ArticleJCO clinical cancer informatics2021
Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 4 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Systematic Review of Interventions to Optimize Emergency Department Care of Patients with Cancer.The western journal of emergency medicine · 2026Pooled it
- Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Pooled it
- Drivers of Emergency Department Use Among Oncology Patients in the Era of Novel Cancer Therapeutics: A Systematic Review.The oncologist · 2023Pooled it
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Graph augmented transformers improve chemotherapy toxicity symptom extraction from clinical notes.Nature communications · 2026Article
- Acute Care Events During Systemic Cancer Treatment: Moving From Risk Prediction to Clinical Decision Support Using a Two-Model Approach.JCO oncology practice · 2026Article
- Cost-Benefit Analysis of Preventing Acute Care Use in Oncology Patients Following Systemic Therapy Using Medicare Claims Data: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Using the cancer aging and research group- breast cancer (CARG-BC) predictive model in older adults (OA) with early breast cancer: an external validation study.Breast cancer research and treatment · 2025Article
- Dissonance in the Sole Quality Measure for Outpatient Chemotherapy, OP-35.JCO clinical cancer informatics · 2025Article
- Acute Care Utilization Patterns During Chemotherapy and Predictive Model Development at a Rural Community Cancer Center.JCO clinical cancer informatics · 2025Article
- Secondary Neutropenias.Biomedicines · 2025Review
- Prediction and Feature Selection of Mastectomy-Related Post Traumatic Stress Disorder (PTSD) Using Machine Learning Among Breast Cancer Patients in Bangladesh.Cancer informatics · 2025Article
- Use of Patient-Reported Outcomes in Risk Prediction Model Development to Support Cancer Care Delivery: A Scoping Review.JCO clinical cancer informatics · 2024Article
- Digital Remote Monitoring Using an mHealth Solution for Survivors of Cancer: Protocol for a Pilot Observational Study.JMIR research protocols · 2024Observational
- Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.JCO clinical cancer informatics · 2024Article
- Ensuring fair, safe, and interpretable artificial intelligence-based prediction tools in a real-world oncological setting.Communications medicine · 2023Article
- A Bayesian approach to predictive uncertainty in chemotherapy patients at risk of acute care utilization.EBioMedicine · 2023Article
- Reducing Avoidable Emergency Visits and Hospitalizations With Patient Risk-Based Prescriptive Analytics: A Quality Improvement Project at an Oncology Care Model Practice.JCO oncology practice · 2023Article
- Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence.Journal of clinical medicine · 2023Article
- Natural Language Processing Methods to Identify Oncology Patients at High Risk for Acute Care with Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeAcute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data.
methodsChemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve.
resultsEight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients.
conclusionDense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.
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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.