Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Weighted Bayesian Network for Visual Tracking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Learning Bayesian Networks
Diagnosis of breast cancer using Bayesian networks: A case study
Computers in Biology and Medicine
On probabilistic inference by weighted model counting
Artificial Intelligence
Bayesian Network Decomposition for Modeling Breast Cancer Detection
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Toward Expert Knowledge Representation for Automatic Breast Cancer Detection
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Bayesian prediction of an epidemic curve
Journal of Biomedical Informatics
A Bayesian biosurveillance method that models unknown outbreak diseases
BioSurveillance'07 Proceedings of the 2nd NSF conference on Intelligence and security informatics: BioSurveillance
Proposing a Business Model in Healthcare Industry: E-Diagnosis
International Journal of Healthcare Information Systems and Informatics
MedRank: discovering influential medical treatments from literature by information network analysis
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
Defining and measuring completeness of electronic health records for secondary use
Journal of Biomedical Informatics
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In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction.