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Application of Bayesian network modeling to pathology informatics
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Expert system support using a Bayesian belief network for the classification of endometrial hyperplasia
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Dynamic Bayesian Network for Cervical Cancer Screening
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Bayesian networks in biomedicine and health-care
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Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers
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Increased cervical cancer risk associated with extended screening intervals after negative human papillomavirus test results: Bayesian risk estimates using the Pittsburgh Cervical Cancer Screening Model
inferredcites →https://doi.org/10.1016/j.jbi.2007.12.005openalex-snapshot
A Bayesian derived network of breast pathology co-occurrence
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Computerized diagnostic decision support system for the classification of preinvasive cervical squamous lesions
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Toward Normative Expert Systems: Part I The Pathfinder Project
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Negative Predictive Value and Significance of Benign-Appearing Endometrial Cells in Papanicolaou Tests
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Immunohistochemistry and HPV in situ Hybridization in Pathologic Distinction Between Endocervical and Endometrial Adenocarcinoma
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Accuracy of reading liquid based cytology slides using the ThinPrep Imager compared with conventional cytology: prospective study
inferredcites →https://doi.org/10.1136/jcp.47.4.329openalex-snapshot
Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast.
inferredcites →https://doi.org/10.1136/jcp.49.3.194openalex-snapshot
How to develop and use a Bayesian Belief Network.
inferredcites →https://doi.org/10.1200/jco.2009.27.15_suppl.e11523openalex-snapshot
Predictors of invasive breast cancer or DCIS recurrence in estrogen receptor positive (ER+) and estrogen receptor negative (ER-) ductal carcinoma in situ (DCIS) patients with and without associated invasive carcinoma (IC)
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How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling
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The Pittsburgh Cervical Cancer Screening Model: A Risk Assessment Tool
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Reusability of Bayesian Networks case studies: a survey
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A Multi-Granularity Information-Based Method for Learning High-Dimensional Bayesian Network Structures
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Machine and Deep Learning
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Opportunities and challenges of explainable artificial intelligence in medicine
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Risk-based cervical screening guidelines should utilize large diverse national database and specifically measure invasive cancer risk of screened patients
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Are CIN3 risk or CIN3+ risk measures reliable surrogates for invasive cervical cancer risk?
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Using Bayesian networks with Tabu-search algorithm to explore risk factors for hyperhomocysteinemia
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Demystifying machine learning: a primer for physicians
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Personalized Medicine and Cervical Screening: Development of Individualized Quantitative Risk Assessments for Cervical Adenocarcinoma and Adenocarcinoma in situ
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A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data
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PI Prob: A risk prediction and clinical guidance system for evaluating patients with recurrent infections
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Effective diagnosis of sepsis in critically ill children using probabilistic graphical model
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Location-Based Sentiment Analysis using Bayesian Networks on COVID-19 Twitter Data
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Optimization of anesthetic decision-making in ERAS using Bayesian network
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Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China
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Identifying Associations in Minimum Inhibitory Concentration Values of Escherichia coli Samples Obtained From Weaned Dairy Heifers in California Using Bayesian Network Analysis
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Individualized Bayesian Risk Assessment for Cervical Squamous Neoplasia
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Additional file 1 of A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data
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