Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction

By Di Zhao, Chunhua Weng. 2011

139 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management, Oncology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Information Management

In Oncology

Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “Compared to logistic regression or Naive Bayes methods, as a probabilistic formalism, Bayesian networks offer a better capacity to integrate heterogeneous knowledge in a single representation, which is particularly important in computational phenotyping because it complements the increasing availabi”
Defining and measuring completeness of electronic health records for secondary use (2013, unknown): “Researchers may be interested in predicting, amongst other clinical phenomena, disease status and risk [36–38], readmission [39,40], or mortality [41,42].”
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (2017, methodology): “In particular, the patient data contained in EHR systems has been used for such tasks as medical concept extraction [7], [8], patient trajectory modeling [9], disease inference [10], [11], clinical decision support systems [12], and more (Table I).”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “Some of real-time examples include modeling diagnostic performance (195), preserving privacy of medical diagnosis in e-Health cloud (196), medical dataset classification (197), classification of lymph node metastasis in gastric cancer (198), pattern classification for breast cancer diagnosis (199), ”
Mining Electronic Health Records: A Survey (2017, methodology): “…Fung et al. 2008; Wells et al. 2008; Vinzamuri et al. 2014; Li et al. 2015; van der Heijden et al. 2014; Jackson et al. 2011; Paxton et al. 2013; Zhao et al. 2011; Maroco et al. 2011; Breault et al. 2002; Wang et al. 2015b; Sun et al. 2014] Time Aware Case-Control or Cohort [Wang et al. 2014b;…”