ReviewJMIR medical informatics2022
Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal.
Review in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic health records: managerial insights from an umbrella review.BMC health services research · 2026Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Optimizing In-Hospital Mortality Prediction After Cardiac Surgery: A Machine Learning Approach Using Feature Engineering for Imbalanced Data.Diagnostics (Basel, Switzerland) · 2026Article
- Mortality Prediction Among People Living With HIV on Antiretroviral Therapy in Public Health Facilities in Gondar City Administration, Northwest Ethiopia: Machine Learning-Based Study.JMIR medical informatics · 2026Article
- Survival Prediction for Postoperative Patients With Kidney Cancer Based on Computed Tomography Radiomics: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models.ESMO real world data and digital oncology · 2025Article
- Predictive modeling for metastasis in oncology: current methods and future directions.Annals of medicine and surgery (2012) · 2025Review
- A machine learning approach to predicting inpatient mortality among pediatric acute gastroenteritis patients in Kenya.Learning health systems · 2025Article
- Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support.Frontiers in digital health · 2025Article
- Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System.Bioengineering (Basel, Switzerland) · 2024Article
- Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients.Journal of imaging · 2024Article
- Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning.Cancers · 2024Article
- Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.BMC palliative care · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Machine learning to predict mortality for aneurysmal subarachnoid hemorrhage (aSAH) using a large nationwide EHR database.PLOS digital health · 2023Article
- Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004).Annals of surgical oncology · 2023Article
- Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach.Scientific reports · 2023Article
- The Quality and Utility of Artificial Intelligence in Patient Care.Deutsches Arzteblatt international · 2023Review
- Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer.Scientific reports · 2023Article
- Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies.Current oncology reports · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the United States, national guidelines suggest that aggressive cancer care should be avoided in the final months of life. However, guideline compliance currently requires clinicians to make judgments based on their experience as to when a patient is nearing the end of their life. Machine learning (ML) algorithms may facilitate improved end-of-life care provision for patients with cancer by identifying patients at risk of short-term mortality.
objectiveThis study aims to summarize the evidence for applying ML in ≤1-year cancer mortality prediction to assist with the transition to end-of-life care for patients with cancer.
methodsWe searched MEDLINE, Embase, Scopus, Web of Science, and IEEE to identify relevant articles. We included studies describing ML algorithms predicting ≤1-year mortality in patients of oncology. We used the prediction model risk of bias assessment tool to assess the quality of the included studies.
resultsWe included 15 articles involving 110,058 patients in the final synthesis. Of the 15 studies, 12 (80%) had a high or unclear risk of bias. The model performance was good: the area under the receiver operating characteristic curve ranged from 0.72 to 0.92. We identified common issues leading to biased models, including using a single performance metric, incomplete reporting of or inappropriate modeling practice, and small sample size.
conclusionsWe found encouraging signs of ML performance in predicting short-term cancer mortality. Nevertheless, no included ML algorithms are suitable for clinical practice at the current stage because of the high risk of bias and uncertainty regarding real-world performance. Further research is needed to develop ML models using the modern standards of algorithm development and reporting.
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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.