P.H. Bartels
University of Arizona
4 Papers
78 Citations
P.H. Bartels is an academic researcher from University of Arizona. The author has contributed to research in topics: Basal (phylogenetics) & Key (cryptography). The author has an hindex of 4, co-authored 4 publications.
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Papers
Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast.
TL;DR: Developing an expert system model for the diagnosis of fine needle aspiration cytology of the breast will have three important roles in breast cytodiagnosis: to aid the cytologist in making a more consistent and objective diagnosis, to provide a teaching tool on breast cytological diagnosis for the non-expert, and it is the first stage in the development of a system capable of automated diagnosis through the use of expert system machine vision.
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Improved diagnostic decision-making in pathology: do inference networks hold the key?
TL;DR: The ability of computer systems to reason about knowledge and to make decisions has been the subject of much debate over the last two decades and the role of this in pathology has been discussed previously.
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Immunohistochemical expression of pi class glutathione S-transferase in the basal cell layer of benign prostate tissue following chronic treatment with finasteride.
Rodolfo Montironi,Roberta Mazzucchelli,Roberto Pomante,Deborah Thompson,V Duval da Silva,Vaught L,P.H. Bartels +6 more
TL;DR: Following chronic treatment with finasteride the immunohistochemical expression of pi class glutathione S-transferase in the benign prostate ducts and acini is upregulated in relation to an expanded basal cell layer, which could indicate thatfinasteride acts as a GST-pi inducer.
Expert system support using a Bayesian belief network for the classification of endometrial hyperplasia.
M.L. Morrison,W.G. Mccluggage,G.J. Price,James Diamond,M.R.M. Sheeran,K.M. Mulholland,M.Y. Walsh,Rodolfo Montironi,P.H. Bartels,Deborah Thompson,Peter W. Hamilton +10 more
TL;DR: A decision support system (DSS) was developed for the classification of endometrial hyperplasias using a Bayesian belief network to distinguish proliferative endometrium, simple hyperplasia, complex hyperPlasia, atypicalhyperplasia and grade 1 endometrioid adenocarcinoma and there was excellent or moderate to good inter‐observer agreement.