ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012
Survival prediction and treatment recommendation with Bayesian techniques in lung cancer.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Bayesian networks as prognostic models in oncology: a systematic review and recommendations for clinical practice.BMJ oncology · 2026Article
- HCLmNet: A unified hybrid continual learning strategy multimodal network for lung cancer survival prediction.PloS one · 2026Article
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- CD-Surv: a contrastive-based model for dynamic survival analysis.Health information science and systems · 2022Article
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- Hybrid bag of approaches to characterize selection criteria for cohort identification.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.Advances in experimental medicine and biology · 2016Review
- A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer.PloS one · 2015Article
- Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.Journal of the Royal Society, Interface · 2014Article
- Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.Bone marrow transplantation · 2014Review
- Predicting discharge mortality after acute ischemic stroke using balanced data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Article
- A review of approaches to identifying patient phenotype cohorts using electronic health records.Journal of the American Medical Informatics Association : JAMIAReview
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by the M(3) and K2 structure learning algorithms. For treatment recommendation, the Bayesian Network, whose structure was learned by the MC(3) algorithm, has marginally outperformed NB, based on producing concordant results with the recorded treatments in the dataset. We observed that in cases where the classifier recommendations were discordant with the recorded treatments, the 1-year-survival rate decreased by 15%. We also observed that discordance between the classifier and the dataset was more dominant in cases where the recorded treatment was non-curative or was not frequently encountered in the dataset.
Indexed as
Identifiers
23304358PMC3540451What OpenQuestion holds
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.