ArticleBMC medical informatics and decision making2019
EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 22 citations in OpenAlex.
- Temporal Trends in Clinical Evidence of 5-Year Survival Within Electronic Health Records Among Patients With Early-Stage Colon Cancer Managed With Laparoscopy-Assisted Colectomy vs Open Colectomy.JAMA network open · 2022Trial
- Estimate renal cell carcinoma recurrence rates using electronic health records.ESMO real world data and digital oncology · 2026Article
- Risk of Incident Heart Failure and Heart Failure Subtypes in Patients With Rheumatoid Arthritis.Arthritis care & research · 2025Article
- Increasing the Impact and Value of Laboratory Medicine Through Effective and AI-Assisted Communication.EJIFCC · 2025Review
- A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records.BMC medical informatics and decision making · 2025Article
- A pragmatic methodology to extract anesthetic and physiological data from the electronic health record.Paediatric anaesthesia · 2024Article
- Extracting laboratory test information from paper-based reports.BMC medical informatics and decision making · 2023Article
- Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.Journal of medical Internet research · 2023Article
- Effectiveness of Medication Reconciliation in a Chinese Hospital: A Pilot Randomized Controlled Trial.Journal of multidisciplinary healthcare · 2023Article
- One Clinician Is All You Need-Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development.JMIR medical informatics · 2022Article
- Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation.IEEE open journal of engineering in medicine and biology · 2022Article
- Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.JAMA network open · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this data is often manually extracted for use in research studies. However, due to the large volume of notes in datasets, manually extracting numerical data often becomes infeasible. The objective of this study is to develop and validate a natural language processing (NLP) tool that can efficiently extract numerical clinical data from narrative notes.
resultsTo validate the accuracy of the tool EXTraction of EMR Numerical Data (EXTEND), we developed a reference standard by manually extracting vital signs from 285 notes, EF values from 300 notes, glycated hemoglobin (HbA1C), and serum creatinine from 890 notes. For each parameter of interest, we calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F
conclusionsEXTEND is an efficient, flexible tool that uses knowledge-based rules to extract clinical numerical parameters with high accuracy. By increasing dictionary terms and developing new rules, the usage of EXTEND can easily be expanded to extract additional numerical data important in clinical outcomes 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.