ArticleJournal of the National Cancer Institute2019
Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data.
Article in Journal of the National Cancer Institute, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
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- Interpretable machine learning approach for optimizing hospice care predictions using health assessment data.BMC medical informatics and decision making · 2025Article
- Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data.Diagnostics (Basel, Switzerland) · 2025Article
- Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data.BMC medical research methodology · 2025Article
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- A SEMIPARAMETRIC METHOD FOR RISK PREDICTION USING INTEGRATED ELECTRONIC HEALTH RECORD DATA.The annals of applied statistics · 2024Article
- Metastasis patterns and prognosis in patients with gastric cancer: a Surveillance, Epidemiology, and End Results-based analysis.Journal of gastrointestinal oncology · 2024Article
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- Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning.Journal of the American Statistical Association · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials.Seminars in radiation oncology · 2023Review
- Study of Patient and Physician Attitudes Toward Automated Prognostic Models for Patients With Metastatic Cancer.JCO clinical cancer informatics · 2023Article
- Review
- Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer.JCO oncology practice · 2023Article
- Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer.JCO clinical cancer informatics · 2022Article
- Deep Learning Approaches for Predicting Glaucoma Progression Using Electronic Health Records and Natural Language Processing.Ophthalmology science · 2022Article
- Uncovering interpretable potential confounders in electronic medical records.Nature communications · 2022Article
- Creation of clinical algorithms for decision-making in oncology: an example with dose prescription in radiation oncology.BMC medical informatics and decision making · 2021Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
backgroundOncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer.
methodsThe model was trained and tested using 12 588 patients treated for metastatic cancer in the Stanford Health Care system from 2008 to 2017. Data sources included provider note text, labs, vital signs, procedures, medication orders, and diagnosis codes. Patients were divided randomly into a training set used to fit the model coefficients and a test set used to evaluate model performance (80%/20% split). A regularized Cox model with 4126 predictor variables was used. A landmarking approach was used due to the multiple observations per patient, with t0 set to the time of metastatic cancer diagnosis. Performance was also evaluated using 399 palliative radiation courses in test set patients.
resultsThe C-index for overall survival was 0.786 in the test set (averaged across landmark times). For palliative radiation courses, the C-index was 0.745 (95% confidence interval [CI] = 0.715 to 0.775) compared with 0.635 (95% CI = 0.601 to 0.669) for a published model using performance status, primary tumor site, and treated site (two-sided P < .001). Our model's predictions were well-calibrated.
conclusionsThe model showed high predictive performance, which will need to be validated using external data. Because it is fully automated, the model can be used to examine providers' practice patterns and could be deployed in a decision support tool to help improve quality of care.
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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.