ArticleBMJ open2014
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.
Article in BMJ open, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04985253 (Correlation of Predictive Accuracy of PREDICT Version 2.2,), which is not on this map. Cited by 46 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Correlation of Predictive Accuracy of PREDICT Version 2.2, (PREDICT V2.2) on a Retrospective Cohort of Indian Women With Operable Breast Cancer (OBC)
Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it, 121 citations in OpenAlex.
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled 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
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- PSOA-LSTM: a hybrid attention-based LSTM model optimized by particle swarm optimization for accurate lung cancer incidence forecasting in China (1990-2021).Frontiers in medicine · 2025Article
- Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients.Applied clinical informatics · 2024Article
- Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.JCO clinical cancer informatics · 2024Article
- Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums.Cancers · 2024Article
- The importance of planning CT-based imaging features for machine learning-based prediction of pain response.Scientific reports · 2023Article
- Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach.JMIR formative research · 2023Article
- Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning.Biosensors · 2023Article
- Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models.Head & neck · 2023Article
- Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time.Scientific reports · 2023Article
- Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms.Journal of pathology informatics · 2023Article
- Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.Frontiers in oncology · 2023Article
- An ensemble learning with active sampling to predict the prognosis of postoperative non-small cell lung cancer patients.BMC medical informatics and decision making · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesUsing the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.
settingA regional cancer centre in Australia.
participantsDisease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. PRIMARY AND SECONDARY OUTCOME MEASURES: Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC).
resultsThe ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours.
conclusionsMachine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.
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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.