Evidence map›Paper›PMID 31014980›Full record

ArticleJournal of biomedical informatics2019

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.

Jean Coquet, Selen Bozkurt, Kathleen M Kan, Michelle K Ferrari, Douglas W Blayney, James D Brooks, Tina Hernandez-Boussard

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
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  11. Leveraging Digital Data to Inform and Improve Quality Cancer Care.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jean CoquetDepartment of Medicine, Stanford University, Stanford, CA, USA.
Selen BozkurtDepartment of Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, USA.
Kathleen M KanDepartment of Urology, Stanford University School of Medicine, Stanford, USA.
Michelle K FerrariDepartment of Urology, Stanford University School of Medicine, Stanford, USA.
Douglas W BlayneyDepartment of Medicine, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, USA.
James D BrooksDepartment of Urology, Stanford University School of Medicine, Stanford, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, USA.
Tina Hernandez-BoussardDepartment of Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, USA; Department of Surgery, Stanford University School of Medicine, Stanford, USA. Electronic address: boussard@stanford.edu.

Funding

Utilizing Electronic Health Records to Measure and Improve Prostate Cancer CareR01CA183962 · NCI · STANFORD UNIVERSITY · PI HERNANDEZ-BOUSSARD, TINA · 2015 to 2019
$3.1M
NCI NIH HHS R01 CA183962
6 · The paper itself

Abstract

objectiveClinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches. MATERIALS AND

methodsOur cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings.

resultsA total of 5500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question.

conclusionUsing structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.

Indexed as

Natural Language ProcessingPhenotypeBone NeoplasmsElectronic Health RecordsGuideline AdherenceHumansMaleProstatic NeoplasmsRisk FactorsElectronic health recordsMachine learningNatural language processingProstate cancer

Identifiers

PMID31014980
PMCPMC6584041

What OpenQuestion holds

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Registered trials

None linked

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.