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Mendeley readers
Attention Score in Context
| Title |
A richly interactive exploratory data analysis and visualization tool using electronic medical records
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, November 2015
|
| DOI | 10.1186/s12911-015-0218-7 |
| Pubmed ID | |
| Authors |
Chih-Wei Huang, Richard Lu, Usman Iqbal, Shen-Hsien Lin, Phung Anh (Alex) Nguyen, Hsuan-Chia Yang, Chun-Fu Wang, Jianping Li, Kwan-Liu Ma, Yu-Chuan (Jack) Li, Wen-Shan Jian |
| Abstract |
Electronic medical records (EMRs) contain vast amounts of data that is of great interest to physicians, clinical researchers, and medial policy makers. As the size, complexity, and accessibility of EMRs grow, the ability to extract meaningful information from them has become an increasingly important problem to solve. We develop a standardized data analysis process to support cohort study with a focus on a particular disease. We use an interactive divide-and-conquer approach to classify patients into relatively uniform within each group. It is a repetitive process enabling the user to divide the data into homogeneous subsets that can be visually examined, compared, and refined. The final visualization was driven by the transformed data, and user feedback direct to the corresponding operators which completed the repetitive process. The output results are shown in a Sankey diagram-style timeline, which is a particular kind of flow diagram for showing factors' states and transitions over time. This paper presented a visually rich, interactive web-based application, which could enable researchers to study any cohorts over time by using EMR data. The resulting visualizations help uncover hidden information in the data, compare differences between patient groups, determine critical factors that influence a particular disease, and help direct further analyses. We introduced and demonstrated this tool by using EMRs of 14,567 Chronic Kidney Disease (CKD) patients. We developed a visual mining system to support exploratory data analysis of multi-dimensional categorical EMR data. By using CKD as a model of disease, it was assembled by automated correlational analysis and human-curated visual evaluation. The visualization methods such as Sankey diagram can reveal useful knowledge about the particular disease cohort and the trajectories of the disease over time. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 17% |
| United States | 1 | 17% |
| Spain | 1 | 17% |
| India | 1 | 17% |
| Argentina | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 65 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 12% |
| Student > Master | 5 | 8% |
| Researcher | 5 | 8% |
| Student > Bachelor | 4 | 6% |
| Professor > Associate Professor | 4 | 6% |
| Other | 7 | 11% |
| Unknown | 32 | 49% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 14% |
| Medicine and Dentistry | 5 | 8% |
| Nursing and Health Professions | 2 | 3% |
| Agricultural and Biological Sciences | 2 | 3% |
| Arts and Humanities | 2 | 3% |
| Other | 11 | 17% |
| Unknown | 34 | 52% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 01 February 2022.
All research outputs
#3,830,095
of 23,025,074 outputs
Outputs from BMC Medical Informatics and Decision Making
#325
of 2,008 outputs
Outputs of similar age
#52,942
of 283,168 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 40 outputs
Altmetric has tracked 23,025,074 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,008 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 83% 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 283,168 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.