BNs' structural accuracy in representing causal relationships between genes. Here, we describe the scstruc R package. This package provides a suite of BN structure learning algorithms specifically designed to handle SCT data, to evaluate the resulting networks based on the causal relationships they represent regardless of the availability of established molecular interaction networks, and to compare regulatory relationships between conditions. We demonstrated that scstruc can identify biologically relevant differential regulatory relationships between groups on a per-cell basis.

Bayesian networks↗
PubMed2026

Molecular targeted therapy in combination with chemotherapy for the treatment of platinum-resistant/refractory ovarian cancer (PROC): a systematic review and network meta-analysis.

BACKGROUND: Although single-agent chemotherapy is the most common approach for treating platinum-resistant or refractory ovarian cancer (PROC), there is growing evidence that combining molecular targeted agents with chemotherapy is beneficial, especially for certain patient groups. However, the most effective combination regimen remains elusive. OBJECTIVES: This Bayesian network meta-analysis (NMA) aims to identify the best combination therapy for PROC. METHODS: Relevant studies were searched in PubMed, EMBASE, Web of Science and the Cochrane Central Register of Controlled Trials from their inception until October 2024. The primary outcomes were overall survival (OS), progression-free survival (PFS) and adverse events (AEs). Statistical analyses were performed using the GEMTC package (1.0-2) and R 4.2.0. This review was registered in PROSPERO (CRD42023428414). RESULTS: Our analysis of 22 randomized controlled trials (RCTs) (n = 3408) demonstrated that chemotherapy combinations with bevacizumab (hazard ratio (HR) = 0.52-0.65), sorafenib (HR = 0.65, 95% confidence interval (CI): 0.45-0.93) or adavosertib (HR = 0.56, 95%CI: 0.35-0.90) significantly improved OS and PFS versus chemotherapy alone. Notably, adavosertib + gemcitabine was associated with an increased risk of grade 3-4 AEs (relative risk (RR) = 1.8, 95%CI: 1.3-2.7), but these were generally manageable. CONCLUSIONS: Bevacizumab-based combinations demonstrate consistent benefits across multiple regimens for PROC. Paclitaxel + bevacizumab emerges as the optimal balance of efficacy and safety. Topotecan + sorafenib could be an alternative for patients who are ineligible for anti-angiogenic therapy.

Humans↗
PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗
PubMed2026

ReGAIN: a bioinformatics platform for assessing probabilistic co-occurrence between resistance genes in bacterial pathogens.

MOTIVATION: Multidrug-resistant bacterial pathogens continue to rise globally, yet scalable methods are needed to infer how resistance determinants co-occur across pathogen populations and to quantify conditional dependencies underlying co-occurrence and shared genetic context. RESULTS: We present ReGAIN (Resistance Gene Association and Inference Network), an open-source platform that applies Bayesian network structure learning to infer probabilistic, conditional dependency relationships among antibiotic resistance, heavy metal tolerance, stress response, and virulence determinants in bacteria. In contrast to pairwise co-occurrence analyses, ReGAIN reports conditional probabilities, relative risks, and absolute risk differences with confidence intervals to prioritize candidate relationships for downstream prioritization. Applied across ESKAPEE pathogens, ReGAIN recapitulated established resistance gene relationships and identified additional candidate patterns consistent with co-selection and shared genetic context. Together, these results support scalable, reproducible population-wide analysis of resistance networks for surveillance, comparative genomics and epidemiology. AVAILABILITY: ReGAIN analyses are performed using Python v3.11.5 and R v4.4.1 and is available as open-source software through Bioconda at {https://anaconda.org/bioconda/regain-cli}. Source code and documentation can be found at {https://github.com/ERBringHorvath/regain_CLI}. All genomes used in this publication were downloaded from the National Center for Biotechnology Information database. Large supplementary tables and results data from the ESKAPEE pathogen example network analyses can be downloaded from https://figshare.com/articles/dataset/ReGAIN_command_line_software_and_supplemental_figures_/28959431.

Computational Biology↗
PubMed2026

Optimal dose and exercise modality to improve HbA1c in older adults with type 2 diabetes mellitus: a systematic review with pairwise, network, and dose-response meta-analyses.

