ArticleJCO clinical cancer informatics2022
Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer.
Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Real-world implementation of electronic patient-reported outcome measures (ePROMs) in routine oncology practice: a scoping review.ESMO real world data and digital oncology · 2026Review
- Review
- Machine Learning for Prediction of High-Risk Infections in Patients With Cancer.Cancer medicine · 2026Article
- Characterizing Survivors Living With Likely Incurable Cancer: A Closer Look at an Emerging Population.JCO oncology practice · 2026Review
- Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer.JCO clinical 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
- Review
- Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.Cancer medicine · 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
- An overview and a roadmap for artificial intelligence in hematology and oncology.Journal of cancer research and clinical oncology · 2023Review
- On the importance of interpretable machine learning predictions to inform clinical decision making in oncology.Frontiers in oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeMachine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer.
methodsWe trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice.
resultsAmong 12,350 patients, 5,870 (47.5%) completed PRO assessments. Compared with EHR- and PRO-only algorithms, the EHR + PRO model improved predictive performance in both tertiary oncology (EHR + PRO
conclusionRoutinely collected PROs contain added prognostic information not captured by an EHR-based ML mortality risk algorithm. Augmenting an EHR-based algorithm with PROs resulted in a more accurate and clinically relevant model, which can facilitate earlier and targeted supportive care for patients with cancer.
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What Socratic holds
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 Socratic graph.