ArticleJournal of biomedical informatics2011
Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.
Article in Journal of biomedical informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advancing Alzheimer's research: A review of big data promises.International journal of medical informatics · 2017Pooled it
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- Review
- Comprehensive review of Bayesian network applications in gastrointestinal cancers.World journal of clinical oncology · 2025Review
- Expert Judgment Supporting a Bayesian Network to Model the Survival of Pancreatic Cancer Patients.Cancers · 2025Article
- Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.Scientific reports · 2024Article
- In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records.JAMIA open · 2024Article
- Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.JCO clinical cancer informatics · 2024Article
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- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Application of artificial intelligence to pancreatic adenocarcinoma.Frontiers in oncology · 2022Review
- Predicting the risk of cancer in adults using supervised machine learning: a scoping review.BMJ open · 2021Article
- Weakly supervised temporal model for prediction of breast cancer distant recurrence.Scientific reports · 2021Article
- Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising.World journal of gastroenterology · 2021Review
- Clinical Data Prediction Model to Identify Patients With Early-Stage Pancreatic Cancer.JCO clinical cancer informatics · 2021Article
- Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks.Journal of biomedical informatics · 2021Article
- Constructing a knowledge-based heterogeneous information graph for medical health status classification.Health information science and systems · 2020Article
- Article
- Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations.PloS one · 2020Article
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.Database : the journal of biological databases and curation · 2020Article
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Authors and funding
2 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.