We aimed to compare exercise modalities and evaluate dose-response relationships with glycemic control including continuous aerobic exercise (CAE), resistance training (RT), combined exercise (CE), mind-body exercise (MBE), and high-intensity interval training (HIIT) in older adults with type 2 diabetes mellitus (T2DM). Three databases were searched for randomized controlled trials of exercise interventions in older adults with T2DM reporting glycated hemoglobin (HbA1c). Pairwise, Bayesian network, and dose-response meta-analyses were conducted. Compared with control, HIIT demonstrated the largest estimated reduction (MD = -0.95%; 95% CrI -1.45, -0.49), followed by CE (MD = -0.59%; 95% CrI -0.93, -0.25), CAE (MD = -0.46%; 95% CrI -0.69, -0.24), MBE (MD = -0.42%; 95% CrI -0.76, -0.10), and RT (MD = -0.29%; 95% CrI -0.51, -0.08). Dose-response network meta-analyses suggested a non-linear association between overall exercise dose and HbA1c reduction, with maximal estimated benefits at approximately 704 METs-min/week with the 95% CrI excluding zero between 241 and 920 METs-min/week. HIIT demonstrated the steepest estimated dose-response relationship, but with wider credible intervals. Other exercise modalities showed more gradual dose-response patterns across their estimated effective ranges. Our findings suggest that exercise prescription for older adults with T2DM should be individualized according to exercise modality, dose, and health status.

Humans↗
PubMed1992

Quality control in nerve conduction studies with coupled knowledge-based system approach.

Contemporary equipment used for nerve conduction studies is usually capable of computerized measurement of latency, amplitude, duration, and area of nerve and muscle action potentials and resulting conduction velocities. Abnormalities can be due to technical error or disease. Identification of technical error is a major element of quality control in electromyography, and artificial intelligence could be useful for this purpose. We have developed a coupled knowledge-based prototype system (QUALICON) to assess the correctness of recording and stimulating characteristics in routine conduction studies. QUALICON extracts numeric features from CMAPs or SNAPs, which are translated into symbolic form to drive a Bayesian network. The network uses high-level knowledge to infer the quality of stimulating and recording electrode placement as well as polarity and stimulus strength making recommendations as to the likely technical error when abnormal potentials are detected. A preliminary assessment shows that QUALICON performs as well as manual assessment performed by professionals.

Action Potentials↗
PubMed2026

NExON-Bayes: a Bayesian approach to network estimation informed by ordinal covariates.

MOTIVATION: In heterogeneous disease settings, accounting for intrinsic sample variability is crucial for obtaining reliable and interpretable omic network estimates. However, most graphical model analyses of biomedical data assume homogeneous conditional dependence structures, potentially leading to misleading conclusions. To address this, we propose a joint Gaussian graphical model that leverages sample-level ordinal covariates (e.g. disease stage) to account for heterogeneity and improve the estimation of partial correlation structures. RESULTS: Our modelling framework, called NExON-Bayes, extends the graphical spike-and-slab framework to account for ordinal covariates, jointly estimating their relevance to the graph structure and leveraging them to improve the accuracy of network estimation. To scale to high-dimensional omic settings, we develop an efficient variational inference algorithm tailored to our model. Through simulations, we demonstrate that our method outperforms the vanilla graphical spike-and-slab (with no covariate information), as well as other state-of-the-art network approaches which exploit covariate information. Applying our method to reverse phase protein array data from patients diagnosed with stage I, II or III breast carcinoma, we estimate the behaviour of proteomic networks as cancer progresses. Our model provides insights not only through inspection of the estimated proteomic networks, but also of the estimated ordinal covariate dependencies of key groups of proteins within those networks, offering a comprehensive understanding of how biological pathways shift across disease stages. AVAILABILITY AND IMPLEMENTATION: A user-friendly R package for NExON-Bayes with tutorials is available on Github at github.com/jf687/NExON, and archived at https://doi.org/10.5281/zenodo.20312938. The source of the dataset used is cited in the relevant section.

Bayes Theorem↗
PubMed1990

Artificial intelligence-guided analysis of cytologic data.

A design for the integration of artificial intelligence (AI) technology with large databases of clinical and objective cytologic data, such as are on file at the University of Chicago, is presented. Among the key features of this approach are the use of a knowledge representation structure based upon an associative network, the use of a Bayesian belief network as a method of managing uncertainty in the system, and the use of neural networks and unsupervised learning algorithms as a means of discovering patterns within this database. Such an automated approach is necessary, given the complexity and interdependence of these data, to gain an understanding of their dependence structure and to assist in their exploration and analysis.

Algorithms↗