ArticleJournal of the American Medical Informatics Association : JAMIA2021
Automated model versus treating physician for predicting survival time of patients with metastatic cancer.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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.
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Who cites it
25 citing papers in PubMed.
- Automated patient selection and care coaches to increase advance care planning for patients with cancer.Journal of the National Cancer Institute · 2025Trial
- Review
- "Black box" artificial intelligence for mortality prediction: a mixed-methods study of palliative care team, patient, and caregiver perspectives.Annals of palliative medicine · 2026Article
- Explainable machine learning model predicts response to adjuvant therapy after radical cystectomy in bladder cancer.Frontiers in oncology · 2025Article
- Article
- Developing a Research Center for Artificial Intelligence in Medicine.Mayo Clinic proceedings. Digital health · 2024Article
- Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.BMC palliative care · 2024Article
- Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.JCO clinical cancer informatics · 2024Article
- Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data.NPJ precision oncology · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.BMC medical research methodology · 2023Article
- Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials.Seminars in radiation oncology · 2023Review
- Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.JCO clinical cancer informatics · 2023Article
- Study of Patient and Physician Attitudes Toward Automated Prognostic Models for Patients With Metastatic Cancer.JCO clinical cancer informatics · 2023Article
- Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer.JCO oncology practice · 2023Article
- Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing.JAMA network open · 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
- Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2022Article
- Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure.Communications medicine · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveBeing able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model. MATERIALS AND
methodsA machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015-2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots.
resultsThe radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63-0.81), 0.77 (0.73-0.81), and 0.68 (0.65-0.71), respectively.
conclusionsThe machine learning model's predictions were more accurate than those of the treating physician or a traditional model.
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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.