ArticleJournal of the Royal Society, Interface2014
Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.
Article in Journal of the Royal Society, Interface, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 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
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care.Journal of clinical monitoring and computing · 2017Pooled it
- Do Multidisciplinary Team (MDT) processes influence survival in patients with colorectal cancer? A population-based experience.BMC cancer · 2015Pooled it
- Intelligent MDT treatment decision making for stage III NSCLC using dual level embedding and three level explanation.Scientific reports · 2026Article
- AI-MDT: an automatic and intelligent multidisciplinary team consultations platform for lung cancer diagnosis.Journal of cancer research and clinical oncology · 2026Article
- Performance of an Electronic Health Record-Based Automated Pulmonary Embolism Severity Index Score Calculator: Cohort Study in the Emergency Department.JMIR medical informatics · 2025Article
- Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19.Biomedicines · 2024Article
- Article
- Ontologies Applied in Clinical Decision Support System Rules: Systematic Review.JMIR medical informatics · 2023Review
- Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis.Frontiers in oncology · 2023Review
- Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.JMIR medical informatics · 2022Review
- Biodiagnostics in an era of global pandemics-From biosensing materials to data management.View (Beijing, China) · 2022Review
- Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk.Journal of personalized medicine · 2022Article
- Risk Prediction Using Bayesian Networks: An Immunotherapy Case Study in Patients With Metastatic Renal Cell Carcinoma.JCO clinical cancer informatics · 2021Article
- Article
- Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention.JMIR medical informatics · 2016Article
- TrhOnt: building an ontology to assist rehabilitation processes.Journal of biomedical semantics · 2016Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.
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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.