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Mendeley readers
Attention Score in Context
| Title |
Automated chart review utilizing natural language processing algorithm for asthma predictive index
|
|---|---|
| Published in |
BMC Pulmonary Medicine, February 2018
|
| DOI | 10.1186/s12890-018-0593-9 |
| Pubmed ID | |
| Authors |
Harsheen Kaur, Sunghwan Sohn, Chung-Il Wi, Euijung Ryu, Miguel A. Park, Kay Bachman, Hirohito Kita, Ivana Croghan, Jose A. Castro-Rodriguez, Gretchen A. Voge, Hongfang Liu, Young J. Juhn |
| Abstract |
Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria. This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma. Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6-6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8-10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively. NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 91 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 12% |
| Student > Ph. D. Student | 8 | 9% |
| Student > Bachelor | 7 | 8% |
| Student > Master | 7 | 8% |
| Student > Doctoral Student | 6 | 7% |
| Other | 19 | 21% |
| Unknown | 33 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 21 | 23% |
| Agricultural and Biological Sciences | 7 | 8% |
| Computer Science | 7 | 8% |
| Nursing and Health Professions | 4 | 4% |
| Psychology | 4 | 4% |
| Other | 10 | 11% |
| Unknown | 38 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 13 February 2018.
All research outputs
#20,465,050
of 23,023,224 outputs
Outputs from BMC Pulmonary Medicine
#1,608
of 1,950 outputs
Outputs of similar age
#383,520
of 446,078 outputs
Outputs of similar age from BMC Pulmonary Medicine
#48
of 52 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,950 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.