| Title |
Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes
|
|---|---|
| Published in |
Journal of Digital Imaging, August 2017
|
| DOI | 10.1007/s10278-017-0013-3 |
| Pubmed ID | |
| Authors |
Hannu T. Huhdanpaa, W. Katherine Tan, Sean D. Rundell, Pradeep Suri, Falgun H. Chokshi, Bryan A. Comstock, Patrick J. Heagerty, Kathryn T. James, Andrew L. Avins, Srdjan S. Nedeljkovic, David R. Nerenz, David F. Kallmes, Patrick H. Luetmer, Karen J. Sherman, Nancy L. Organ, Brent Griffith, Curtis P. Langlotz, David Carrell, Saeed Hassanpour, Jeffrey G. Jarvik |
| Abstract |
Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using N = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52-0.82), specificity 404/408 = 0.99 (0.97-1.0), precision (positive predictive value) 35/39 = 0.90 (0.75-0.97), negative predictive value 404/419 = 0.96 (0.94-0.98), and F1-score 0.79 (0.43-1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 30% |
| Switzerland | 1 | 10% |
| Germany | 1 | 10% |
| Unknown | 5 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 80% |
| Practitioners (doctors, other healthcare professionals) | 2 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 80 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 16% |
| Researcher | 12 | 15% |
| Student > Postgraduate | 6 | 8% |
| Student > Ph. D. Student | 6 | 8% |
| Student > Bachelor | 6 | 8% |
| Other | 16 | 20% |
| Unknown | 21 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 21 | 26% |
| Computer Science | 10 | 13% |
| Nursing and Health Professions | 4 | 5% |
| Engineering | 3 | 4% |
| Business, Management and Accounting | 2 | 3% |
| Other | 13 | 16% |
| Unknown | 27 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 May 2019.
All research outputs
#6,681,019
of 24,823,556 outputs
Outputs from Journal of Digital Imaging
#264
of 1,122 outputs
Outputs of similar age
#98,019
of 322,676 outputs
Outputs of similar age from Journal of Digital Imaging
#11
of 27 outputs
Altmetric has tracked 24,823,556 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 322,676 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.