ArticleStudies in health technology and informatics2019
Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.
Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes.JCO clinical cancer informatics · 2025Article
- Comprehensive testing of large language models for extraction of structured data in pathology.Communications medicine · 2025Article
- Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish.JCO clinical cancer informatics · 2024Article
- Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence.Journal of clinical medicine · 2023Article
- Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology.BMC bioinformatics · 2023Article
- Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.JMIR medical informatics · 2022Review
- Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.JCO clinical cancer informatics · 2021Article
- Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classify free-text pathology reports for prostate cancer into T, N and M stage groups.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.