ArticleWorld journal of urology2014
Extracting data from electronic medical records: validation of a natural language processing program to assess prostate biopsy results.
Article in World journal of urology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.Yearbook of medical informatics · 2015Pooled it
- Automated Extraction of Imaging and Pathology Data From Diverse Prostate Cancer Electronic Records.JCO clinical cancer informatics · 2025Article
- Assessing the proficiency of large language models on funduscopic disease knowledge.International journal of ophthalmology · 2025Article
- Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment.Nature reviews. Urology · 2024Review
- An overview and a roadmap for artificial intelligence in hematology and oncology.Journal of cancer research and clinical oncology · 2023Review
- Article
- 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
- Pandemic tele-smart: a contactless tele-health system for efficient monitoring of remotely located COVID-19 quarantine wards in India using near-field communication and natural language processing system.Medical & biological engineering & computing · 2022Article
- Automatic Classification of Cancer Pathology Reports: A Systematic Review.Journal of pathology informatics · 2022Article
- A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging.BMC medical informatics and decision making · 2021Article
- A Natural Language Processing-Assisted Extraction System for Gleason Scores: Development and Usability Study.JMIR cancer · 2021Article
- Using Systematized Nomenclature of Medicine clinical term codes to assign histological findings for prostate biopsies in the Gauteng province, South Africa: Lessons learnt.African journal of laboratory medicine · 2020Article
- Performance of a Natural Language Processing Method to Extract Stone Composition From the Electronic Health Record.Urology · 2019Article
- Article
- Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research.JCO clinical cancer informatics · 2019Article
- Creating a Synthetic Clinical Trial: Comparative Effectiveness Analyses Using an Electronic Medical Record.JCO clinical cancer informatics · 2019Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Automatic mining of symptom severity from psychiatric evaluation notes.International journal of methods in psychiatric research · 2018Article
- Racial and Ethnic Variation in Health-Related Quality of Life Scores Prior to Prostate Cancer Treatment.Sexual medicine · 2017Article
- Dental Treatment in a State-Funded Primary Dental Care Facility: Contextual and Individual Predictors of Treatment Need?PloS one · 2017Article
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe extraction of specific data from electronic medical records (EMR) remains tedious and is often performed manually. Natural language processing (NLP) programs have been developed to identify and extract information within clinical narrative text. We performed a study to assess the validity of an NLP program to accurately identify patients with prostate cancer and to retrieve pertinent pathologic information from their EMR. MATERIALS AND
methodsA retrospective review was performed of a prospectively collected database including patients from the Southern California Kaiser Permanente Medical Region that underwent prostate biopsies during a 2-week period. A NLP program was used to identify patients with prostate biopsies that were positive for prostatic adenocarcinoma from all pathology reports within this period. The application then processed 100 consecutive patients with prostate adenocarcinoma to extract 10 variables from their pathology reports. The extraction and retrieval of information by NLP was then compared to a blinded manual review.
resultsA consecutive series of 18,453 pathology reports were evaluated. NLP correctly detected 117 out of 118 patients (99.1%) with prostatic adenocarcinoma after TRUS-guided prostate biopsy. NLP had a positive predictive value of 99.1% with a 99.1% sensitivity and a 99.9% specificity to correctly identify patients with prostatic adenocarcinoma after biopsy. The overall ability of the NLP application to accurately extract variables from the pathology reports was 97.6%.
conclusionsNatural language processing is a reliable and accurate method to identify select patients and to extract relevant data from an existing EMR in order to establish a prospective clinical database.
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.