ArticleJCO clinical cancer informatics2020
Interactive Exploration of Longitudinal Cancer Patient Histories Extracted From Clinical Text.
Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- A survey of NLP methods for oncology in the past decade with a focus on cancer registry applications.Artificial intelligence review · 2025Article
- DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.medRxiv : the preprint server for health sciences · 2023Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium · 2022Article
- Informatics Tools for Cancer Research and Care: Bridging the Gap Between Innovation and Implementation.JCO clinical cancer informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeRetrospective cancer research requires identification of patients matching both categorical and temporal inclusion criteria, often on the basis of factors exclusively available in clinical notes. Although natural language processing approaches for inferring higher-level concepts have shown promise for bringing structure to clinical texts, interpreting results is often challenging, involving the need to move between abstracted representations and constituent text elements. Our goal was to build interactive visual tools to support the process of interpreting rich representations of histories of patients with cancer.
methodsQualitative inquiry into user tasks and goals, a structured data model, and an innovative natural language processing pipeline were used to guide design.
resultsThe resulting information visualization tool provides cohort- and patient-level views with linked interactions between components.
conclusionInteractive tools hold promise for facilitating the interpretation of patient summaries and identification of cohorts for retrospective research.
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.